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ADVANCINGENVIRONMENTAL ANALYSIS

SUPPLEMENT TO

October 2015

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ENVIRONMENTAL

ANALYSIS

ENVIRONMENTAL

ANALYSIS

ADVANCINGADVANCING

6 ADVANCING ENVIRONMENTAL ANALYSIS OCTOBER 2015

Articles

New Solutions to Environmental Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Kevin Schug

A brief introduction to the articles presented in this supplement

A Fast, Accurate, Speciation-Capable, Automated, and Green Gas-Phase Chemiluminescence Approach for Analyzing Waterborne Arsenic . . . . . . . . . . . . . . . . . . 10Arup K. Ghosh, Aditya N. Das, and Purnendu K. Dasgupta

This cost-effective approach has a limit of detection well below 1 µg As/L and a linear range that extends to >100 µg As/L.

Fourier Transform Molecular Rotational Resonance Spectroscopy: Bridging the Gap Between Spectroscopy and Chromatography for VOC analysis. . . . . . . . . . . . . . . . 18Brent H. Harris, Justin L. Neill, Robin L. Pulliam, and Matthew T. Muckle

This study demonstrates the strengths of FT-MRR for simple, direct analysis of VOCs and other toxic industrial chemicals.

The State of the Art of Flow-Through Solid-Phase Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Inês C. Santos, Raquel B.R. Mesquita, and António O.S.S. Rangel

Flow-through SPS lowers reagent and sample consumption and decreases waste generation.

The Benefits of Single-Particle ICP-MS to Better Understand the Fate and Behavior of Engineered Nanoparticles in Environmental Water Samples . . . . . . . . . . . . . . . . . . 32Chady Stephan and Robert Thomas

SP-ICP-MS demonstrates excellent potential for characterizing nanoparticles in varied types of environmental samples.

GC–MS and UHPLC–MS-MS Analysis of Organic Contaminants and Hormones in Whale Earwax Using Selective Pressurized Liquid Extraction . . . . . . . . . . . . . . . . 40Sascha Usenko, Zach C. Winfield, Stephen J. Trumble, and Nadine Lysiak

This work addresses two challenges: developing a technique capable of measuring ppb levels of hormones, and developing

an SPLE technique capable of extracting contaminants and hormones from a single sample without additional cleanup steps.

Analytical Efforts Toward Monitoring Groundwater in Regions of Unconventional Oil and Gas Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Kevin A. Schug, Doug D. Carlton Jr., and Zacariah L. Hildenbrand

A mix of analytical methods is required to understand the impact, if any, that UOG activity is having on groundwater.

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www.chromatographyonline.com8 AdvAncing EnvironmEntAl AnAlysis OCTOBER 2015

FROM the Guest eDItOR

Environmental science and analytical chemistry are intimately intertwined. New methods are continually

needed to address emerging challenges, which often comprise complicated matrices and ultratrace levels

of chemical constituents. Further, the breadth of chemicals of interest spans a wide range of metals, ions,

small organic compounds, complexes, and nanoparticles. Because of the diversity of this group of samples,

researchers need access to a wide range of analytical methods. While multitude standardized methods, such as

those curated by the U.S. Environmental Protection Agency (EPA), are available for guidance, some of these

are not compatible with the problem at hand. Further, the evolution of analytical instrumentation continues

to provide novel means to accelerate workflows. We should not be beholden to outdated methods; rather, we

should continually strive to push the boundaries, to make analyses cheaper, more portable, more reliable, more

sensitive, and more available.

In this special issue, we provide a snapshot of emerging applications and solutions in environmental analytical

chemistry. The compilation includes a wide range of instrumental techniques. The uses of sample preparation,

spectroscopy, liquid and gas chromatography (LC and GC), and mass spectrometry (MS) are shown to span a variety of operation modes,

formats, and applications. Further, we feature contributions from academic and industry groups in the United States and Europe.

On the spectroscopy side, a new instrument developed by Dasgupta and coworkers provides impressive performance and portability

for arsenic measurements in water. The primary mode of detection is through gas-phase chemiluminescence, and both laboratory-scale

and portable (<5 kg weight) instruments have been designed and constructed to be simple, but effective. Such a development provides

means for low-cost analysis of arsenic, which is a significant need worldwide. The maximum contamination limit set by the US EPA

for arsenic is 10 µg/L, and the new systems are capable of detecting an order of magnitude below this level, and more than two orders

above it. The analysis can be performed in less than 3 minutes.

An interesting new spectroscopy technique has been brought to light in an article prepared by Harris and colleagues. The benchtop system

used makes use of sub-terahertz (millimeter wavelength) radiation and Fourier transform molecular rotational resonance spectroscopy (FT-

MRR) for absolute structure-specific analyte measurement. This technique alleviates the need for lasers, chromatography, and chemometrics

to provide rapid measurement performance in the low picomole range and spanning upwards 3–5 orders of magnitude in dynamic range.

Rounding out our more purely spectroscopy techniques is a great article on the use of inductively coupled plasma–MS (ICP-MS) for

single-particle analysis in environmental water samples. Stephan and Thomas describe the detection and analysis of metal nanopar-

ticles at low concentrations, but also with the capability for measuring salient properties such as particle distribution and particle size. In

addition to providing an excellent overview of the technology and specific operation modes, they demonstrate the use of the technique

for evaluating the effectiveness for removal of TiO2 nanoparticles by wastewater treatment plants, among other water applications.

Rangel and coworkers describe the use of flow injection analysis (FIA) techniques and their potential for analyzing a variety of envi-

ronmental samples. FIA includes the lab-on-a-valve concept, which is highly modular and flexible in its design. Thus, concepts such

as solid-phase spectrometry, where analytes are trapped and detected spectroscopically on beads, can be incorporated into novel flow

schemes, which can be automated and are highly reproducible. Further, reagent and sample consumption are reduced. Overall, I think

that FIA is an overlooked analytical concept in mainstream laboratories. Fit-for-purpose setups can be robust, reliable, and efficient.

Moving from a focus on spectroscopy to sample preparation and chromatography–mass spectrometry, Usenko and coworkers

describe their unique work in oceanographic environmental analysis. The researchers are using whale ear wax as a core sample to study

changes in ocean pollution over the life of a whale. What an exceptional idea! Whales can live long lives and traverse many parts of

the planet, so interesting spatial and temporal coverage of organic contaminant levels is possible. However, to effectively sample the

matrix and analyze for multiple classes of compounds, special attention to sample preparation is needed. Selective pressurized liquid

extraction (SPLE) is described and demonstrated for hormone analysis when combined with ultrahigh-pressure liquid chromatography

(UHPLC)–MS-MS. SPLE involves the strategic combination of the sample with one or more sorbent materials. The sorbents provide

unique selectivity for targeting different compound classes and enable the preparation of samples for both LC and GC analysis.

Finally, my colleagues and I contribute an article from our own work at UT Arlington. For several years, we have been refining and

applying a variety of analytical methods to investigate the potential environmental impact of unconventional oil and gas extraction.

The article provides an overview of the GC- and ICP-based measurements we and others have made to study water quality. These

methods emphasize utility and reliability, but also good throughput for handling large numbers of samples. Thus, analytical results

can be accompanied with geospatial analysis to help understand potential sources for contaminants detected.

I want to thank the authors, who have dedicated their time and efforts to communicate their latest technologies, interests, and exper-

tise so that readers can be better versed in the state-of-the-art today. It is my hope that this collection will provide a broad overview

of emerging methods in chromatographic and spectroscopic analysis. Specifically, I hope that the articles will inspire readers in their

own work, and that the implementation and propagation of the strategies the articles describe can help provide more-comprehensive

assessments of analytical problems.

New Solutions to Environmental Challenges

Kevin A. Schug, The University of Texas at Arlington


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Arup K. Ghosh, Aditya N. Das, and Purnendu K. Dasgupta

A Fast, Accurate, Speciation-Capable, Automated, and Green Gas-Phase Chemiluminescence Approach for Analyzing Waterborne Arsenic

This article describes a cost-effective and sensitive approach for

quantifying waterborne arsenic based on gas-phase chemiluminescence.

The approach centers on the use of photometric instruments—one

configured for laboratory use and one field-deployable version—that

can quantify total arsenic as well as individually measure As(III) and

As(V). The regulatory limit for arsenic in drinking water is 10 μg/L. The

limits of detection of the gas-phase chemiluminescence instruments

are well below 1 μg As/L and the linear range extends to >100 μg

As/L. Total arsenic analysis using this approach requires 3 min.

Arsenic in drinking water is a

serious issue in many parts of

the world (1). Arsenic is a Class

A human carcinogen, and it can lead to

skin, bladder, lung, and prostate cancer

(2). It has also been linked with respiratory,

reproductive, developmental, immunolog-

ical, and neurological defects (2).

The maximum permissible level of arse-

nic in drinking water, according to the

World Health Organization as well as the

United States Environmental Protection

Agency (US EPA), is 10 μg/L. However,

such a standard is presently too stringent

to be met in many places in the world; in

India, Bangladesh, Taiwan, China, and

Vietnam, the current limit is 50 μg/L (3).

Arsenic is the 20th most abundant crustal

element; the presence of arsenic in ground-

water is common. The Trace Elements

National Synthesis Project of the United

States Geological Survey has created a

map of groundwater arsenic distribution

in the United States (4); high (50 μg/L)

levels of arsenic in groundwater exist in

many locations. A recent publication has

also reported increased occurrence of ele-

vated arsenic levels in groundwater after

local hydraulic fracturing activities in the

Barnett Shale area of Texas (5).

Present US EPA-approved detection

methods are all based on atomic spec-

trometry, and “approved” methods must

be used for regulatory reporting. For non-

regulatory testing, any method may be

used, but it is beneficial if the method or

instrument performance has been verified

by the US EPA Environmental Technol-

ogy Verification program. One stripping

voltammetric analyzer has undergone such

verification, but electrochemical analyzers

often suffer from electrode fouling, mak-

ing standard addition a necessity; even

traces of copper present can be a problem

(6). Other reports have indicated the high

degree of expertise needed to obtain reli-

able results with electrochemical analyzers

(7). On the other hand, atomic spectrom-

etry–based instruments are bulky, expen-

sive and require large amounts of pure

gas in addition to expensive consumables.

Moreover, such instruments cannot be

used in the field.

Field analysis is extremely important

considering the temporal changes in arse-

nic concentrations at various sampling

sites and the uncertainty that always sur-

rounds the integrity of a sample preserved

in the field. Currently commercially avail-

able field assays are based on the Gutzeit

method (8), which involves generation

of arsine (AsH3), filtering through lead

acetate paper to trap any hydrogen sul-

fide formed, and capturing the arsine


OCTOBER 2015 AdvAncing EnvironmEntAl AnAlysis 11www.chromatographyonline.com

on a mercuric bromide–soaked paper to

induce a yellow coloration. Arsine that

is produced can escape from the device,

thus posing a health hazard (9). The less

expensive instruments use comparison

with a color chart for stepwise quantifi-

cation; color degradation in sunlight and

operator judgment can be factors (10).

Undesirable aspects of methods involving

those instruments include the use of large

sample volumes, a corresponding amount

of acid, and lead and mercury compounds

that generate toxic waste.

A field-usable instrument must be robust

and easy to operate. Other important fac-

tors are reproducibility, capital and con-

sumable cost, the ability to differentiate

inorganic As(III) and As(V) (which differ

considerably in their ease of removal and

acute toxicity), and environmental friend-

liness. In the past, work in our laboratory

demonstrated the principles of a gas-phase

chemiluminescence–based arsenic ana-

lyzer (11). This article describes the use

of prototype laboratory-based and field-

deployable gas-phase chemiluminescence

analyzers developed in our laboratory to

perform simple, fast, environment-friendly,

Waste

Water

DV

SV2

SV3

R2

RC

R1

AsH3

O3

Air pump

Activated charcoal

Activated charcoal

OzoneGenerator

Air

Exit

CC

Waste

PMT

Amplifer

DisplaySV1

SV on

SV off

Citrate buffer

(1 mL)

6 M H2SO

4

(1 mL)

Syringe pump

4.8% (w/v) NaBH4

(0.5+0.5 mL)

H AB

C

DEF

G

Sample (2.5 mL)

Figure 1: Schematic of the arsenic analysis system: DV = eight-way distribution valve, RC = reaction chamber, SV1 = two-way solenoid valve, SV2 and SV3 = three-way so-lenoid valves, PMT = photomultiplier tube, CC = chemiluminescence chamber, R1 and R2 = fow restrictors, A–H = distribution valve ports. The amounts of liquid used in each sequential assay are indicated in the schematic: 1 mL of citrate buffer is added to the 2.5-mL sample, followed by 0.5 mL of sodium borohydride. After the frst signal is acquired, 0.5 mL of sulfuric acid is added, followed by a further 0.5 mL of sodium borohydride to acquire the second signal. To measure total arsenic, 1 mL of sulfuric acid is added to the 2.5 mL of the sample, followed by 0.5 mL of the sodium borohydride.

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and fully automated analysis of water-

borne arsenic. The instruments measure

total arsenic or As(III) (with As(V) by dif-

ference), or As(III) and As(V) sequentially.

Other configurations currently in develop-

ment in our laboratory include in-line con-

tinuous process instruments and remotely

deployed autonomous instruments.

Experimental

Experimental Reagents

Standard 1000-mg/L stock solutions of

As(III) and As(V) were prepared using

arsenic trioxide and sodium arsenate

heptahydrate, respectively. Working solu-

tions were prepared by serial dilution with

deionized water (18 MΩ cm-1) generally

immediately before use. For hydride gen-

eration, 4.8% (w/v) sodium borohydride

(NaBH4) (98%) was prepared in 0.5 M

sodium hydroxide and 1 mM disodium

ethylenediamine tetraacetate (Na2EDTA).

For sequential analysis of As(III) and

As(V), citrate buffer (pH 4.5) was used.

Citrate buffer (pH 4.5) was prepared by

adding sodium hydroxide pellets to 1 M

citric acid and then making fine adjust-

ments with 2 M sodium hydroxide. In

this approach, 1 mL of citrate buffer is

added to the sample followed by 0.5 mL of

the sodium borohydride reagent. For the

subsequent As(V) measurement, another

0.5 mL aliquot of sodium borohydride is

added, followed by 1 mL of 6.0 M sulfuric

acid (H2SO4).

Instrumentation

A basic schematic of the instrument is

shown in Figure 1. Figure 2 shows an

inside view of the laboratory instrument.

The laboratory and field-deployable instru-

ments both are fully automated. All liquid

handling is conducted by a syringe pump

(designated as “SP” in Figure 1) connected

to a multiport distribution valve (DV).

The laboratory setup has an eight-port dis-

tribution valve, and the portable instru-

ment has a six-port distribution valve. The

distribution valve ports are respectively

connected to a sample container, a waste

bottle, a reaction chamber (RC), and res-

ervoirs respectively containing solutions of

sodium borohydride, sulfuric acid, citrate

buffer, and water. One port of the distri-

bution valve in the laboratory instrument

is left unused and vented to the atmo-

sphere. With the field instrument, one of

the ports of the six-port distribution valve

is connected to the common port of a

three-way solenoid valve to effectively cre-

ate a seven-position valve. The normally

open and normally closed ends of the

solenoid valve are connected to reservoirs

containing solutions of sulfuric acid and

citrate buffer, respectively. The temporal

operational sequence, aspiration–dis-

pense volumes, and aspiration–dispense

velocities are programmable. The reac-

tion chamber (RC) is a 20-mL cylindrical

chamber made of poly(methyl methac-

rylate) (PMMA), the bottom of which is

connected to a tube that drains to waste

via a normally closed solenoid valve (SV1).

Three fluid lines enter through the top of

the reaction chamber. One of the tubes

reaches the bottom of the reaction cham-

ber and carries all the liquid delivered by

the syringe pump. A second line reaches to

a point inside the reaction chamber that is

just above the maximum fill volume of the

reaction chamber in operation such that

the headspace can be swept. It forms an air

delivery line that comes from a three-way

solenoid valve (SV2), and passes through

a flow restriction tube (R2) (PTFE, 0.30

mm i.d., 68 cm long). The third fluid line

entering the reaction chamber is a gas exit

line and terminates at the top. It goes to

a third solenoid valve (SV3), identical to

SV2. The flow restrictors, R1 and R2, join

at a tee (T), which splits the output of an

air pump. The other side of the tee goes

through restrictor tube R1 (PTFE, 0.30-

mm i.d., 34 cm long) and through an

ozone generator. The ozone generator con-

sists of two concentric glass tubes placed

inside a polypropylene tee. The inner

glass tube (0.7 mm i.d., 2 mm o.d., 16 cm

long) is sealed at both ends and contains

a nichrome wire (0.5 mm thick, 16.5 cm

long) that serves as the high-voltage anode.

The outer surface of the other glass tube

Figure 2: Inside view of the laboratory version of the instrument.

20

Sig

nal in

ten

sity

(V

)

Sig

nal in

ten

sity

(V

)

18

16

14

12

10

8

6

4

2

0

0 2000 4000 6000 8000

0.5

0.4

0.3

0.2

0.1

0.0

0 500 1000

Time (s)

Time (s)

Blank

1 ppb

2 ppb 100 ppb

75 ppb

60 ppb

40 ppb

30 ppb

20 ppb15 ppb

10 ppb

5 ppb2 ppb

1500 2000

Figure 3: Calibration plot for total arsenic. Note that the blank is actually responding to arsenic present in the standard-grade sulfuric acid reagent.



(4 mm i.d., 6.2 mm o.d., 10 cm long) is

wrapped with aluminum foil, and it serves

as a cathode. The optimized flow rates

were 60 mL/min through ozone generator

and 30 mL/min for the headspace flush

flow. The output of the ozone generator

and the output line of the reaction cham-

ber from the third solenoid valve (SV3)

terminate in a polypropylene tee on top

of the chemiluminescence chamber (CC).

The chemiluminescence chamber is fabri-

cated from the bottom ~5-cm portion of

a 12-mm i.d. glass test tube. It is silvered

on the exterior with a commercial silvering

solution and then painted black repeatedly

with black epoxy-based paint.

The bottom of the test tube is closed by

covering it with a 0.15-mm-thick, 25-mm-

square microscope cover glass. A hole (3

mm) is drilled into the top of the dome.

The tee assembly is then cemented using

epoxy adhesive. The tee assembly has a

glass tube (1.7 mm i.d., 3.0 mm o.d., 4.6

cm long) attached to it at the bottom, and

it carries both ozone and arsine to the bot-

tom of the chemiluminescence chamber so

that they have sufficient time to react and

produce chemiluminescence. The length

of the PTFE tube carrying arsine inside

the chemiluminescence chamber can be

changed by either pulling it out or push-

ing it inside the glass tube. Another hole

(1 mm) is drilled near the top to serve as

the gas exit and it leads to a vent cartridge

containing granular activated charcoal to

catalytically destroy any excess ozone in

the exit stream. The volume of the che-

miluminescence cell is ~5.5 cm3. All the

conduits leaving and entering the che-

miluminescence chamber were opaque

black PTFE tubes (1.1-mm i.d.). The che-

miluminescence chamber is mounted on

top of a miniature photomultiplier tube

(PMT). The PMT along with the chemi-

luminescence chamber is placed in a plas-

tic opaque black box so that there is no

interference from room light. The output

of the PMT was further amplified using

a secondary two-stage operational ampli-

fier. The overall gain is 1000× with a time

constant of 1 s.

For the laboratory instrument, the data

were acquired on a 12-bit USB data acqui-

sition card using LabVIEW (National

Instruments)-based software developed

in-house. The LabVIEW program also

triggers the operational sequence of the

syringe pump and the opening of the of

the solenoid valves through the digital

outputs of the same data acquisition card.

The final results are displayed on a laptop

computer. The operational sequence of

the portable instrument was controlled by

a microcontroller and the results were dis-

played on a color multitouch liquid crystal

display panel.

Operational Sequence

The typical operational sequence for total

arsenic estimation for both instruments is

similar, as follows:

1. Aspirate 2.5 mL of sample and dis-

pense it to the reaction chamber.

2. Aspirate 0.5 mL of sulfuric acid and

dispense it to the reaction chamber.

3. Aspirate 1 mL of water and dispense

it to the waste reservoir. Repeat this

step four times to rinse and clean the

syringe.

4 Aspirate 0.5 mL of sodium borohy-

dride solution and dispense all of it to

the reaction chamber.

5. Wait for 30 s to allow the arsine gas

to accumulate at the headspace of the

reaction chamber.

6. Open the solenoid valve SV3.

7. After 2 s, open the solenoid valve SV2

and record the signal height for 60 s.

This step allows the air to purge all the

arsine gas out of the reaction chamber.

8. Close solenoid valve SV3 and open

solenoid valve SV1 to allow the

contents of the reaction chamber to

drain off.

9. Close solenoid valves SV1 and SV2.

10. Aspirate 2.5 mL of water and dispense

it to the reaction chamber. Repeat this

step one more time so that the reaction

chamber is flushed with 5 mL of water.

11. Open solenoid valves SV1 and SV2 so

that the contents are drained out.

12 Close solenoid valves SV1 and SV2.

The software converts the recorded

signal height into the amount of the total

arsenic using a calibration curve and dis-

plays it on-screen. The complete cycle

takes ~3 min. For the laboratory setup,

the operational protocol for the sequential

analysis of As(III) and As(V) is as follows:

1. Aspirate 2.5 mL of sample and dis-

pense it to the reaction chamber.

2. Aspirate 1 mL of citrate buffer and

dispense it to the reaction chamber.


it to the waste reservoir. Repeat this

step four times to rinse and clean the

syringe.

4. Aspirate 0.5 mL of sodium borohy-

dride solution and dispense all to the

reaction chamber.

20

15

10

5

0

0 20 40 60 80 100

Sig

nal in

ten

sity

(V

)

Signal i

ntensit

y = 0

.1923 ±

0.0015*[A

s in p

pb]

+ 0

.0951 (±

0.0016),

R2 =

0.9

993

Arsenic concentration (ppb)

Figure 4: Calibration data for the total arsenic measurement.


excellent performance

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015 T

herm

o F

isher

Scie

ntific

Inc. A

ll rights

reserv

ed

. A

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ad

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re

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o F

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5. Wait for 30 s to allow the arsine gas

to accumulate at the headspace of the

reaction chamber.

6. Open the solenoid valve SV3.

7. After 2 s, open the solenoid valve SV2

and record the signal height for 60

s. This measurement gives the signal

height for As(III) species.


to the waste. Repeat this step four

times to rinse the syringe.

9. Aspirate 1 mL of sulfuric acid and dis-

pense to the reaction chamber.


it to the waste. Repeat this step four

times.

11. Aspirate 0.5 mL of sodium borohy-

dride solution and dispense all to the

reaction chamber.

12. Wait for 30 s to allow the arsine gas to

accumulate.

13. Open solenoid valve SV3.

14. After 2 s open solenoid valve SV2 and

record the signal height for 60 s. This

measurement gives the signal height

for the As(V) species.

15. Close solenoid valve SV3 and open

solenoid valve SV1 to allow the con-

tents of the reaction chamber to drain

off.


17. Aspirate 2.5 mL of water and dispense

to the reaction chamber. Repeat this

step one more time so that the reac-

tion chamber is flushed with 5 mL of

water.

18. Open solenoid valves SV1 and SV2 so

that the contents are drained out.


As mentioned before, in the case of

the portable field instrument the syringe

pump is connected to a six-port distri-

bution valve and one of the ports of the

six-port distribution valve is connected

to the common port three-way solenoid

valve. While working in the sequential

analysis mode, this valve should be ener-

gized before step 1, so that the citrate

buffer is connected to the distribution

valve and de-energized after step 4. The

signal heights for As(III) and As(V) are

then fed into the corresponding calibra-

tion curves to obtain the amount of the

respective As species. The time required

for one complete sequential analysis is

about 6 min.

Results and Discussion

Parametric Optimization

Ozone Flow Rate. The air f low rate

through the ozone generator was opti-

mized at 60 mL/min, and the ozone

produced at this f low rate was mea-

sured iodometrically to be 0.25% v/v. At

lower flow rates the ozone concentration

decreased, and at increased flow rates the

amount of ozone produced was diluted.

The ozone generator was operated at a

duty cycle of about 30%. Increasing the

duty cycle resulted in excessive heat gen-

eration, which reduced the ozone con-

centration.

Prereaction. The chemiluminescence

from the arsine–ozone reaction is not

instantaneous and needs some finite

amount of time to reach a maximum.

Hence, the chemiluminescence signal

intensity was optimized by varying the

length of the black PTFE tube carry-

ing arsine inside the chemiluminescence

chamber. The chemiluminescence signal

intensities are at a maximum when the

PTFE tube has been withdrawn 4.1 cm

from the tip of the glass tube, so that

only 0.5 cm of it is inside the glass tube.

Air purge flow rate. With addition

of sodium borohydride in the reaction

chamber, about 50 mL of hydrogen is

also evolved along with arsine, and this

hydrogen can be used to purge the reac-

tion chamber. However, when the reac-

tion chamber was purged with air at a

flow rate of 30 mL/min, the signal inten-

sities reached their maximum. Increasing

the f low rate further decreased the sig-

nal intensity by diluting the arsine. The

same air purge also helps to drain out the

contents of the reaction chamber quickly.

Accumulation time. Reduction of

As(V) to arsine was observed to be slower

than the reduction of As(III). This dif-

ference in reduction rate resulted in a

significant difference in chemilumi-

nescence signal heights of the two spe-

cies. To overcome this signal height

difference, the solenoid valves SV2 and

SV3 were kept closed for 30 s after the

addition of sodium borohydride, and

arsine gas was allowed to accumulate

and then was released into the chemi-

luminescence chamber as a pulse. With

an accumulation time > 30 s, the signal

intensities for As(V) and As(III) became

essentially equal.

Total Arsenic Determination

The results with As(III) standards in

the concentration range 1–100 μg/L

are shown in Figure 3. The inset figure

shows the comparison of the blank read-

ings with 1 and 2 μg/L of total arsenic.

The blank readings show a chemilumi-

nescence signal intensity of 0.067 ± 0.001

V, which resulted from the As present as

2.0

Signal 1

Signal 2

20 ppb As(III) +0 ppb As(V)15 ppb As(III) +5 ppb As(V)10 ppb As(III) +10 ppb As(V)5 ppb As (III) +15 ppb As(V)0 ppb As (III) +20 ppb As(V)

Blank

1.5

1.0

0.5

0.0

0 60 120 180

Time (s)

Sig

nal

inte

nsi

ty (

V)

240 300 360

Figure 5: Response of the analyzer to the sequential measurement of different mix-tures of As(III) and As(V) at low levels (20 µg/L total arsenic).



an impurity in the sulfuric acid. As men-

tioned above, under strongly acidic con-

ditions (pH ≥ 1), both As(III) and As(V)

are very efficiently converted to arsine.

With an accumulation time of 30 s, the

As(III) and As(V) chemiluminescence

signals are indistinguishable. Hence,

either As(III) or As(V) standards can

be used as calibrant for total As assays.

The peak height and the peak area both

produce a linear response in the range of

interest (1–100 μg/L). For ease and sim-

plicity, peak height has been plotted to

obtain the following calibration curve:

Peak height, V = 0.1923(±0.0015),

total As (μg/L) + 0.0675(±0.0016) [1]

r2 = 0.9992

The corresponding calibration data for

total arsenic measurement are shown in

Figure 4. For a 2.5-mL sample, the limit

of detection (LOD) (for S/N = 3) based

on the standard deviation of the blank is

estimated to be 0.03 μg/L.

Sequential Arsenic Analysis

Both the laboratory instrument and the

field instrument can measure As(III)

and As(V) separately. Operating first

at pH 4.5, the response is mostly from

As(III) with a small response from As(V).

Subsequent strong acid measurement of

this sample results in a response that is

predominantly from As(V) with a small

response from residual unreacted As(III).

The software then solves the relevant

bivariate calibration equation to produce

the final results. The actual response

data from different mixtures of As(III)

and As(V) are shown in Figure 5. The

relevant bivariate equations (signal repre-

senting peak height) are as follows:

As(III), μg/L = 9.74*signal 1,

volts – 0.887*signal 2, volts [2]

As(V), μg/L = -1.35*signal 1,

volts + 10.6*signal 2, volts [3]

Interference Studies

It is well known that ozone produces che-

miluminescence with many volatile gases.

However, compared to the chemilumi-

nescence from arsine, most of the volatile

gases produce weak chemiluminescence.

Idowu and colleagues (11) have previ-

ously shown that under the above test

conditions this approach does not suffer

from significant interferences from other

common waterborne species, including

bicarbonate, nitrate, sulfate, and, nota-

bly, sulfide. They also tested various tap

water samples and other samples and car-

ried out a blind intercomparison with the

U. S. Geological Survey. Results from

the gas-phase chemiluminescence tech-

nique were in very good agreement with

those obtained with benchmark tech-

niques, notably graphite furnace atomic

absorption spectrometry (GFAAS) and

liquid chromatography–ion chromatog-

raphy–inductively coupled plasma mass

spectrometry (LC–IC–ICP-MS).

Conclusion and Future Scope

Arsenic-contaminated drinking water

is a serious issue, not only in develop-

ing countries but also in industrialized

nations. The widespread use of hydrau-

lic fracturing is only likely to exacer-

bate this problem. Globally about 140

million people are affected by arsenic

poisoning. The need to develop better,

cost-effective, field-deployable instru-

ments for speciated arsenic analysis is

a real societal issue in places that can

least afford expensive instrumentation.

We have described here the use of a fully

automated, extremely sensitive instru-

ment that can be assembled at modest

cost and thus can potentially replace

test kits that use toxic mercury and lead

compounds. It will provide regulatory

bodies with more elaborate data and a

better understanding of arsenic levels

to make better informed and accurate

decisions.

Visible luminescence from the arsine–

ozone reaction is highly selective and

sensitive, and has been known for a long

time (12,13). If all the automated fluid

handling were omitted and the instru-

ment could still provide accurate quan-

tification near the regulatory limit of

10 μg/L as has already been proven (14),

one would have an even more afford-

able instrument for measuring arsenic in

the field and could do so more rapidly

and more accurately than possible with

currently available technologies. Future

work involves development of an instru-

ment that can be used for industrial pur-

poses based on continuous electrochemi-

cal reduction of arsenic to arsine, which

would help monitor arsenic levels in

large-scale continuous-flow operations.

Acknowledgment

This work was supported by the National

Science Foundation grant PFI: AIR-TT

IIP-1414383. We thank Scott Evans of

Lumion Laboratories, Inc., for market

research.

References

(1) M.K. Sengupta, A. Mukherjee, M.A. Hos-

sain, S. Ahmed, M.M. Rahman, D. Lodh,

U.K. Chowdhury, B.K. Biswas, B. Nayak,

B. Das, K. C. Saha, D. Chakraborti, S. C.

Mukherjee, G. Chatterjee, S. Pati, R. N.

Dutta, and Q. Quamuzzaman, Arch. Environ.

Health 58, 701–702 (2003).

(2) M.M. Karim, Water Res. 34, 304–310 (2000).

(3) J. Nriagu, P. Bhattacharya, A. Mukherjee, J.

Bundschuh, R. Zevenhoven, and R. Loep-

pert (Eds.), Arsenic in Soil and Groundwater

Environment (Elsevier, Amsterdam, 2007),

pp. 3–60.

(4) United States Geological Survey. National

Water Quality Assessment. http://water.usgs.

gov/nawqa/trace/arsenic.

(5) B.E. Fontenot, L.R. Hunt, Z.L. Hildenbrand,

D.D. Carlton Jr., H. Oka, J.L. Walton, D.

Hopkins, A. Osorio, B. Bjorndal, Q.H. Hu,

and K.A. Schug, Environ. Sci. Technol. 47,

10032–10040 (2013).

(6) J.A. Gomesa, D. Cocke, S. Varma, H. More-

noa, and E. Peterson, ECS Trans. 2(14),

57–70 (2007).

(7) J. Feldmann, Rev. Environ. Contam. Toxicol.

197, 61–76 (2008).

(8) H. Gutzeit, Pharmaz. Zeitung. 24, 263 (1879).

(9) A. Hussam, M. Alauddin, A.H. Khan, S.B.

Rasul, and A.K. Munir, Environ. Sci. Technol.

33, 3686–3688 (1999).

(10) D.G. Kinniburgh, and W. Kosmus, Talanta

8, 165–180 (2002).

(11) A.D. Idowu, P.K. Dasgupta, Z. Genfa, and

K. Toda, Anal. Chem. 78, 7088–7097 (2006).

(12) K. Fujiwara, Y. Watanabe, K. Fuwa, and J.D.

Winefordner, Anal. Chem. 54, 125–128 (1982).

(13) M.E. Fraser, D.H. Stedman, and M.J. Hen-

derson, Anal. Chem. 54, 1200–1201 (1982).

(14) M.K. Sengupta, Z.A. Hossain, S.I. Ohira,

and P.K. Dasgupta, Environ. Pollut. 158,

252–257 (2010).

Purnendu K. Dasgupta and

Arup K. Ghosh are with the

Department of Chemistry and Biochemistry

at the University of Texas at Arlington

in Arlington, Texas. Aditya N. Das is

with the University of Texas at Arlington

Research Institute in Fort Worth, Texas.

Please direct correspondence to:

[email protected] ◾

ES676751_LCGCSUPP1015_017.pgs 09.25.2015 17:48 ADV black


Brent H. Harris, Justin L. Neill, Robin L. Pulliam, and Matthew T. Muckle

Fourier Transform Molecular Rotational Resonance Spectroscopy: Bridging the Gap Between Spectroscopy and Chromatography for VOC analysis

The connection of a quantum spectral fingerprint to molecular structure

makes spectroscopy ideal for chemical detection. Even with the broad utility

of chromatography and mass spectrometry, there is still a rigor to expand

the applicability of high-resolution spectroscopy by miniaturizing Fourier

transform nuclear magnetic resonance (FT-NMR) spectroscopy and enhancing

the performance of mid-infrared (IR) techniques. While IR instrumentation

experts have been incorporating the latest diode lasers, molecular rotational

resonance (MRR) spectroscopists have designed a digital, solid-state approach

to reach sub-terahertz (millimeter–submillimeter-wave) molecular spectroscopy

from the radio regime. Recent innovations for FT-MRR techniques have

finally brought millimeter-wave spectroscopy into the modern age. FT-MRR

spectroscopy is applied here to gas analyses, air analysis, and headspace analysis

for sensitive, chemically specific detection of volative organic compounds

(VOCs) without the need for lasers, chemometrics, or chromatography.

Acting under federal acts for pol-

lution control (1–4), the United

States Environmental Protection

Agency (US EPA) sets standards for the

United States environmental footprint

based on health risk assessments (5) that

have increased the need to monitor more

chemicals at lower levels. Although the EPA

publishes compendiums of reliable meth-

ods for chemical analysis to measure trace-

level toxins (6,7), they are not necessarily

cost effective or compatible with the exper-

tise available to meet the reporting demand.

Since the public is the primary stakeholder

of the EPA, the level of involvement of the

commercial industry in setting regulatory

standards has traditionally been limited.

However, the EPA has made strides to col-

laborate with public and industrial stake-

holders recognizing the need to modernize

the method specific approach to standard-

ization that is too slow to keep up with new

technology (8–10). These efforts lay a path

to ease the analytical burden and increase

the data gathering potential. In this article,

we demonstrate the performance of a new

high-resolution spectroscopy technique to

add to the environmental analysis suite at

the benchtop or in the field: Fourier trans-

form molecular rotational resonance (FT-

MRR) spectroscopy.

To achieve accurate results, the current

EPA methods for volatile organic com-

pound (VOC) analysis detail extensive

sample preparation and separation strate-

gies that require hours of turnaround time,

creating both a throughput challenge and

the need for expert level operation. State-

ments about the requirement of “expert

judgment” (11) and restriction to “analysts

experienced in the use of . . .” and “skilled

in the interpretation of” (12) can be found

throughout the compendia. At detection

levels of nanogram per liter in water or

parts per trillion in air, it is a challenge

to make an interference-free composition

analysis. In addition to naming specific

background or matrix chemicals that

interfere (for example, water, isomeric pairs,

methylene chloride, sulfur dioxide, and

carbonyls), there is a specific call in each

of the methods to pay careful attention


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to chemical carryover effects. Most meth-

ods for air, water, and soil analysis neces-

sitate purge and trap, preconcentration

on sorbents, and cryotrapping. As a result,

washing-bake-out routines and multiple

analyses on blank samples are required to

establish an interference-free baseline (sub-

traction of blank values is not permitted).

In addition, practically all of the methods

require high-purity carrier gases, which

provide an additional source of interfer-

ence and may require preconditioning with

a chemical scrubber.

The pressure falls on analytical chemists

in contract laboratories and the chemical

manufacturing industry to adopt and vali-

date higher performance analytical instru-

mentation that can detect trace levels of a

broad range of toxins. Expensive instru-

ments that remove the need for specialized

separation methods can pay for themselves

in labor relief alone (13). Where applicable,

direct spectroscopy techniques are desired

for their simple operation, lack of sample

preparation requirements, and continuous

monitoring capabilities to meet the needs

for field analysis. Mid-infrared (IR) spec-

troscopy has become popular for envi-

ronmental monitoring since the introduc-

tion of diode-based lasers (14,15). These

technologies show promise for sensitivity,

but limitations in chemical selectivity. As

an example, an acrolein-specific method

based on a quantum cascade laser (QCL)

direct absorption spectroscopy requires

a chemical scrubber to select for acrolein

and remove chemicals that have incident

spectral overlap, particularly ethylene (16).

Mid-IR spectroscopy in the manufactur-

ing industry is typically reserved for clean

sample matrices.

QCLs are one approach to reaching the

so-called terahertz gap, a region between

microwave and infrared that has histori-

cally been underutilized for spectroscopy

and communications because of the lack of

high-power radiation sources and detectors

(17). QCLs can operate down to 0.95 THz

with a fundamental limitation to how large

of a photon (long wavelength) these diodes

can produce (18). There is also a limitation

in tuning range that restricts the chemical

coverage of any one spectrometer. Lower

frequency terahertz lasers are still looking

for a commercial kick-off market in imag-

ing applications. What has been altogether

unaddressed by instrumentation compa-

nies today is the application of sub-tera-

hertz spectroscopy for molecular rotational

resonance (MRR) spectroscopy, an active

research tool dating back to the 1940s (19).

However, the only major analytical appli-

cation has been for astrochemistry studies.

In a field where the chemical mixtures are

measured from light years away, it is under-

standable that astrochemists have been the

champions and advocates for advancing

MRR technology, what is perhaps the most

chemically specific spectroscopy.

A molecule’s pure rotational spectrum

is described very accurately by a Hamilto-

nian that is closely related to the molecule’s

three-dimensional (3D) mass distribution

through the three directional moment of

inertia tensor (20,21). Any difference in

mass distribution between two molecules

leads to distinct rotational spectra. Isomers,

conformers, and isotopologues can all be

resolved in a mixture. Using a Kraitchman

substitution analysis, physical chemists use

the site-specific isotopologue spectra to cal-

culate the atomic distribution and render a

3D image of the molecule (22). The ability

to resolve site-specific isotopologue spectra

has significant implications for using isoto-

pic ratios as a signature to trace chemical

pathways (23). In addition to the structure

connection, the Doppler-limited FT-MRR

spectra (in the 1–100 mTorr range) are so

well resolved that complex mixtures can

be analyzed without chromatography or

chemometrics. For example, millimeter or

submillimeter telescopes have been used to

catalogue the chemical inventory of molec-

ular clouds that can contain more than 50

different chemical species (24).

The technology leading to the recent

development of millimeter-wave FT-MRR

includes high-power (>20 mW), broad-

band (10–12% of center frequency) fre-

quency multiplier sources (25–27). The

260–290 GHz FT-MRR spectrometer

used in this study has sufficient bandwidth

to cover the repeating spectral patterns

of hundreds of organic volatiles. At room

temperature, the Boltzmann distribution

places the peak population for small mole-

cules (<120 amu) at rotational energy levels

where resonances are best measured in the

millimeter or submillimeter spectrum. For

the analysis of trace-level VOCs and other

gaseous toxins with a dipole moment (>0.1

D), the chemical specificity of high-reso-

lution millimeter-wave FT-MRR greatly

exceeds that of mid-IR spectra and reduces

the interference challenges. In this study,

we demonstrate the spectral resolution of

FT-MRR by applying it to a headspace

VOC mixture, and we show the sensitiv-

ity for several other gaseous toxins that fall

in the chemical niche of millimeter-wave

spectroscopy.

Experimental

The millimeter-wave FT-MRR spectrom-

eter operates at 260–290 GHz and is

described in more detail elsewhere (28,29).

It consists of a high-speed arbitrary wave-

form generator that is the fundamental

light source for both the sample excitation

and heterodyne receiver local oscillator. A

millimeter-wave active multiplier chain

(AMC) is driven by microwave input to

generate short (0.2–1 µs), phase-coherent

excitation pulses from 260 to 290 GHz

that are broadcast into a 65-cm, single

pass, stainless steel sample cell (~1 L in vol-

ume) treated with an inert coating, kept at

40 °C, and maintained under vacuum via

a turbomolecular pump. When empty, the

sample cell vacuum is maintained at 1–10

µTorr; when loaded with a gaseous sample,

the optimal pressure is 1–100 mTorr (stan-

dard temperature and pressure [STP] gas

and number of moles). The vacuum drives

the sample transfer, as opposed to a carrier-

gas flow.

An excitation pulse that is resonant with

a populated rotational transition induces

a macroscopic polarization of those rotat-

ing molecules and a coherence coupling of

the upper and lower angular moment state.

After the excitation pulse, the phase coher-

ent, free induction decay (FID) is detected

against zero background for up to 2 µs and

propagates into the heterodyne receiver for

down conversion and subsequent sampling

of the time domain emission on a digitizer.

The receiver local oscillator is also driven

by microwave input to an AMC. One exci-

tation–detection cycle is a total of 2–3 µs

depending on the excitation pulse dura-

tion. The sensitivity is enhanced by real-

time phase-coherent signal averaging in

the time domain. After the accumulated

time domain data is transferred out of the

digitizer field programmable gate array

(FPGA) memory, an apodization function

is applied and the spectrum is generated by

fast Fourier transform. For calibration and

correction of the spectrum intensity, the fre-

quency dependent spectrometer response is

captured by a calibration scheme by broad-

casting a reduced power waveform through


www.chromatographyonline.com OCTOBER 2015 AdvAncing EnvironmEntAl AnAlysis 21

the spectrometer. In broadband mode, a

series of many narrow bandwidth spectral

segments are concatenated for a full spec-

trum. In targeted mode, a single frequency

π/2 pulse is applied in repetition and only

one signal averaged spectrum is generated

at a time. The FT-MRR spectrometer is

also capable of a selective excitation mode

based on double resonance modulation

described in more detail elsewhere (29).

Static Headspace Sampling

For FT-MRR, any pressure balancing or

sample transfer via a carrier gas would only

serve to dilute the concentration of the

headspace analytes and limit the fractional

makeup of analytes in the 1–100 mTorr

spectrometer sample cell. We remove the

requirement for carrier gas altogether and

use the vacuum to drive the sample path.

Before injection of the analyte solution, a

standard 27-mL headspace vial is sealed

with a rubber septum and evacuated of

air through a sampling needle connected

to the spectrometer (2 min for sufficient

evacuation). Next, approximately 1 mL of

solution is injected via syringe. As the solu-

tion is injected into vacuum the volatiles

boil out, so the vapor-phase equilibration

is fast. In the absence of air (removed in

the vial evacuation step), the minimum

total pressure is that of the diluent vapor

pressure. After a minute of room-temper-

ature equilibration, an aliquot of head-

space is transferred through the sampling

needle and into the spectrometer cell by

manual control of a needle valve to reach

a total pressure of 10 mTorr and the FT-

MRR spectrum is measured on the static

gas sample. No method development for

salt treatment or heating is applied for

the broadband data set presented in the

results section. However, a heated control,

valve-loop sampling mechanism has been

published separately (29). The sample

for analysis is a Supelco EPA VOC Mix

6 standard composed of chloromethane,

dichlorodifluoromethane, choroethane,

bromomethane, trichlorofluoromethane,

and vinyl chloride dissolved in methanol

at 2000 µg/mL.

Gas Flow Sampling

For measurement of gas samples (from

Summa canisters, compressed gas contain-

ers, or Aldrich Sure/Pac containers) a steady

flow of approximately 5 SCCM (standard

cubic centimeters per minute) is regulated

by a mass flow controller attached directly

to the spectrometer measurement cell under

constant evacuation by the turbomolecular

pump. To minimize chemical carryover

in the sample transfer lines, the mass flow

controller connected to the sample cell

picks off its flow from a higher flow, flush-

ing line. After a steady flow is achieved it

can be adjusted to achieve an optimal flow

pressure inside the sample cell to maximize

signal strength until pressure broadening

starts offsetting the signal gains. For trace

analysis of known analytes, targeted mode

is used where 10 million signal averages

can be acquired in 40 s. A zero gas mea-

surement is also performed with scrubbed,

industrial grade, nitrogen gas to determine

any background interference. The samples

used for analysis were calibrated gas stan-

dards prepared at 50 ppm in nitrogen and

acquired from SpecGas, Inc.

Air Analysis with Cryotrapping

In this method, approximately 1 L of gas

is processed through a liquid nitrogen cold

trap consisting of a 25-cm-long, coiled 1/8-

in. o.d. stainless steel tube at a flow rate of

0.1 L/min for 10 min. It is then warmed,

and the vaporized volatiles are released into

the spectrometer sample cell to a total pres-

sure of 10 mTorr.


The EPA VOC Mix 6 mixture comprises

the six rapidly eluted gases out of the 75

volatiles in the EPA methods for VOC

analysis (see experimental section). We

diluted the mixture 10:1 by volume with

water to release the volatiles and better rep-

resent performance for a water matrix. Five

volatiles were detected, including metha-

nol. Trichlorofluoromethane and dichlo-

rodif luoromethane were not detected

because they partition poorly out of water,

have lower dipole moments, and are much

heavier compared to the rest of the analytes.

Although water is the most abundant por-

tion of the vapor and a favorable analyte

for FT-MRR, it is not detected in this

bandwidth because there are no transitions

in the 260–290 GHz band. Because the

total pressure in the measurement cell is

10 mTorr, it is under ideal gas conditions.

The only effect of the presence of water is

dilution since there is an upper limit to the

ideal pressure that can be transferred to the

spectrometer measurement cell.

The broadband mixture spectrum in

Figure 1 illustrates many of the key fea-

tures of FT-MRR spectroscopy. First, it is

clear that these six gases are well resolved

from each other; chromatographic separa-

tion and chemometric analysis is not nec-

essary. For a typical FT-MRR spectrum,

the full width at half maximum (FWHM)

line width is approximately 2 MHz out of

Figure 1: A 10-min, high dynamic range FT-MRR broadband spectrum of the head-space of an EPA VOC Mix 6 calibration standard. The mixture is composed of six gases dissolved in methanol at 2000 µg/mL and diluted 10:1 in water, by volume. Two com-ponents, trichlorofuoromethane and dichlorodifuoromethane, are not detected. The mixture spectrum is in black with scaled reference spectra overlaid in color. On top is the full 30-GHz high-resolution spectrum. The panels below expand the data set in the frequency dimension to show approximately 5% and 0.2% of the spectrum from left to right, respectively.



30,000 MHz of bandwidth and line cen-

ters are determined to within 100 kHz

(better than 0.1 ppm of center frequency).

The line shapes are controlled by the apo-

dization function to maximize baseline

resolution for enhanced dynamic range, an

important advantage that reduces Lorent-

zian pressure broadening and extends the

optimal pressure range compared to direct

absorption spectroscopy. For quantitation,

the intensity at line center is used rather

than the area under the curve, which

means complete resolution is not required

for composition analysis. The information

contained in 30,000 independent data

channels (where 1 MHz separation is con-

sidered resolved) is difficult to capture in

the fullband spectrum at the top of Figure

1. The insets better illustrate the spectrum

structure and the space available in the

band. With a noise level of 2× 10-4 mV

indicated in the figure, and a maximum

signal level of 0.4 mV (emitted by chlo-

romethane), this 10-min spectrum dem-

onstrates a dynamic range of three orders

of magnitude. However, the receiver can

tolerate signals up to 100 mV without com-

pression for a dynamic range of 105. The

practical limitation for signal averaging in

this full-band high dynamic range mode is

100,000 shots because of the time required

to reach any more significant drop in the

noise floor, which falls proportionally to

N1/2. For targeted excitation of a single

spectral transition, the noise floor will

fall appropriately out to 100 million shots

achievable in under 7 min. The ultimate

dynamic range feasible is very near 107.

Another notable feature is the charac-

teristic structure of the spectral intensity

profile and the appearance of repeating

patterns, representative of increasing values

of the total angular momentum state for a

particular molecule. In combination with

the high resolution, the spectral structure

in both frequency and intensity add to the

parameters for high confidence, automated

composition analysis by broadband library

matching—a straightforward algorithm

that involves least squares scaling of a pure

reference spectrum at known pressure to

the mixture spectrum. The result is a par-

tial pressure measurement of each gas, and

a direct measure of the number of moles

of gas in the 1-L sample cell. With a com-

prehensive library archive that stores the

partial pressure and calibration metadata,

chemical standards are not required. Sev-

eral reference spectra have been measured

and sensitivity calculations are listed in

Table I. Typical detection limits in FT-

MRR spectroscopy are in the 0.1–10 pmol

range. Table I reflects the fact that FT-

MRR is most sensitive to low-molecular-

weight, rigid, polar molecules.

Also of note in Figure 1, are the resolved

isotopologue patterns illustrated clearly for 79Br and 81Br bromomethane, approxi-

mately equal in natural abundance and

therefore of comparable intensity. These

two spectra are similar in their spectral

pattern, and shifted in frequency, depen-

dent upon how the change in mass affects

the principle moments of inertia (I = MR2).

For atoms further away from the center

of mass and lighter in mass, isotopic sub-

stitutions cause a greater shift. Since the

angular momentum is conserved (L =

Iω), a decrease in the moment of inertia is

accommodated with a higher frequency of

rotation for each angular momentum state

causing a higher frequency spread in the

spectrum. The direct relationship between

the change in the moment of inertia and

the spectral shift is how site specific iso-

topologues can be identified and molecu-

lar structure can be accurately calculated.

Current methods for isotopic ratio analysis

based on mass spectrometry (MS) destroy

all of the structural information because

the molecule is converted (for example,

to CO2) for ionization and detection (23).

Chlorinated molecules are important envi-

ronmental analysis targets for isotopic ratio

analysis (37Cl/35Cl), but chlorine chemical

conversion and detection by MS is a chal-

lenge (30). For FT-MRR, halogenated mol-

ecules have the potential to set up a strong

dipole moment, making for a very sensi-

tive measurement for capturing (37Cl/35Cl).

The 3:1 relative abundance ratio is well

represented in the relative intensity of the 35Cl-chloromethane spectrum and 37Cl-

chloromethane spectrum in Figure 1 as

well. Mid-IR spectroscopy is also used for

isotopic ratio analysis, but it is generally

not selective enough for organic molecules

with multiple carbon atoms.

Detection limits for several toxic gases

are summarized in Table II. These detec-

tion limits are for targeted analyses where

only a small frequency region around a line

of interest is measured, driving up sensitiv-

ity by approximately a factor of 10. Since

FT-MRR spectroscopy is performed at low

flow rates, less than 20 mL of STP gas is

required for direct analysis. The isotopo-

logue spectra scale in natural abundance

and further support the detection limits

as indicated in Table II with 34S-hydrogen

sulfide, D-hydrogen cyanide, 13C-formal-

dehyde, and 18O-formaldehyde. EPA meth-

ods for formaldehyde analysis follow the

commonplace technique of dinitrophenyl-

hydrazine (DNPH) derivatization (31). In

general, all of the molecules in Table II

pose challenges for analysis by conventional

methods because they are either reactive or

poorly detected without derivatization. It is

important to note that at millimeter wave-

lengths, precision optics are not required. In

the single, 65-cm pass configuration used

here, the chemical sample does not come

into contact with any mirrors.

For air analysis, we have also imple-

mented a simple cryotrapping method that

can yield almost a 1000-fold decrease in

detection limits for VOCs. Figure 2 shows

Table I: Measured broadband FT-MRR reference spectra (nonexhaustive)

ChemicalMolecular

Weight (amu)Net Dipole

Moment (D)LDL (pmol) Broadband

(10 min)

Hydrogen cyanide 27 2.7 1.2

Ethylene oxide 44 1.89 9.2

Acetonitrile 41 3.92 15

Acetaldehyde 41 3.92 27

Vinyl chloride 62 1.4 51

Dichloromethane 84 1.6 67

Chloroethane 64 2.06 67

Methanol 32 1.69 150

Acetone 58 2.19 210

Chloroacetonitrile 75 3 250

Chloroform 118 1.04 350

Ethanol 46 1.69 330

Toluene 92 0.36 2200



a side-by-side comparison of the direct gas

flow measurement and cryotrapped pre-

concentration measurement of a 500 ppb

phosphine sample in nitrogen. The phos-

phine signal after preconcentration is 800

times stronger. In a real air sample, con-

sideration must be given to the removal of

water to realize a 1000-fold sensitivity gain.

To test a temperature controlled release of

volatiles at -10 °C, and retain the water in

the cryotrap, we prepared a mixture of four

VOCs (acetaldehyde, acetone, ethanol,

and methanol) diluted to approximately

20 ppm with air. The 10 mTorr spec-

tra yield approximate detection limits of

1–2 ppb where direct, targeted FT-MRR

detection limits determined from experi-

mental reference measurements were on

the order of 1 ppm.

For all the results presented, very mini-

mal method development was required to

show how the FT-MRR spectrometer can

be used for simple, highly selective, sensitive

analytical chemistry. Methods that push

the detection limits of current technology

generally require a detailed sample prepa-

ration procedure that can introduce user

variability. The precision of the FT-MRR

detector has been assessed by consecutive

measurements to yield a short term devia-

tion in spectral intensity of 0.5% across

the 260–290 GHz band of operation. Per-

formance validation will predominantly

depend on the reproducibility of a vacuum

sample transfer method. A previously pub-

lished FT-MRR headspace analysis sam-

pling method shows promise for acceptable

linearity and precision (29). Reliable, auto-

mated FT-MRR sampling methods would

provide a benefit for both EPA and Food

and Drug Administration (FDA) initiatives

that require stringent monitoring of toxic

chemicals. The long method development

cycle times for detecting trace level muta-

genic impurities during drug development

process research and development (R&D)

and the ensuing process and quality con-

trol measures mirror the data throughput

challenge across the environmental moni-

toring industry. As a concluding note to the

implications of FT-MRR spectroscopy, the

recent advances applying phase-sensitive

microwave detection for the distinction of

enantiomeric pairs (32) provides a trans-

formative tool for environmental analyses

concerning the enantioselective toxicity of

chiral pesticides to biochemistry (33)—a

need equally applicable to the production

of enantiopure medicines.

Conclusions

Both the EPA and the FDA realize the

importance of streamlining a way to regu-

late and keep pace with technology. FT-

MRR spectroscopy is a timely introduction

that draws on innovation at the intersec-

tion of telecommunications, computa-

tional chemistry, and Fourier transform

millimeter-wave spectroscopy to add new

capabilities for high performance analytical

Table II: Targeted FT-MRR sensitivity for flow gas measurements of calibrated nitrogen mixtures*

Sample Frequency (MHz) TimeLDL mols (Flow |

Static) (pmol)LDL Conc. (ppb)

Ammonia (50.0 ppm) 572498.6† 3 min 13 | 0.25 35

Ethylene oxide (51.3 ppm) 291478.0 3 min 26 | 0.53 75

Vinyl chloride (98.6 ppm) 287158.6 80 s 30 | 1 200

Phosphine (468 ppb) 266944.7 12 min 270 | 1.2 200

Formaldehyde (43.0 ppm)-H2CO (98.8%, 42.48 ppm )-H2

13CO (1.07 %, 0.4601 ppm)-H2C18O (.205%, 0.8815 ppm)

6 min 7 | 0.05 10

281526.9274726.1274726.1

———

———

———

Hydrogen sulfde (50 ppm)- H2

32S (94.93%, 47.465 ppm)-H2

34S (4.29%, 2.145 ppm)

80 s 45 | 2 300

555240.0555254.0

———

———

———

Hydrogen cyanide (50.6 ppm)- HCN (99.98%, 50.58 ppm ) - DCN (0.012%, 6 ppb)

3 min 2 | 0.025 5

265886.4289644.7

——

——

——

*Samples prepared by SpecGas, Inc. †Measured in a FT-MRR spectrometer confguration with AMCs that operate at 520–580 GHz

0.16 140

120

100

80

60

40

20

0

468 ppb PH3 in N2

Direct sampling3-min measurementLDL = 80 ppb

468 ppb PH3 in N2

Preconcentrated__1 Lof gas20-min collection + measurementLDL = 100 ppb

Inte

nsi

ty (µ

V)

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00266920 266960

Frequency (MHz)266920 266960

Frequency (MHz)

Figure 2: A comparison of a targeted FT-MRR spectrum for a 468 ppb sample of phosphine in nitrogen gas measured by direct gas fow and cyrotrapping preconcen-tration. The signal enhancement achieved is approximately a factor of 800.



chemistry. In this study, we presented the

strengths of FT-MRR for simple, direct

analysis of VOCs and other toxic industrial

chemicals by applying it for gas analysis,

static headspace analysis, and air analysis

with minimal method development. The

results demonstrate highly specific detec-

tion for small (<120 amu) polar (>0.1 D)

gases and volatiles toward parts-per-billion

detection limits with pmol of sample—all

without lasers, chemometrics, chroma-

tography, or magnets. The performance

benchmarks for specificity, dynamic range,

and sensitivity are reported to set the stage

for more in-depth FT-MRR analytical

studies and robust sample transfer devel-

opment that will greatly reduce interfer-

ence challenges for trace-level analysis of

hazardous chemicals.

References

(1) Code of Federal Regulations (CFR), Title 40

Part 136, “Clean Water Act,” (U.S. Govern-

ment Printing Office, Washington, D.C,

2011), pp. 5–363.


Part 141, “Safe Drinking Water Act,” U.S.

Government Printing Office, Washington,

D.C, 1998), pp. 367–637.

(3) Code of Federal Regulations (CFR), Title 40 Part

239–282, “Resource Conservation and Recov-

ery Act,” (U.S. Government Printing Office,

Washington, D.C, 2003), pp. 309–594.


Part 50–97, “Clean Air Act Title III,” (U.S.

Government Printing Office, Washington,

D.C, 2011), pp. 5–358.

(5) J.R. Sobus, Y.M. Tan, J.D. Pleil, and L.S.

Sheldon, Sci. Total Environ. 409(22), 4875–

4884 (2011).

(6) Test Methods for Evaluating Solid Waste, Phys-

ical/Chemical Methods, (U.S. Environmental

Protection Agency, Washington, D.C., EPA

Report SW-846, Office of Solid Waste,

2007).

(7) Compendium of Methods for the Determina-

tion of Toxic Organic Compounds in Ambient

Air, (U.S. Environmental Protection Agency,

Office of Research and Development, Cen-

ter for Environmental Research Information,

Cincinnati Ohio, 1999).

(8) US EPA, Technology Verification Program

Fact Sheet, (U.S. Environmental Protection

Agency, Office of Research and Development,

National Risk Management Research Labo-

ratory, Revised 2013). Available at: http://

archive.epa.gov/nrmrl/archive-etv/web/pdf/

p1002ccd.pdf.

(9) B. Erickson, Anal. Chem. 69(23), 716A (1997).

(10) Hazardous Waste SW-846 Process Streamlined,

(U.S. Environmental Protection Agency,

Washington, D.C.). Available at: http://www.

epa.gov/epawaste/hazard/testmethods/sw846/

pdfs/mir-final-fs3.pdf.

(11) Code of Federal Regulations (CFR), Title

40 Part 439, “Measurement of Purgeable

Organic Compounds in Water by Capillary

Column Gas Chromatograph/Mass Spec-

trometry, Method 524.2,” (Office of Research

and Development, Environmental Monitor-

ing Systems Laboratory, Cincinnati, Ohio,

1992).

(12) SW-846: Test Methods for Evaluating Solid

Waste, Physical/Chemical Methods, Method

8260B, (U.S. Environmental Protection

Agency, Office of Solid Waste, Washington

D. C., 1996).

(13) J.M. Perkel, Biocompare, “GC-MS: The yin

to LC-MS’s yang” June 6, 2013. Retreived

from http://www.biocompare.com/Editorial-

Articles/138483-GC-MS-The-yin-to-LC-

MS-s-yang/.

(14) Spectroscopy, “Mid-IR Technologies Help EPA

Monitor Environmental Stressors,” March

19, 2012. Retreived on August 14, 2015 from

http://www.spectroscopyonline.com/mid-ir-

technologies-help-epa-monitor-environmen-

tal-stressors.

(15) ETV Joint Verification Statement. (U.S. Envi-

ronmental Protection Agency, Advanced

Monitoring Systems Center & National Risk

Management Research Laboratory, 2011).

Available at: http://nepis.epa.gov/Adobe/PDF/

P100DOXS.pdf.

(16) J.H. Shorter, Small Business Innovation and

Research Grant Phase II Final Report: Acro-

lein Monitor Using Quantum Cascade Laser

Infrared Adsorption. (U.S. Environmental

Protection Agency and Aerodyne Research,

Inc., Washington, D.C., 2009). Available

at: http://cfpub.epa.gov/ncer_abstracts/

index.cfm/fuseaction/display.highlight/

abstract/8382.

(17) R. Köhler, A. Tredicucci, F. Beltram, H.E.

Beere, E.H. Linfield, A. Giles Davies, D.A.

Ritchie, R.C. Iotti, and F. Rossi, Nature 417,

156–159 (2002).

(18) G. Scalari, C. Walther, M. Fischer, R. Terazzi,

H. Beere, D. Ritchie, and J. Faist. Laser &

Photon. Rev. DOI 10.1002/lpor.200810030

(2008).

(19) R.H. Hughes and E.B. Wilson, Phys. Rev. 71,

562 (1947).

(20) W. Gordy and R.L. Cook, Microwave Molecu-

lar Spectra (John Wiley and Sons, Hoboken,

New Jersey, 1984).

(21) C. Townes and A. Schawlow, Microwave Spec-

troscopy (Dover, Mineola, New York, 1975).

(22) J. Kraitchman, Amer. J. Phys. 21, 17 (1953).

(23) A Guide For Assessing Biodegradation and

Source Identification of Organic Ground

Water Contaminants Using Compound Spe-

cific Isotopic Ratio Analysis, (U.S. Envi-

ronmental Protection Agency, Office of

Research and Development, National Risk

Management Research Laboratory, Ada,

Oklahoma, 2008).

(24) A. Belloche, C. Comito, C. Hieret, K.M.

Menten, H.S.P Mueller, and P. Schilke, “The

Search for Complex Molecules in the ISM: A

Complete 3mm Line Survey of Sgr B2-N and

–M” presented at Molecules in Space & Labo-

ratory, Paris, France, 2007.

(25) T. Crowe, W. Bishop, D. Porterfield, J. Hesler,

and R. Weikle, IEEE J. Solid-State Circuits

40(10), 2104–2110, (2005).

(26) A.L. Steber, B.J. Harris, J.L. Neill, and B.H.

Pate, J. Mol. Spec. 280, 3 (2012).

(27) J.L. Niell, B.J. Harris, A.L. Steber, K.O.

Douglas, D.F. Plusquellic, and B.H. Pate, Opt.

Expres. 21, 19743 (2013).

(28) J.L. Neill, B.J. Harris, R.L. Pulliam, M.T.

Muckle, R. Reynolds, D. McDaniel, and B.H.

Pate, “Pure Rotational Spectrometers for

Trace-Level VOC Detection and Chemical

Sensing,” Proc. SPIE 9101, Next-Generation

Spectroscopic Technologies VII, 91010B, Bal-

timore, Maryland, 2014.

(29) B.J. Harris, R.L. Pulliam, J.L. Neill, M.T.

Muckle, R. Reynolds, and B.H. Pate, “Fourier

Transform Molecular Rotational Resonance

Spectroscopy for Reprogrammable Chemical

Sensing,” Proc. SPIE 9362, Terahertz, RF, Mil-

limeter, and Submillimeter-Wave Technology

and Applications VIII, 936215, February 2015.

(30) T. Kuder and P. Philp, Environ. Sci. Technol.

47, 1461 (2013).

(31) USEPA. Determination of Formaldehyde

in Ambient Air Using Adsorbent Cartridge

Followed by High Performance Liquid Chro-

matography (HPLC), Compendium Method

TO-11A, Center for Environmental Research

Information, Cincinnati, OH, USA, 1999.

(32) D. Patterson, M. Schnell, and J.M. Doyle,

Nature 497, 475–477 (2013).

(33) A.W. Garrison et al., in Chiral Pesticides: Ste-

reoselectivity and Its Consequences (ACS Sym-

posium Series, American Chemical Society,

Washington DC, 2011), Chapter 1.

Brent H. Harris, Justin L. Neill, Robin L. Pulliam, and Matthew T. Muckle are with BrightSpec, Inc., in Charlottesville, Virginia. Please direct correspondence to: [email protected] ◾



Inês C. Santos, Raquel B.R. Mesquita, and António O.S.S. Rangel

The State of the Art of Flow-Through Solid-Phase Spectrometry

Sample pretreatment is one of the bottlenecks in analytical chemistry,

especially when dealing with complex matrices like environmental

samples. When performed in a batch mode, sample handling methods

are tedious and time consuming. Therefore, the hyphenation of these

methods with flow-injection techniques yields many advantages. The

possibility of automation not only increases the determination rate, but

also decreases sample and reagent consumption. As a consequence,

analyte separation, enrichment, and elimination of sample matrix

becomes possible with an increase in selectivity and sensitivity.

This is a significant contribution for the analysis of environmental

samples because the analyte is usually present at trace levels in a

complex matrix. In this scenario, the state of the art of solid-phase

spectrometry (SPS) with a focus on the lab-on-valve (LOV) platform

is discussed. LOV facilitates the manipulation of bead suspension

for SPS with lower reagents consumption and waste production.

When analyzing environmen-

tal samples such as water,

soil, and plants, some major

challenges may be found. For example,

when dealing with dynamic systems

such as estuarine waters, spatial and

temporal variability may be encoun-

tered. For this reason, the analyte

concentration may range from low to

trace levels. Salinity in estuarine sys-

tems may be a good example, because

it presents both spatial variability

(proximity to the sea) and temporal

variability (tides). Solid environmental

samples, such as soil and plants, are

another example where difficulties may

be found because some type of extrac-

tion is needed to isolate and separate

the analyte from its matrix. Because of

these challenges, a sample pretreatment

is often necessary before identifying or

quantifying the analyte of interest to

increase the method’s selectivity and

sensitivity. Different separation tech-

niques such as liquid–solid extraction,

liquid–liquid extraction (LLE), and

gas chromatography (GC) and liquid

chromatography (LC) are available to

overcome these issues. In this manner,

analyte extraction and enrichment can

be performed along with the removal of

sample matrix interferences. However,

when performed in a batch mode, these

sample pretreatment methods are very

tedious and time-consuming. Further-

more, high amounts of organic solvents

are usually necessary, especially for

solvent extraction methods, which can

cause health and environmental prob-

lems because of their high volatility and

release into the environment (1).

In this context, the coupling of sepa-

ration techniques with f low-injection

methods not only allows the automa-

tion of the entire sample preparation

process, but also achieves a reduction

in reagent and sample consumption.

Also, an increase in the sensitivity of

the method can be obtained together

with an increase in throughput (1,2).



By incorporating external devices,

such as gas diffusion or dialysis units,

or resin packed columns, in the f low

manifolds, the analyte of interest can

be collected, enriched, and separated

from its matrix before detection in a

miniaturized fashion.

Different f low techniques can be

used according to their suitability for

the intended determination. Flow-

injection analysis (FIA) was f irst

described by Ruzicka and Hansen in

1975 (3) where the concept of complete

reaction and physical equilibrium was

discarded. The sample is injected in

a continuous f low of reagent and the

mixture is performed as the sample is

propelled downstream to the detector.

To overcome some of the FIA limita-

tions, a second generation was proposed

as an evolution to this technique. The

main principle of sequential-injection

analysis (SIA), the so-called second

generation of f low injection analysis,

is the programmable f low where the

mixing occurs by reversing the f low

of sample and reagents (4). With this

principle, SIA allows an even lower

consumption of reagents and eff luent

production. The third generation of

f low injection analysis, called sequen-

tial injection lab-on-valve (SI-LOV),

has the main characteristics of SIA (5).

However, this technique incorporates

the detection system in the selection

valve, which allows a working volume

in the microliter range. Additionally,

this technique allows handling solid

materials within the manifold conduits

in a relatively simple way. This feature

opens new perspectives for performing

several processes on the sorbent surface,

such as analyte enrichment, immobi-

lization of reagents, and derivatization

reactions. If the solid material is suf-

ficiently transparent, even the spectro-

metric measurement itself can be made

directly on the solid material—that is,

solid-phase spectrometry (SPS). This

approach is quite a breakthrough for

samples with complex matrices such

as environmental samples. SPS pro-

vides the ability to minimize possible

physical interferences (those caused by

sample intrinsic color or turbidity) and

chemical interferences. Also, as already

mentioned, the analyte is usually pres-

ent at trace levels, so this technique

allows the enrichment as well as sample

cleanup (for example, desalting). Addi-

tionally, a sensitivity enhancement can

be achieved because there is no need for

(a)

(b)

(c)

250C

um

ula

tive n

um

ber

of

art

icle

sC

um

ula

tive n

um

ber

of

art

icle

sC

um

ula

tive n

um

ber

of

art

icle

s

YearSPE LLEMembrane-based Chromatography



200

150

100

50

0

45

40

35

30

25

20

15

10

5

0

60

50

40

30

20

10

0

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Figure 1: Progression over the years of the papers coupling on-line sample pretreat-ment and (a) FIA, (b) SIA, and (c) SI-LOV. SPE = solid phase extraction and LLE = liquid-liquid extraction.



a previous elution before measurement.

This feature is explored in this article.

Hence, the coupling of FIA, SIA, and

SI-LOV with on-line sample pretreat-

ment procedures offers different advan-

tages when compared to performing

these procedures in a batch mode. Each

technique presents unique characteris-

tics that can contribute to the automa-

tion of the sample pretreatment proce-

dure. FIA requires a simpler manifold,

and because of its continuous f low, a

higher throughput can be achieved.

SIA and SI-LOV, in contrast to FIA,

require a more sophisticated manifold;

however, since they are based on a pro-

grammable f low, even lower sample and

reagent consumption can be achieved.

Also, since a selection valve is used

instead of an injection valve, different

reagents and devices can be coupled

and therefore multiparametric determi-

nations can be performed with the same

manifold. In the meantime, other f low

techniques have been described such as

multicommuted f low injection analysis

(MCFIA), multisyringe f low injection

analysis (MSFIA), and multi-pumping

f low analysis (MPFA) (6–8). Since they

are all based on a unidirectional f low

like f low-injection analysis—and to

facilitate the reading of this article—

all of these techniques were included

in the FIA group. This article discusses

the state of the art of f low-through SPS.

Furthermore, it provides a detailed

review on the use of SI-LOV for SPS.

On-Line Sample Pretreatment

The advantages of coupling f low tech-

niques with on-line sample pretreat-

ment procedures are diverse. In this

section, we focus on extraction and

preconcentration as sample pretreat-

ment techniques. In this context, liq-

uid–solid extraction or solid-phase

extraction (SPE), LLE, GC, LC, and

membrane-based techniques such as

gas diffusion, dialysis, and pervapora-

tion, are the techniques included in this

section for discussion. SPE, LLE, GC,

and LC are techniques used for separa-

tion of the analyte from possible sam-

ple matrix interferences. SPE and LLE

moreover can be used to enrich the ana-

lyte in a solid or liquid phase, respec-

tively. Membrane-based techniques

can also be used for analyte separation

from sample matrix as the species are

transferred through a membrane from

a donor to an acceptor solution. The

difference between dialysis and gas dif-

fusion is the membrane material. These

techniques can also be performed with

the purpose of dilution and microex-

traction.

A search on ISI Web of Knowledge–

Web of Science (Figure 1) was made for

the existing publications (between the

years 2000 and 2015) that couple sam-

ple pretreatment methods with f low

techniques.

As shown in Figure 1, SPE is the first

choice for on-line sample pretreatment

in all f low techniques. In comparison,

there have been few papers describ-

ing the hyphenation of LLE with f low

techniques. This method still requires

the use of organic solvents, although in

lower volumes when compared to batch

LLE, which may be the cause for it

being used less when compared to other

pretreatment methods.

The coupling of membrane-based

methods to f low techniques constitutes

an excellent tool for the monitoring of

dynamic systems. These on-line pre-

treatment methods have been reason-

ably used throughout the years, with

more application in the FIA and SIA

methods. The yields of these mem-

brane-based methods can be optimized

and adapted to the intended application

(for example, separation, enrichment,

or dilution). However, when coupled to

f low injection techniques, the obtained

yields are usually quite low as f low tech-

niques frequently present a short time

available for analyte transfer. This may

be one of the reasons for the decrease

in published works that use this on-line

sample pretreatment method.

Chromatography has been well

explored using SIA methods. In fact,

a signif icant increase in the pub-

lished papers where chromatography is

hyphenated to SIA is shown in Figure

1b. This increase can be explained by

the recent development of monolithic

columns (9–11) that, because of their

porosity, allow efficient separations

at lower pressure. Although this was

a good contribution, chromatogra-

phy usually requires a step of analyte

enrichment before separation, which in

turn makes the method more complex.

SPE coupled to f low techniques has

been well explored throughout the

years (1,12,13). Indeed, this method has

resulted in the improvement of simplic-

ity and ease of automation when com-

pared to the batch mode. By introduc-

ing a packed resin to the f low manifold,

analyte separation and enrichment can

be achieved in a few steps. In doing so,

the method’s throughput, sensitivity,

and selectivity may be increased.

Flow-Through SPS

The performance of on-line SPE was

a great advance in the automation of

sample pretreatment. In this context,

SPS was described for the first time by

SI-LOV

18%

SIA

15%

FIA

67%

Figure 2: Distribution of published articles that performed fow-through SPS by fow technique.



Yoshimura and colleagues (14). Both

techniques are based on analyte reten-

tion on a solid support. In SPE, the

analyte must be eluted from the solid

support toward the detector, but in

SPS the beads are trapped in a f low

cell where the signal is measured at

the surface of the beads (optosensing).

When compared to the traditional SPE

technique, SPS does not require the

step of analyte elution from the solid

support where dilution and partial

loss of the preconcentration capabili-

ties may occur. In fact, SPS not only

allows analyte retention and matrix

interference elimination, it also reduces

intrinsic sample and bead absorption by

resetting the absorbance baseline value

after propelling the sample through the

packed beads. Therefore, this technique

exhibits high sensitivity and selectivity

provided because of the in situ precon-

centration and detection of analytes on

the solid sensing support.

SPS has been mainly performed

using FIA, as shown in Figure 2, prob-

ably because of the simplicity of the

manifold.

The great advantage of perform-

ing f low-through SPS when compared

to batch SPS, besides consuming less

reagents and producing less waste, is

the possibility of bead reutilization, as

they are regenerated after each determi-

nation. Therefore, SPS is a good contri-

bution to the green analytical chemis-

try concept because there is a reduction

in sample, reagent, and solid support

consumption and eff luent production

by downscaling the analytical system.

Different approaches can be used for

SPS (15). In the first approach, the ana-

lyte is primarily retained on the beads

and then the chromogenic reagent is

added. This procedure is mainly applied

when the reagent has poor selectivity

for the analyte. For this reason, possible

matrix interferences must be removed so

the analyte is the only species available

to react with the chromogenic reagent.

In the second approach, the reagent is

first retained on the beads, functionaliz-

ing them, and then the sample is added.

This procedure is recommended when

the color reaction is highly selective for

the analyte and the product formed can

be sorbed on the solid support. Another

approach performed is the measurement

of the analyte intrinsic absorbance or

f luorescence without the need of using

a chromogenic reagent.

Detection Methods

To perform f low-through SPS, only a

few detection methods are available

because of the need to measure the

signal at the surface of the beads. The

major problem associated with this

approach is that, due to the packed sor-

bent in the f low cell, a large background

(a)

(b)

(c)

Phosphorescence

8%

Refectance

6%

Chemiluminescence

2%

Chemiluminescence

36%

Fluorescence

50%

Fluorescence

55%

Fluorescence

7%

UV–vis

spectrophotometry

34%

UV–vis

spectrophotometry

9%

UV–vis

spectrophotometry

93%

Figure 3: Distribution of published articles in fow-through SPS by type of detection system used in conjunction with: (a) FIA, (b) SIA, and (c) SI-LOV.



signal is already measured before the

determination. As there is a large back-

ground attenuance of the solid phase,

a relatively small absorbance of the

colored species adsorbed on the solid

phase is measured, which can decrease

the sensitivity of the method.

Consequently, the detection methods

described in the literature used for flow-

through SPS are ultraviolet–visible (UV–

vis) molecular absorption, f luorescence,

chemiluminescence, phosphorescence,

and ref lectometry (Figure 3), because

these allow the measurement of a signal

at the surface of a solid support (16).

According to Figures 3a and 3b, f luo-

rescence is the most common detection

method in FIA and SIA techniques

for f low-through SPS. Fluorescence is

more sensitive and selective than UV–

vis spectrophotometry; it is subject to

less inf luence from the solid material

background signal and interferences

from the sample matrix.

However, for SI-LOV, UV–vis is

clearly the most common detection

device used. In fact, the f low cell of

the SI-LOV can be configured for UV–

vis and f luorescence measurements by

means of optical fibers. However, f luo-

rescence is not as sensible as UV–vis

molecular absorption when performed

in this platform. Usually, higher f low

paths (1 or 1.5 cm) are advised for f luo-

rescence measurements and the f low

path of SI-LOV can only be 1 cm maxi-

mum, if no additional device, such as a

Garth cell (17), is used.

Lab-on-Valve Platform for SPS

The development of SI-LOV was a big

step toward miniaturization and automa-

tion of chemical analysis. Its new design,

which integrates the flow cell on top of

the multiposition valve, made the reduc-

Table I: SI-LOV methods for SPS

AnalyteSolid-Phase Resin

Functionalized Beads

SPS Mode ReagentDynamic Range

LODRSD (%)

Deter. Rate (h-1)

Sample Reference

Iron NTA superfow (agarose)

- Reusable 3-Hydroxy-1(H)-2-methyl-4-pyridinone

20.0–100 µg/L

9 µg/L 2.1; 3.4

13/14 Fresh and coastal waters

19

Proteins NTA superfow (agarose)

Copper Renewable Folin–Cio-calteu

up to 0.3 g/L

0.03 g/L

1.9 - 4.9

9 White wines

20

Copper Sephadex QAE A-25

Zincon Renewable — 10.0–100 µg/L

3 µg/L 2.5 8 River water

21

Cobalt Sephadex QAE A-25

PAN-S Renewable — 20.0–500 µg/L

8 µg/L 2.8 16 Tablets, spring and river water

22

Iron NTA superfow (agarose)

— Renewable SCN− 0.09–5.0 mg/L

0.02 mg/L

- 20 Wine 23

Cell density, hydrogen peroxide

Cytodex beads

— Renewable DCFH-DA 1×106–8×10 cells; 5–100 µmol/L

— 38 4 Live cells 24

Biotinyl-ated DNA

Agarose Streptavidin Renewable OliGreen fuorescent dye

0–9.93 ng 111 pg — — — 25

Nucleic acids

Porous beads

Streptavidin Renewable DNA probes

1–1000 pmol

1 pmol 7.2 3 No appli-cation

26

Proteins Agarose — Renewable — 0.06–12 µg/µL

A = 0.003

— 6 No appli-cation

27

Immuno-globulin G

Sepharose Protein G Renewable — 0.1–0.4 µg/µL

50 ng/µL

— — IgG samples

28

Antibodies Sepharose Biotinylated GAD65

Renewable Secondary antibody, HRPO sub-strate

100–400 ng/mL

20 ng/mL

2–5 2 Human serum

29

Biotin-containing conjugates

Agarose Streptavidin Renewable — 250–1500 pmol

— — 13 Human cell homog-enates

30

Immuno-globulin G

Sepharose Protein Renewable — 0–0.4 µg/µL

470 ng — — No appli-cation

31

Immuno-globulin G

Sepharose Protein Renewable — 4.0–100 µg/mL

5/10 µg/mL

— — No appli-cation

5

PAN-S = 1-(2-pyridylazo)-2-naphthol-sulfonic acid; zincon, 2-carboxy-2’-hydroxy-5’-sulfoformazylbenzene; DCFH-DA = dichlorofuores-cein diacetate.



tion of sample and reagent volumes and

eff luent production (5) possible. More-

over, the geometry of the channels in

the multiposition valve of the SI-LOV

allows the manipulation of beads that

can be trapped in different places in the

valve, an approach called bead injection

(BI). The use of beads and their entrap-

ment in the selection valve allows the

performance of SPE or chromatography

without the need for coupling external

devices such as columns. Furthermore, if

the beads are entrapped in the flow cell of

the multiposition valve, the analyte can

be retained and determined at the sur-

face of the beads (SPS). When compared

to FI-SPS and SIA-SPS, SI-LOV-SPS

offers the possibility to renew the adsor-

bents not only by chemical regeneration

(elution), but also by physical regenera-

tion, where the beads are discarded and

a new sensor is prepared in the flow cell

after each analytical cycle. By doing so,

no elution step is necessary to clean the

sensor and thus, no analyte or interfering

species accumulation occurs. Therefore,

the lifetime of the sensor is not a limi-

tation of the method. Bead injection in

an LOV platform for SPS simplified the

on-line sample pretreatment procedure

because column preparation, analyte

retention, enrichment, detection, and

elution-washing can be performed auto-

matically by computer control (18).

Absorbance and f luorescence mea-

surements can be carried out in the f low

cell that is integrated within an LOV

module by means of optical fibers. The

distance between the optical fiber ends

defines the optical pathlength, which

can be varied from 1 mm (Figure 4b) to

10 mm (Figure 4c). Fluorescence mea-

surements (Figure 4a) are carried with

optical fibers assembled at a 90° angle.

The use of a higher f low path may

increase the sensitivity of the method

because a higher mass of resin can be

packed in the f low cell. On the other

hand, a higher amount of sensor in the

flow cell may cause higher background

signal and therefore low analytical signal

decreasing the sensitivity of the method.

A review of all the works describing

the use of SI-LOV for SPS is presented

in Table I.

Almost all works use functional-

ized beads, where the reagent has been

previously retained on the surface of

the beads. This approach can increase

throughput, because there is no need

to aspirate the chromogenic reagent.

However, the chromogenic reagent

must be selective towards the analyte;

otherwise, there may be some possible

interferences from the sample matrix.

In fact, some researchers not only func-

tionalize the beads, but also use a sec-

ond reagent that is propelled after the

analyte is retained. By doing so, even

higher selectivity is obtained.

Almost all the described works use

the renewable approach, which means

that the sorbents are not reused but

renewed after each determination. As

previously discussed, this method is

not limited by the sensor’s lifetime

and no accumulation, either of the

analyte or of the interfering species, is

observed. When comparing the meth-

ods that use the reusable or the renew-

able approach, no significant difference

in the throughput is observed. Higher

determination rates would be expected

for the reusable approach as the sensor

is not built after each analytical cycle.

However, the need to elute and wash

the beads in the reusable approach, also

requires some time, which can decrease

the determination rate.

As shown in Table I, the SI-LOV-SPS

methods were applied to samples with

complex matrices such as human serum,

wine, and fresh and coastal waters. In

fact, the more recent works (19,21,22)

were applied to water samples, show-

ing the increased need for new and

more sensitive methods that can be

applied to these complex samples. The

presented works describe the efficient

removal of potential sample matrix

interferences and the methods were

successfully applied to the determina-

tion of the analyte of interest at trace

levels. In fact, limits of detection at the

microgram-per-liter level were achieved,

demonstrating the increased sensitivity

and selectivity obtained when perform-

ing f low-through SPS.

The chosen material for the solid

support was agarose because it is eas-

ily coated with different molecules to

modify its affinity according to the

intended determination. This is also

a good choice for bead injection in an

LOV platform as explained in the fol-

lowing section.

Adsorbent Characteristics

For the efficient retention and enrich-

ment of analytes, and for the efficient

removal of possible interfering matrix

substances, a suitable sorbent must be

selected. The interaction of the analyte

with the solid support is of extreme

importance, but the adsorbent itself

must fulfill some requirements so it

can be used as an optosensor in f low-

through SPS. The adsorbent charac-

teristics are more important when per-

forming SPS in a bead injection mode.

(a)

Out

Light

In

InIn

Light Light

Out

Out

(b) (c)Fluorescence

detectorUV–vis detector

UV–vis detector

Figure 4: SI-LOV-SPS fow cell confguration for (a) fuorescence measurements, (b) UV–vis measurements with a 1-cm fow path, and (c) UV–vis measurements with a 1-mm fow path.



When the sensor is manually placed in the f low cell, there is only the need for optical transparency to prevent high signal background. In the bead injection mode, as the optosensor is built by aspirating the beads through the manifold tubing, certain size and material requirements are necessary to prevent clogging and scratching of, for example, the LOV channels. Accord-ing to Ruzicka (17), the particles must be spherical with a size in the range of 20 to 150 µm. The bead size must be homogeneous to ensure reproducibility in the SPS method when bead injection is performed in a renewable approach. Soft polymer beads are preferable, as rigid beads may scratch the selection valve. Sephadex and sepharose beads are therefore a good choice for bead injec-tion applications because they fulfill the mentioned criteria by being globe-shaped and of regular size (13,32).

Conclusions

Automation of sample pretreatment techniques is of great interest because they are tedious and time consuming and consume large amounts of toxic reagents. The hyphenation of f low tech-niques with sample pretreatment tech-niques was a breakthrough in analyti-cal chemistry because the automation of these tedious methods was possible. Flow-through SPS has been a signifi-cant contribution to this field. This technique allows the analyte separa-tion and enrichment by removing pos-sible sample matrix interferences with detection on the surface of the sensor. Therefore, higher sensitivity and selec-tivity is achieved when comparing this technique with other on-line sample pretreatment techniques. SI-LOV-SPS was an even bigger advance as sensor preparation, analyte retention, enrich-ment, detection, and washing can be performed automatically in few steps by computer control.

Nowadays, the concept of green chemistry receives greater attention to prevent environmental pollution by chemical activities. The main aim is to minimize or eliminate reagent consumption and waste production if possible by automation and miniatur-ization of the analytical systems. Flow-through SPS is a good contribution

in this field because it allows a lower reagent and sample consumption with a decrease in waste generation.

In spite of presenting several benefits in on-line sample pretreatment, f low-through SPS also has some disadvantages. Since the sensor is built in the flow cell, high background signal can be experi-enced that may decrease the sensitivity of the method. Also, a gain in sensitivity by increasing the sensor length is difficult, especially when using SI-LOV because the flow path is limited to a maximum of 1 cm.

Acknowledgments

I.C. Santos thanks Fundação para a Ciência e a Tecnologia (FCT, Portu-gal) and Fundo Social Europeu (FSE) through the program POPH–QREN for the grant SFRH/BD/76012/2011. This work was supported by National Funds through FCT, through projects PTDC/A AG-MA A/3978/2012, and PEst-OE/EQB/LA0016/2013.

References

(1) S. Motomizu and T. Sakai, in Comprehen-

sive Analytical Chemistry: Advances in Flow

Injection Analysis and Related Techniques,

1st Ed. (Elsevier, The Netherlands, 2008),

pp. 159–199.

(2) M.D.L. de Castro, in Comprehensive Ana-

lytical Chemistry: Advances in Flow Injection

Analysis and Related Techniques, 1st Ed. (Else-

vier, The Netherlands, 2008), pp. 204–232.

(3) J. Ruzicka and E.H. Hansen, Anal. Chim.

Acta 78, 145–147 (1975).

(4) J. Ruzicka and G.D. Marshall, Anal. Chim.

Acta 237, 329–343 (1990).

(5) J. Ruzicka, Analyst 125, 1053–1060 (2000).

(6) B.F. Reis, M.F. Gine, E.A.G. Zagatto,

J.L.F.C. Lima, and R.A. Lapa, Anal. Chim.

Acta 293, 129–138 (1994).

(7) V. Cerdà, J.M. Estela, R. Forteza, A.

Cladera, E. Becerra, P. Altimira, and P.

Sitjar, Talanta 50, 695–705 (1999).

(8) R.A.S. Lapa, J.L.F.C. Lima, B.F. Reis,

J.L.M. Santos, and E.A.G. Zagatto, Anal.

Chim. Acta 466, 125–132 (2002).

(9) F. Svec and C.G. Huber, Anal. Chem. 78,

2100–2107 (2006).

(10) D. Šatínský, P. Solich, P. Chocholouš, and

R. Karlíček, Anal. Chim. Acta 499, 205–

214 (2003).

(11) M. Fernández, H.M. González-San

Miguel, J.M. Estela, and V. Cerdà, TrAC-

Trends Anal. Chem. 28, 336–346 (2009).

(12) M. Miró and E.H. Hansen, TrAC-Trends

Anal. Chem. 25, 267–281 (2006).

(13) M. Miró, S.K. Hartwell, J. Jakmunee, K.

Grudpan, and E.H. Hansen, TrAC-Trends

Anal. Chem. 27, 749–761 (2008).

(14) K. Yoshimura, H. Waki, and S. Ohashi,

Talanta 23, 449–454 (1976).

(15) A. Molina-Díaz, J.F. García-Reyes, and B.

Gilbert-López, TrAC-Trends Anal. Chem.

29, 654–666 (2010).

(16) S. Matsuoka and K. Yoshimura, Anal.

Chim. Acta 664, 1–18 (2010).

(17) Tutorial on Flow Based microAnalytical

Techinques, J. Ruzicka, Ed. (CD, http://

www.f lowinjection.com, USA, 4th Ed.,

2009).

(18) S.S.M.P. Vidigal, I.V. Tóth, and A.O.S.S.

Rangel, Anal. Methods 5, 585–597 (2013).

(19) R. Suárez, R.B.R. Mesquita, M. Rangel, V.

Cerdà, and A.O.S.S. Rangel, Talanta 133,

15–20 (2015).


Rangel, Talanta 96, 102–106 (2012).

(21) Y.-L. Yu, Y. Jiang, and R.-H. He, Talanta

88, 352–357 (2012).

(22) Y.-L. Yu, Y. Jiang, S. Dou, and J.-H. Wang,

Anal. Methods 4, 1718–1724 (2012).


Rangel, Talanta 84, 1298–1303 (2011).

(24) I. Lähdesmäki, P. Chocholous, A.D. Car-

roll, J. Anderson, P.S. Rabinovitch, and J.

Ruzicka, Analyst 134, 1498–1504 (2009).

(25) M. Decuir, I. Lähdesmäki, A.D. Carroll, and

J. Ruzicka, Analyst 132, 818–822 (2007).

(26) K.A. Edwards and A.J. Baeumner, Anal.

Chem. 78, 1958–1966 (2006).

(27) J. Ruzicka, A.D. Carroll, and I. Läh-

desmäki, Analyst 131, 799–808 (2006).

(28) Y. Gutzman, A.D. Carroll, and J. Ruzicka,

Analyst 131, 809–815 (2006).

(29) A.D. Carroll, L. Scampavia, D. Luo, Å.

Lernmark, and J. Ruzicka, Analyst 128,

1157–1162 (2003).

(30) Y. Ogata, L. Scampavia, J. Ruzicka, C.R.

Scott, M.H. Gelb, and F. Tureček, Anal.

Chem. 74, 4702–4708 (2002).

(31) A.D. Carroll, L. Scampavia, and J. Ruz-

icka, Analyst 127, 1228–1232 (2002).

(32) H.M. Oliveira, M. Miró, M.A. Segundo,

and J.L.F.C. Lima, Talanta 84, 846–852

(2011).

Inês C. Santos, Raquel B.R. Mesquita, and António O.S.S. Rangel are with the CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Supe-rior de Biotecnologia, at the Universidade Católica Portuguesa/Porto, in Porto, Portu-gal. Direct correspondence to: [email protected] ◾



Groundwater

A

B

C

D

E

F

G

Chady Stephan and Robert Thomas

The Benefits of Single-Particle ICP-MS to Better Understand the Fate and Behavior of Engineered Nanoparticles in Environmental Water Samples

Single-particle inductively coupled plasma–mass spectrometry (SP-ICP-

MS) is an exciting new technique for detecting and characterizing metal

nanoparticles at very low concentrations. It is fast and can provide

significantly more information than other traditional techniques, including

particle number concentration, particle size, and size distribution, in

addition to the concentration of dissolved metals in solution. The added

benefit of using ICP-MS is that it can distinguish between particles

of different elemental compositions. The study will investigate the

use of SP-ICP-MS to track the release of engineered nanoparticles

(ENPs) into the environment and to better understand their fate and

behavior specifically in drinking, surface, and wastewater samples.

The unique properties of engineered

nanoparticles (ENPs) have cre-

ated intense awareness in their

environmental behavior. Because of the

increased use of nanotechnology in con-

sumer products, industrial applications,

and healthcare technology, nanoparticles

are more likely to enter the environment.

For this reason, it is not only important

to know the type, size, and distribution

of nanoparticles in soils, potable waters,

and wastewaters, but it is also crucial to

understand their impact on the grow-

ing mechanism of crops used for human

consumption. Therefore, to ensure the

future development of nanotechnol-

ogy products, there is clearly a need to

evaluate the risks posed by these ENPs,

which will require proper tools to fully

understand their toxicological impact on

human health. Current approaches to

assess exposure levels include predictions

based on computer modeling, together

with direct measurement techniques. Pre-

dictions through modeling are based on

knowledge of how they are emitted into

the environment and by their behavior in

the samples being studied. Although the

life cycles of ENPs are now starting to be

understood, very little is known about

their environmental behavior. Prediction

through life-cycle assessment modeling

requires validation through measurement

at environmentally significant concentra-

tions. For ENPs that are being released

into the environment, extremely sensi-

tive methods are required to ensure that

direct observations are representative in

time and space. ENPs differ from most

conventional ‘‘dissolved’’ chemicals in

terms of their heterogeneous distribu-

tions in size, shape, surface charge, com-

position, and degree of dispersion. For

this reason, it is not only important to

determine their concentrations, but also

these other important metrics, particu-

larly when they are discharged and inter-

act with their real-world surroundings.

Impact of Nanoparticles

Released into the Environment

When nanoparticles enter the environ-

ment, they can undergo a number of

potential transformations that depend

not only on the properties of the nanopar-

ticles but also on the medium they are

being released into. These changes

typically involve chemical and physi-

cal processes, but they can also involve

biodegradation of surface coatings used



to stabilize many nanomaterials. For

example, the toxicity impact of nanoma-

terials on algae involves adsorption onto

cell surfaces, which has the potential to

disrupt transportation through the cell

membrane. Additionally, larger organ-

isms can directly ingest nanoparticles,

and then enter the food chain when they

are consumed by other aquatic and ter-

restrial forms of life. These processes are

further complicated by aggregation of

nanoparticles with other natural miner-

als and natural colloids, which will dra-

matically change their fate and potential

toxicity in the environment.

Nanomaterials from domestic, medical,

and industrial sources may also undergo

significant changes during wastewater

cleaning processes, such as the conversion

of silver nanoparticles to silver sulfide in

treatment plants. In addition, aggregation

of the nanomaterials with other miner-

als and organic matter in the wastewater

often results in the nanomaterial com-

bining with other solids in the effluent,

rather than remaining as a dispersed

nanoparticle suspension. So, apart from

atmospheric deposition or accidental spill-

age of nanomaterials directly into rivers,

lakes, and the surrounding land, waste-

water treatment remains by far the larg-

est source of nanomaterial contamination

either from the runoff into groundwater

sources and drinking water supplies or

from the raw sewage sludge that is often

spread onto the soil as a fertilizer (1).

Risk Assessment

Therefore, risk assessment for nanomate-

rial released into the environment is still

evolving, and reliable measurements of

environmentally significant concentra-

tions remain challenging. Predicted envi-

ronmental concentrations based on cur-

rent usage are low, but they are expected

to increase as the use of nanomaterials

increases. At this early stage, compari-

sons of estimated exposure data with

known toxicity data indicate that the

predicted environmental concentrations

are orders of magnitude below those

known to have environmental effects

on living plant and animal biota. As

more toxicity data are generated under

197A

u In

ten

sity

(co

un

ts)

400

350

300

250

200

150

100

50

00 0.2 0.4 0.6 0.8 1

Time (s)

PP

Figure 1: A suspension of nanoparticles (P) and dissolved analyte reaches the plasma where each particle is ionized, producing a signal that is measured as a single pulse.

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environmentally relevant conditions,

risk assessments for nanomaterials will

improve to produce accurate assessments

that ensure environmental safety.

Many analytical techniques applied

in materials science and other scientific

disciplines could also be applied to ENP

analysis. However, environmental and

biological studies may require that meth-

ods be adapted to work at extremely low

concentrations in complex matrices. The

most pressing research needs are the

development of techniques that introduce

minimal artifacts to optimize sensitivity,

specificity, and throughput, as well as dif-

ferentiating between naturally occurring

particles and manufactured nanoparti-

cles, together with a better understanding

of their dissolution properties.

Analytical Methodologies

The measurement and characterization

of nanoparticles is therefore critical to

all aspects of nanotechnology. In the

field of environmental health, it has

become clear that complete character-

ization of nanomaterials is important

for interpreting the results of toxicolog-

ical and human health studies. Metal-

containing ENPs are a particularly

significant class because their use in

consumer products and industrial appli-

cations makes them the fastest growing

category of nanoparticles (2).

Although many analytical techniques

are available for nanometrology, only

some can be successfully applied to envi-

ronmental and health studies. Methods

for assessing particle size distributions

include electron microscopy, chroma-

tography, laser light scattering, ultra-

filtration, and field f low fractionation.

However, the lack of specificity of these

techniques is problematic for complex

environmental matrices that may con-

tain natural nanoparticles having poly-

disperse size distributions and hetero-

geneous compositions. For this reason,

extremely sensitive detection techniques

are needed if specific information about

the elemental composition and concen-

tration of the nanoparticles is required.

Unfortunately, difficulties can also arise

with some detection techniques because

of a lack of sensitivity for characterizing

and quantifying particles at environmen-

tally relevant concentrations.

Role of ICP-MS

One technique that is proving invalu-

able for detecting and sizing metallic

nanoparticles is single-particle induc-

tively coupled plasma–mass spectrome-

try (SP-ICP-MS) (3). Its elemental speci-

ficity, sizing resolution, and unmatched

sensitivity make it extremely applicable

for the characterization of ENPs con-

taining elements such as Ag and Au and

compounds such as TiO2 and SiO2 that

have been integrated into larger products

such as consumer goods, foods, pharma-

ceuticals, and personal care products.

Much of the early work has focused

on the use of ICP-MS with particle

separation techniques, such as field

flow fractionation and chromatography

(4). However, more recently, SP-ICP-

MS has shown a great deal of promise

in several applications areas, including

the determination of concentrations of

silver nanoparticles in complex samples

(5). This technique is suited to differenti-

ate between the analyte in solution and

existing as a nanoparticle without any

prior separation techniques, simplifying

nanoparticle analysis while eliminat-

ing complex sample preparation steps

(6). This ability allows SP-ICP-MS to

provide information on the size and size

distribution of many varied and different

Table I: Effectiveness of three water treatment plants for removing TiO2 particles and dissolved titanium (11)

PlantPre- or Post-Treatment

Most Frequent Size (nm)

Particle Concentration (particles/mL)

Dissolved Concentration (µg/L)

1PrePost

170< MDL

432,000< MDL

17.91.21

2PrePost

156< MDL

451,000< MDL

11.71.17

3PrePost

15376

425,00017,237

10.6< MDL

19

7A

u I

nte

nsi

ty (

cou

nts

)C

ou

nts

Co

un

ts

400

350

300

250

200

150

100

50

0

100

80

60

40

20

0

200

180

160

140

120

100

80

60

40

20

0

1.557 1.5575 1.558 1.5585 1.559 1.5595 1.56 1.5605 7.7835 7.784 7.7845 7.785 7.7855 7.786 7.7865 7.787

0 1 2 3 4

Time (s)

Time (s)

65

Pulse

(high intensity)

Particle

lons

Continuous signal

(low intensity)

7 8 9 10

Figure 2: Metal nanoparticles (pulses) and metal ions in solution (continuous signal below the yellow-dashed line) being ionized in the plasma.



metal-based nanoparticles, as well as the

dissolved concentration of the analytes

under study. Let’s take a closer look at

the fundamental principle of SP-ICP-MS.

Fundamentals of SP-ICP-MS

SP-ICP-MS is based on the measure-

ment of the signal intensity produced by

a single particle. Nanoparticle suspen-

sions are sufficiently diluted to minimize

the chances that more than one particle

reaches the plasma at a time. The particle

is atomized and ionized, producing a sig-

nal of relatively high intensity, which is

measured as a pulse. This process is exem-

plified in Figure 1, which shows particles

(P) and dissolved analyte in the sample

aerosol entering the plasma and being

ionized. The ions then pass through the

interface region into the ion optics where

they are eventually separated in the mass

spectrometer. In this manner, particles are

detected as individual pulses, whereas the

dissolved analyte contributes to a continu-

ous background signal. The frequency of

pulses (events) provides the particle num-

ber concentration, whereas the intensity

of each pulse is proportional to the mass

of the nanoparticle. Because of the short

transient nature of the pulse, very short

integration times are necessary to maxi-

mize the detection of individual particles

as pulses of ions after they are ionized by

the plasma.

This process is exemplified in Figure

2, which shows both metal nanoparticles

and metal ions in solution being ionized

by an ICP-MS system. The signal from

the dissolved ions is represented by the

continuous signal below the dashed line,

while the ionized pulses of nanoparticles

are represented by the individual spikes.

For this approach to work effectively,

the speed of data acquisition and the

response time of the detector must be

fast enough to capture the time-resolved

nanoparticle pulses, which are typically

300–400 µs (3). If the electronics are not

fast enough, multiple or many pulses can

easily pass through in a single integration

window leading to inaccurate particle

counting and sizing. For this application,

the ICP-MS should be capable of using

dwell times shorter than the particle

transient signal time, thus avoiding false

signals generated from clusters of par-

ticles. In practice, for an instrument that

is optimized for nanoparticle character-

ization, this means using dwell times of

100 µs or less and a settling time of zero,

so that the pulse can be fully character-

ized and precisely integrated using a peak

area integration algorithm (7).

It is important to emphasize that ICP-

MS is a mass-based technique, so in SP-

ICP-MS, the particle size is determined

by relating the pulse intensity to an ele-

mental mass. With traditional ICP-MS

analysis, the first step in this process is to

create a calibration curve using dissolved

standards. This curve connects the sig-

nal intensity from the instrument to the

concentration of the analyte entering the

plasma. The next step is to relate the con-

centration of the dissolved analyte to a

total analyte mass that enters the plasma

during each reading. This relationship

between analyte concentration and the

mass observed per event is called the

mass flux, which is highly dependent on

the transport efficiency of the sampling

process and the instrument ion optics.

This transport efficiency must be calcu-

lated for each instrument and under the

given run conditions for the mass f lux

to be accurate. In this way, the resulting

calibration curve relates signal intensity

(counts/event) to a total mass transported

into the plasma per event. So by using

well-understood SP-ICP-MS principles,

the intensity of each individual pulse

(counts/event) can then be transformed

using the mass f lux calibration curve

to determine the particle mass, which

is easily converted to particle diameter,

by knowing the density and assuming

that the geometry (shape) of the par-

ticle is spherical (8,9). This is exempli-

fied in Figure 3, which shows a signal

of multiple gold nanoparticles over time

(Figure 3a), with an individual pulse on

the left-hand-side at the bottom (Figure

3b) and the dissolved ionic calibration

curve on the top (Figure 3c). The top

Table II: Nanoparticle spike recovery data in drinking water

Sample

Au Ag TiO2

Most Freq. Size (nm)

Part Conc. Spike Rec.

Diss. Conc. Spike Rec.



Diss. Conc. Spike Rec.



Diss. Conc. Spike Rec

1 98 97% 80% 98 97% 80% 102 9% 84%

2 97 88% 84% 97 88% 84% 87 6% 88%

3 101 94% 89% 101 94% 89% 87 6% 112%

(a)

(e)

(d)

(b)

(c)

Blank 500 ppt Ag+

100 ppt AgNP

“Unknown” NP sample, raw data

Ag+

calibration data Apply neb. effciency, fow rate, dwell time

500

400

300

200

100

00 20 40 60 80 100

0

Fre

qu

en

cy

of

Re

ad

ing

Fre

qu

en

cy

of

Re

ad

ing

Inte

nsi

ty (

cou

nts

/dw

ell

)

Inte

nsi

ty (

cou

nts

/dw

ell

)

20 40 60 80 100 120 140

140

120

100

80

60

40

20

0

600

500

400

300

200

100

0

70

60

50

40

30

20

10

0

0

40

80

12

0

16

0

20

0

24

0

28

0

32

0

36

0

40

0

44

0

48

0

52

0

56

0

60

0

64

0

68

0

72

0

76

0

80

0

84

0

88

0

92

0

96

0

00 1E-09 2E-09 3E-09 4E-09

0.2 0.4 0.6 0.8 1 1.2

Concentration (µg/L)

y = 540.1x + 0.1045

R2 = 0.99995

y = 1E+11x + 0.1045

R2 = 0.99995

Mass (µg/dwell)

Pulse Intensity (counts/dwell) Diameter (nm)

600

500

400

300

200

100

0

160 180

0 20 40 60 80 100 0 20 40 60 80 100

500

400

300

200

100

0

500

400

300

200

100

0

NP mass converted to diameter: apply elementmass fraction and density, assume a geometry

197A

u I

nte

nsi

ty (

cou

nts

)

Figure 3: The fundamental principles of converting nanoparticle pulse counts to diameter of the nanoparticle. Adapted with permission from reference 10.



right-hand-side of Figure 3d shows the

mass of the particle (in micrograms) per

dwell time after nebulizer efficiency and

flow rate have been applied. The mass

of the nanoparticle is then converted to

a diameter, based on the mass fraction

and the density, based on the shape of

the particle (Figure 3e) (10). Note: If the

nanoparticle is rod-shaped (for example)

a different calculation is used.

Now, let’s take a look at the real-world

applicability of SP-ICP-MS by evaluat-

ing its capability and suitability for char-

acterizing nanoparticles in environmen-

tal samples.

Real-World Applicability of

SP-ICP-MS for Water Samples

Silver (Ag) nanoparticles are among

the most commonly used nanoparticles

in consumer products, such as fabrics,

deodorants, and detergents because of

their antimicrobial properties. Therefore,

it is expected that Ag ENPs will find their

way into the environment, necessitating

ways to accurately and rapidly detect and

characterize them in a variety of environ-

mental matrices. Work has already been

performed demonstrating the ability to

successfully characterize Ag ENPs and

their dissolution characteristics in a vari-

ety of drinking water (11), surface water

(12), and wastewater samples (13). Let’s

take a closer look at these three very dif-

ferent application requirements for SP-

ICP-MS. Note: All the data presented in

the following section were generated on

a NexION 350D ICP-MS system using

the Syngistix Nano Application Software

Module (PerkinElmer Inc.) (14).

Drinking Water

SP-ICP-MS is an ideal tool for assessing

the efficiency of drinking water treat-

ment systems in removing silver, gold,

and titanium dioxide (TiO2) nanopar-

ticles without using any other analytical

technique. To evaluate the effectiveness

of the water treatment process for these

types of nanoparticles, Donovan and

colleagues (11) collected water samples

both pre- and post-treatment at three

water treatment plants in Missouri.

None of the six waters contained mea-

surable amounts of Ag or Au, either as

particles or dissolved species. However,

all source water samples contained TiO2,

as shown in Table I. Plants 1 and 2 effec-

tively removed both dissolved Ti and

TiO2 particles, as evidenced by the lower

amounts present in the post-treatment

waters than the pretreatment samples.

The results from Plant 3 differed from

the first two plants in that Ti-contain-

ing particles could still be detected after

treatment, although significantly less

than the pretreatment sample. However,

all the dissolved Ti was removed.

To check the accuracy of this method,

spike recovery tests were performed for

all metals, both as dissolved (Diss.) and

particle (Part.) concentrations. Three

samples were spiked with 2 µg/L of the

dissolved metals and 100-nm nanopar-

ticles at a concentration equal to 1 ×

105 particles/mL. Table II shows the

results of these spike recovery stud-

ies, which indicate accurate recoveries,

except for the concentration of TiO2

nanoparticles, which recovered below

10% for each sample. Low recovery for

TiO2 particles was most likely because

of aggregation in the standards and in

the water matrix. Without the addition

Figure 5: An Interactive view of data acquisition of intensity as a function of time for the QC 60 nm nominal diameter silver nanoparticles.

Groundwater

A

B

C

D

E

F

G

Figure 4: Possible fates of silver nanoparticles in surface waters: (A) Dissolution pro-cess leading to free ions release and smaller particles; (B) aggregation into larger particles, which may settle out of the water, depending on the aggregate size; (C, D) adsorption of released Ag+ and nAg, respectively, onto other solids present in the water; (E) formation of soluble complexes; (F) reaction with other components in the water, which may result in precipitation; (G) nAg remaining stable. Adapted with permission from reference 12.



of a surfactant, these uncapped TiO2

particles tended to aggregate, which

resulted in particle loss between dilu-

tions and in the water matrix where

they had an opportunity to react and

form new species or aggregate further

and fall out of solution. The highest

recovery obtained for these particles in

ultra-pure water and in a surface water

matrix were 24% and 9%, respectively.

Surface Waters

For this work, Hadioui and colleagues

(12) investigated the efficiency of SP-

ICP-MS for the detection and char-

acterization of metal nanoparticles in

fresh surface waters where they can be

involved in various physicochemical

processes as shown in Figure 4. In this

example, dissolved silver (A), including

released free Ag ions (C, D) and soluble

complexes (E) can easily and instantly

be measured by SP-ICP-MS. These dis-

solved species could also be determined

by ultrafiltration followed by total metal

quantification using ICP-MS or atomic

absorption spectroscopy (AAS), but

this procedure is time consuming since

it requires the pre-equilibration of the

membrane for at least three cycles of

centrifugation, generally 20 min each

(15). Aggregates (B) and remaining stable

silver nanoparticles (G) can be counted

and measured by other commonly used

techniques such as nanoparticle tracking

analysis (NTA), dynamic light scattering

(DLS), and transmission electron micros-

copy (TEM), but SP-ICP-MS is the only

method that can distinguish between

nano-Ag (nAg) and other metal-based

colloids in surface waters.

The surface water was sampled from a

river in Montreal, Canada, and filtered

with 0.2-µm filter paper before spiking

with silver nanoparticles. Nano-Ag sus-

pensions were added to water samples

with concentrations ranging from 2.5

to 33.1 µg Ag/L and left to equilibrate

under continuous and gentle shaking.

Commercially available suspensions of

gold and silver nanoparticles were used

in this work. A National Institute of

Standards and Technology (NIST) refer-

ence material (RM 8013) consisting of

a suspension of gold nanoparticles (60

nm nominal diameter, 50 mg/L total

mass concentration, and stabilized in

a citrate buffer) was used to determine

the nebulization efficiency, while two

types of silver nanoparticle suspensions

were used for the dissolution studies—

a citrate-coated version (40 nm and 80

nm nominal diameter) and a noncoated

version (80 nm nominal diameter). In

addition, 60-nm nominal diameter Ag

nanoparticles were used for quality con-

trol (QC) purposes.

An interactive view of data acquisition

of intensity as a function of time for the

60-nm nominal diameter silver nanopar-

ticles is shown in Figure 5. In this exam-

ple, the concentration of the analyte was

208 ng/L and was converted to volume

and then into size knowing the density

and the geometry of the particle using

the Syngistix Nano Application Module,

with no further need for manual data

processing.

It is also important to emphasize that

even after filtration of surface waters

at 0.2 µm, the NTA method, which is

commonly used for this type of analysis,

showed the presence of quite significant

amounts of colloidal particles with an

average diameter of ~110 nm. Therefore,

the addition of metal nanoparticles to

this complex matrix would make their

detection and characterization very

difficult, if not impossible, with the

commonly used techniques previously

mentioned, such as DLS and TEM.

Furthermore, even the determination of

the dissolved fraction that is usually per-

formed by ultrafiltration may be inad-

equate because silver ions may adsorb on

the surface of the colloids and, therefore,

be retained by the filtration membrane.

Consequently, the proportion of dis-

solved metal will be underestimated. SP-

ICP-MS measurements were found to be

more effective and to have fewer limita-

tions than other techniques. Indeed, the

Wastewatertreatment

plant

Effuent

Homoaggregation

Heteroaggregation

Transformation

Incorporation

Degradation

DegradationDegradation

Photodegradation

NOM stabilization

NOMfocculation

Homo-aggregation

pH

Ionicstrength

Cations

Sedimentation

Homoaggregation

Straining

Hetero-aggregationAttachment/

heteroaggregation

Dissolution

DissolutionDissolution

Mn+

Mn+

Mn+

Surface runoff

Solids/biosolids

Figure 6: Representation of a wastewater treatment plant, showing the possible pathways that could introduce nanomaterials into the environment. Adapted with permission from reference 1.

Table III: Result for particle size and concentration in the effluent and mixed liquor wastewater samples

Observation Number

Mean Particle Size (nm)

Spiked Particle Concentration (particles/mL)

Measured Particle Concentration (particles/mL)

Effuent wastewater

66.3 50,000 54,691

Mixed liquor wastewater

63.7 50,000 53,123



presence of other insoluble particles did

not interfere with the analysis of sil-

ver nanoparticles, as the signal of Ag

is recorded independently of the other

constituent elements of the colloids.

The same group of researchers found

that the SP-ICP-MS technique allowed

the effective and selective measurement

of changing particle size, aggregation,

and dissolution over time at low con-

centrations. In fact, they concluded that

SP-ICP-MS is probably the only suitable

technique that can provide such informa-

tion on the fate of metal nanoparticles at

very low concentrations typically found

in environmental waters (15).

Wastewater Samples

Another common, more complex matrix

that must be evaluated for the determi-

nation of Ag ENPs is wastewater samples

from wastewater treatment plants. The

complexity and variety of the wastewa-

ter matrices can make the analysis of

ENPs extremely challenging. Figure 6

is a representation of a wastewater treat-

ment plant that shows the possible path-

ways that could introduce nanomaterials

into the environment and the potential

impact they might have on the surround-

ing land and water supplies (1).

A recently published study by Azodi

and colleagues (13) evaluated the ability

of SP-ICP-MS to characterize Ag ENPs

in three common wastewater matrices:

mixed liquor, eff luent, and alginate

solutions. The wastewater samples were

collected from a wastewater treatment

plant near Montreal, Quebec, Canada.

The eff luent wastewater was collected

after the secondary settling tank, while

the mixed liquor was collected after the

secondary aeration tank. The eff luent

wastewater is the final treated wastewater

that is discharged to the river from this

treatment plant, while the mixed liquor

is the wastewater that leaves the aeration

tank after biological treatment to settle

the suspended solids in the settling tank.

As a result, the mixed liquor has much

higher levels of suspended solids and a

relatively higher dissolved carbon con-

tent compared to the effluent wastewater.

Alginate, more commonly known as

alginic acid, is an anionic polysaccharide

distributed widely in the cell walls of

brown algae, which is present at parts-

per-million levels in wastewaters and

comprises the dissolved organic carbon

fraction of wastewaters. The alginate solu-

tion was used as a known control–surro-

gate for comparison with the wastewater

samples. All solutions were prepared using

the alginic acid sodium salt from brown

algae (at 6 ppm) in deionized water by

shaking end-over-end for 1 h. Ag ENPs

capped with polyvinylpyrrolidone (PVP)

with a mean diameter of 67.8 ± 7.6 nm (as

determined with TEM) were spiked into

10 mL of all samples at a concentration of

0

0 20 40 60 80 100 120

0 20 40 60 80 100 120

0 20 40 60 80 100 120

400

800

1200

1600

2000

0

400

800

1200

1600

2000

0

400

800

1200

1600

2000

Pa

rtic

le c

on

cen

tra

tio

n (

NP

/mL)

Pa

rtic

le c

on

cen

tra

tio

n (

NP

/mL)

Pa

rtic

le c

on

cen

tra

tio

n (

NP

/mL)

(a)

(b)

(c)

nAg-PVP alginate

Diameter (nm)

Diameter (nm)

Diameter (nm)

nAg-PVP effuent WW

nAg-PVP mixed liquor WW

Figure 7: (a) Measured Ag particle size distribution in a neat 6 ppm alginate solution; (b) measured Ag particle size distribution in effuent wastewater; (c) measured Ag particle size distribution in mixed liquor wastewater.



10 ppb (5 million particles/mL). The sam-

ples were then diluted 10–1000× with

deionized water and sonicated for 5 min

before analysis. All samples were prepared

in triplicate. To determine the accuracy of

SP-ICP-MS, the Ag ENPs were added to

deionized water at a concentration of 0.1

ppb (50,000 particles/mL). The SP-ICP-

MS measurement determined the mean

size of the Ag ENPs to be 63.2 nm (which

agrees with the TEM measurements), and

the concentration to be 53,758 particles/

mL, thus validating the accuracy of the

measurements.

Next, the Ag ENPs were measured

in a 6 ppm alginate solution. Once the

accuracy of the technique was ensured,

it was followed by the measurement of

the effluent wastewater and mixed liquor

samples. First, the total Ag concentra-

tion was measured in both wastewater

samples and was found to range from 25

to 40 ppt, levels that should not inhibit

the determination of Ag ENPs. Figure

7a shows the Ag particle size distribu-

tion for 0.1 ppb (50,000 particles/mL) in

the alginate sample, which corresponds

to a mean particle size of 66.1 nm, with

a concentration of 52,302 particles/mL.

The agreement between the measured

and TEM-determined particle sizes

indicates that the alginate matrix does

not affect the measurement accuracy as

exemplified in Figures 7b and 7c, which

show the measured particle size distribu-

tions for the effluent and mixed liquor

respectively. Table III shows the mea-

sured particle sizes of both samples that

agree with the certificate value, together

with particle concentrations, which are

close to the calculated value, indicating

that neither of the wastewater matrices

impact the measurement and strongly

suggesting that Ag ENPs can be success-

fully measured in wastewater samples.

Detection Capability

The detection limits for both the Ag

particle size and concentrations in the

wastewaters were also determined. For

determining the particle size detection

limits, the diluted samples were analyzed

without any Ag ENPs being added. The

detection limit was determined by run-

ning the unspiked wastewater matrices

and observing the particle size, which

corresponded to the smallest recorded

peak. For the effluent, the detection limit

was about 18 nm, while for the mixed

liquor, it was about 12 nm. To determine

the lowest concentration of Ag ENPs that

could be detected, Ag ENPs were spiked

into deionized water and diluted multiple

times (Note: Since the wastewater matri-

ces were shown not to affect the results,

the detection limit was only measured in

deionized water). The measured particle

concentration was then recorded for each

concentration. The particle concentration

detection limit was determined to be the

spiked particle concentration where the

measured concentration did not change

when the sample was diluted. In this work,

the Ag ENP particle concentration detec-

tion limit was determined to be 25 ppt

(12,500 particles/mL).

Final Thoughts

SP-ICP-MS demonstrates very excit-

ing potential for the characterization of

nanoparticles in many varied types of

environmental samples. It is a very effec-

tive tool to assess the efficiency of drink-

ing water treatment systems in removing

certain nanoparticles, without the need

for any additional techniques. It has

also allowed the effective and selective

measurement of changing particle size,

aggregation, and dissolution over time

at low concentrations in natural surface

waters. Additionally, the technique has

shown the versatility to measure and

analyze nanoparticles in various matrices

found in a wastewater treatment plant.

There is no question that compared to

other traditional analytical methods,

SP-ICP-MS offers unique capabilities in

tracking the fate and behavior of metal

nanoparticles at extremely low levels in

the environment. Although this study

has focused on the effectiveness of the

technique for the characterization of

nanoparticles likely to be present in

environmental water samples, it is also

applicable to other types of metal and

metal oxide nanoparticles in a variety

of complex environmentally significant

matrices including plant material (16)

and biological tissue (17).

References

(1) G.E. Batley, J.K. Kirby, and M.J. McLaugh-

lin, Acc. Chem. Res. 46(3), 854–862 (2013).

(2) The Project on Emerging Nanotechnolo-

gies, “An Inventory of Nanotechnology-

based Consumer Products Introduced on

the Market,” available at: http://www.nano-

techproject.org/cpi/.

(3) J.W. Olesik and P.J. Gray, J. Anal. At. Spec-

trom. 27, 1143 (2012).

(4) D.M. Mitrano, A. Barber, A. Bednar, P.

Westerhoff, C.P. Higgins, and J.F. Ranville,

J. Anal. At. Spectrom. 27, 1131–1142 (2012).

(5) D.M. Mitrano, E.K. Leshner, A. Bednar, J.

Monserud, C.P. Higgins, and J.F. Ranville,

Environ. Toxicol. Chem. 31(1), 115–121 (2012).

(6) F. Laborda et al., J. Anal. At. Spectrom.

26(7), 1362–1371 (2011).

(7) A. Hineman and C. Stephan, J. Anal. At.

Spectrom 29, 1252–1257 (2014).

(8) A .C. Degueldre and P.Y. Favarger, Physico-

chem. Eng. Aspects 217, 137–142 (2003).

(9) C. Degueldre and P.Y. Favarger, Colloids

and Surfaces A: Physicochem. Eng. Aspects,

217, 137–142 (2003).

(10) H.E. Pace, J. Rogers, C. Jarolimek, V.A.

Coleman, C.P. Higgins, and J.F. Ranville,

Anal. Chem. 83(24), 9361–9369 (2011).

(11) A.R. Donovan, H. Shi, C. Adams, and C.

Stephan, PerkinElmer Application Note,

“Rapid Analysis of Silver, Gold, and Tita-

nium Dioxide Nanoparticles in Drinking

Water by Single Particle ICP-MS” (2015).

(12) M. Hadioui, K. Wilkinson, and C. Stephan,

PerkinElmer Application Note, “Assessing

the Fate of Silver Nanoparticles in Sur-

face Waters Using Single Particle ICP-MS”

(2014).

(13) M. Azodi, S. Ghoshal, and C. Stephan,

PerkinElmer Application Note, “Measure-

ment and Analysis of Silver Nanoparticles

in Wastewaters with Single Particle ICP-

MS” (2015).

(14) PerkinElmer Application Study, “Single

Particle ICP-MS Syngistix Nano Applica-

tion Module,” http://www.perkinelmer.

com/catalog/product/id/N8140309.

(15) M. Hadioui, S. Leclerc, and K.J. Wilkinson,

Talanta 105, 15–19 (2013).

(16) Y. Dan, W. Zhang, X. Ma, H. Shi, and C.

Stephan, PerkinElmer Application Note,

“Gold Nanoparticle Uptake of Tomato

Plants Characterized by Single Particle ICP-

MS” (2015).

(17) E.P. Gray et. al., Environ. Sci. Technol.

47(24), 14315−14323 (2013).

Chady Stephan is the manager of global applications of nanotechnology at PerkinElmer, Inc., in Woodbridge, Ontario, Canada. Robert Thomas is the principal of his own freelance writing and scientific consulting company, Sci-entific Solutions, based in Gaithersburg, Mary-land. Direct correspondence to: [email protected] ◾



Sascha Usenko, Zach C. Winfield, Stephen J. Trumble, and Nadine Lysiak

GC–MS and UHPLC–MS-MS Analysis of Organic Contaminants and Hormones in Whale Earwax Using Selective Pressurized Liquid Extraction

Here we highlight some of the opportunities associated with

combining advanced sample preparation techniques with state-

of-the-art chemical analysis techniques. This article considers the

unique combination of selective pressurized liquid extraction (SPLE)

with gas chromatography (GC) coupled with mass spectrometry

(MS) and ultrahigh-pressure liquid chromatography coupled with

tandem MS (UHPLC–MS-MS). We use this powerful combination to

develop a novel analytical technique capable of measuring hormones

and organic contaminants in whale earwax plugs. We explore the

analytical challenges with such combinations and the advantages of

focusing both on sample preparation as well as chemical analysis.

Sample preparation is a critical step

in the analysis of organic com-

pounds in solid and semisolid

matrices and often involves multiple

labor- and time-intensive steps, which

in turn propagate uncertainty. Over the

past decade, the automated extraction

technique pressurized liquid extraction

(PLE) has made significant advances to

sample preparation as compared to clas-

sic techniques such as Soxhlet extraction.

PLE is considered an exhaustive extrac-

tion technique suitable for a wide range

of solid and semisolid matrices including

tissues, soils, sediments, and particulate

matter, as well as consumer products. To

improve the extraction efficiency, PLE

utilizes both high pressure (1500 psi) and

high temperature (30–200 °C). Elevated

temperatures serve to increase the solubil-

ity of the target analytes through acceler-

ated extraction kinetics. Increasing the

pressure helps ensure that the extraction

solvent is below its boiling point. PLE

typically uses between 20 and 100 mL

of extraction solvent (depending on the

size of the extraction cell) with extraction

times of 15–30 min (depending on the

static time and number of cycles). The

reduction of extraction time and solvent

are significant improvements compared

to techniques like Soxhlet extraction and

serve to increase analytical throughput.

PLE methods are optimized for specific

analytes (typically with similar physi-

cal and chemical properties) through the

assessment of solvent, temperature, extrac-

tion time, percent flush volume, number

of cycles, and mass of matrix. During the

extraction, target analytes and potential

interference are extracted and collected

together in a collection bottle. As a result,

various cleanup strategies, such as packed

chromatographic columns or gel-perme-

ation chromatography, have been used to

help remove potential interferences pres-

ent in the extract. Complex matrices, such

as biological tissues, often require multiple

cleanup steps including the use of multiple

packed chromatographic columns. These

additional steps increase the time, training,

space, and cost associated with the chemi-

cal analysis. Analytical trade-offs arise as

matrix complexity and the number of tar-

get analytes increase.

Recently, PLE techniques have

integrated various cleanup strategies

within the extraction cell and are often


OctOber 2015 AdvAncing EnvironmEntAl AnAlysis 41www.chromatographyonline.com

described as selective PLE (SPLE) or in-

cell cleanup. SPLE is accomplished by

layering commercially available adsor-

bents below the sample homogenate

within the extraction cell. A wide range

of commercially available adsorbents

have been successfully incorporated

within the extraction cell such as silica

gel (including silica gel modified with

sulfuric acid and potassium hydroxide),

alumina, Florisil, carbon, diatomaceous

earth, and C18. Subedi and colleagues

(1) provided a detailed review of SPLE

applications for organic contaminants.

The incorporation of cleanup adsorbents

within the extraction has been shown to

eliminate some or all of the subsequent

cleanup steps. The reduction of the num-

ber of analytical steps is advantageous on

many levels including a reduction of train-

ing, space, and cost of consumables, as

well as an increase in the overall analytical

throughput and efficiency (that is, a reduc-

tion in analytical bottlenecks). These high-

throughput SPLE methods also reduce or

minimize the risk of sample contamina-

tion associated with sample preparation

steps. Sample contamination is a major

concern when measuring trace-level con-

centrations of organic contaminants in

rare or irreplaceable samples.

Marine mammals are often considered

ecosystem sentinels because their survival

is contingent on the health and func-

tioning of marine ecosystems (2). Baleen

whales (suborder Mysticeti) are unique sen-

tinels because of their low trophic position

and extensive geographic range, meaning

that they are especially vulnerable to envi-

ronmental perturbation. The bowhead

whale (Balaena mysticetus), an Arctic spe-

cies, is a sentinel in a habitat under rapid

transition. As the extent and quality of

sea ice decreases, this habitat is experienc-

ing burgeoning impacts from oil and gas

exploration, shipping activity, ocean noise,

persistent contaminants, and commer-

cial fishing—in addition to the ongoing

ecological effects of climate change (3–5).

Understanding and quantifying the expo-

sure and effects of these stressors, such as

persistent organic pollutants (POPs), on

the bowhead whale is paramount to their

conservation and management.

Baleen whale earplugs are waxy struc-

tures that form annual or semiannual lay-

ers or lamina, and have classically been

used in aging techniques (6,7). This waxy

matrix consists of both a high lipid content

(light layer) and keratinized epithelium

cells (dark layer) (8). Recently, an SPLE

technique was developed to measure a

wide range of POPs in whale earwax (that

is, cerumen [9]). One of the major advan-

tages was the complete elimination of

postextraction cleanup steps, which was

accomplished by combining the necessary

cleanup steps in the extraction step (9). In

addition, enzyme-linked immunosorbent

assay (ELISA) techniques have been devel-

oped to measure hormones in whale ceru-

men (10). It is important to note that each

immunoassay kit is only capable of mea-

suring a single hormone and that the anal-

ysis of multiple hormones would require

significant sample mass, which may elimi-

nate additional chemical measurements.

POPs and hormone profiles were recon-

structed for an individual whale with an

estimated 6-month resolution, using the

above mentioned methods to measure

POPs and hormone concentrations in indi-

vidual lamina (10). As a result, cerumen,

and thereby earplugs, have the capability

to record and archive chemical signals

(both natural and anthropogenic). This

technique is similar to chemical recon-

struction techniques used in sediment or

ice cores (11). This technique provides an

unprecedented glimpse at a whale’s life-

time reproductive history, stress response,

and contaminant exposure. This approach

provides more accurate estimates of repro-

ductive rate and age at sexual maturity

than traditional methods and also yields

baseline information regarding stress and

contaminant exposure. One of the major

issues associated with reconstructing

chemical profiles using whale cerumen

is the limited sample mass. Typically, the

sample mass available for chemical analy-

sis depends on the size of the earplug and,

therefore, the age and species of the whale,

but often ranges between 0.5 to 1.5 g/lam-

ina. This limited sample mass reduces the

overall number of chemicals or chemical

classes that can be examined using a single

earwax plug.

The objective of this study is to expand

on the SPLE method (9) to also include

hormones, while preserving its ability to

measure a wide range of POPs (Figure

1). Expanding the SPLE method would

help maximize the number and type of

analytes that can be reconstructed from

a single whale earplug. Here, POPs are

extracted using SPLE and extracts are

analyzed using gas chromatography–mass

spectrometry (GC–MS) (9). Hormones

are eluted during a secondary extraction

and those extracts are analyzed using

ultrahigh-pressure liquid chromatography–

tandem mass spectrometry (UHPLC–

MS-MS). Liquid chromatography (LC)

offers many advantages compared to

traditional hormone analysis with immu-

noassay because smaller sample volumes

can be used, multiple compounds can be

measured from a single sample, and issues

with cross reactivity are avoided (12). Hor-

mones, such as testosterone, are typically

more polar than the current list of POPs

measured in whale earwax and offer a very

unique set of analytical challenges.

Analytical Challenges and Approach

This line of research has two main analyti-

cal challenges: First, developing an analyti-

cal technique capable of measuring part-

per-billion concentrations of biologically

Isotopically labeled surrogates

Cerumen (~0.25 g) andsodium sulfate (~10 g)

homogenate

Basic alumina (~5 g)

Silica gel (~15 g)

Florisil (~10 g)

Hormones LC–MS-MS

Extraction cell

POPsGC–MS

TB

D

Dich

loro

me

tha

ne

–h

exa

ne

First extraction

Second extraction

Figure 1: Modifed schematic of the fnalized SPLE technique capable of measuring POPs in whale cerumen with a secondary follow-up extraction. Adapted from reference 9.



relevant hormones in whale cerumen using UHPLC–MS-MS, and second, develop-ing an SPLE technique that is capable of extracting contaminants as well as hor-mones from a single sample of whale ceru-men without any additional cleanup steps.

To optimize the SPLE technique, we need to maximize the extraction effi-ciency of the analytes from the matrix, in this case cerumen. To do so, we must simultaneously preserve the retention of our biological matrix compounds while extracting both organic contaminants and hormones. We believe we can extract both classes of compounds with the first extrac-tion, but retain the hormones in the adsor-bents along with the matrix itself. To do so, we must use adsorbents capable of retain-ing the lipid-rich matrix and hormones. Subsequently, the adsorbents should retain the bulk matrix while allowing the elution of the hormones with a secondary solvent extraction. A variety of solvents will be examined to produce the desired results.

Experimental

Chemicals and Materials

Unlabeled progesterone, estradiol, testos-terone, cortisol, and labeled D5-testoster-one and D4-cortisol with a purity of ≥98% were purchased from Cambridge Isotope Laboratories. All supplies were stored according to the manufacturer’s recom-mendations. Alumina (basic, activated, Brockmann I), and Florisil (60–100 mesh) were purchased from Sigma Aldrich. Silica gel (pore size 60 Å, 70–230 mesh) was purchased from BDH Chemicals. Concentrated formic acid and UHPLC-grade acetonitrile, ≥99.9% purity, were purchased from Fisher Scientific. For the UHPLC separations, a 100 mm × 2.1 mm, 1.7-µm dp Acquity UPLC BEH C18 column and a 5 mm × 2.1 mm, 1.7-µm dp UPLC BEH C18 VanGuard precolumn were purchased from Waters. Solvents and

mobile phase were prepared using purified water (Thermo Barnstead Nanopure Dia-mond UV water purification, 18 MΩ).

SPLE for Organic

Contaminants in Whale Earwax

The SPLE technique capable of measur-ing contaminants (including pesticides, polychlorinated biphenyls, and polybromi-nated diphenyl ethers) in whale cerumen has been described previously (9). Briefly, aliquots of whale cerumen (∼0.25 g) were homogenized with anhydrous sodium sulfate and placed on precleaned adsor-bents within the extraction cell (from top to bottom; 5 g of basic alumina, 15 g of silica gel, and 10 g of Florisil). All extrac-tions were performed using an accelerated solvent extractor (ASE; ASE 350, Dionex [now part of Thermo Fisher Scientific]). Homogenates were extracted under the following extraction conditions with 1:1 dichloromethane–hexane, 100 °C, 1500 psi, 2 cycles (2 min each), and 150% rinse volume. Homogenates were spiked with isotopically labeled standards to correct for variability in analyte loss during sample preparation before SPLE. Extracts were spiked with a secondary set of isotopi-cally labeled standards and concentrated to ~300 mL before GC–MS with electron ionization and GC–MS with electron cap-ture negative ionization analysis.

Analysis of Hormones

in Whale Earwax

Final Analytical Approach

Hormone separation and analysis were performed using a Waters Acquity UPLC system and a Waters Xevo ESI/TQ-S elec-trospray ionization tandem MS system. Acetonitrile and water, with 0.1% formic acid, were selected along with a 100 mm × 2.1 mm, 1.7-µm dp Acquity UPLC BEH C18 column with a 5-mm guard column with identical packing material

and diameter. All samples were dried and reconstituted in 95:5 (v/v) water–aceto-nitrile with 0.1% formic acid following extraction. The column was initially equilibrated at 95% water (mobile-phase A) and 5% acetonitrile (mobile-phase B), for 30 min with a 0.5-mL/min flow rate. A quantitation method, using area, was established to monitor the following reac-tions (see Table I).

Before analysis, the UHPLC system was flushed with 15 injections of water and 15 injections of acetonitrile at 0.7 mL/min with a 50% volume of 0.1% formic acid in water (mobile-phase A) and 0.1% formic acid in acetonitrile (mobile-phase B) to remove any waste from previous use. After it was cleaned, the column was loaded onto the instrument and conditioned at 95% A and 5% B for 30 min at 0.5 mL/min at 35 °C. A target analyte calibration was performed with five points ranging from 2 ppb to 100 ppb using an isotopi-cally labeled internal standard, then three blanks of 0.1% formic acid in water. The separation was programmed to begin at 5% B and increase to 40% B at 0.5 mL/min after 0.5 min and then gradually increase to 60% B for 4.5 min. The col-umn was returned to initial conditions for 3.5 min before the separation was repeated. After the batch was complete, the column was flushed at initial conditions for 30 min and then flushed for 10 min in 95% B for storage.

SPLE Optimization for

Hormones in Whale Earwax

Typically, to optimize an SPLE method, a wide range of adsorbents, adsorbent masses, and combinations of adsorbents would be examined. However, because we are expanding on a previous SPLE method developed for the extraction and analysis of organic contaminants in whale earwax, the SPLE adsorbents have already been selected (adsorbents [basic alumina, silica gel, and Florisil]). At this juncture, we shift our focus to the hor-mones and acknowledge that hormones may be extracted from the whale earwax during the first extraction (extraction of contaminants using 1:1 dichlorometh-ane–hexane) but retained on one or more of the adsorbents. In fact, our goal would be to extract both the contaminants and hormones during the first extraction step (dichloromethane–hexane) but retain the

Table I: Target analyte multiple reaction monitoring conditions optimized for electrospray ionization source

CompoundPrecursor (m/z)

Product (m/z)

Dwell (s)Cone (V)

Collision (V)

Retention Windows

Ion Mode

Estradiol 273.2 107.0 0.016 10 20 1.65 ± 0.26 POS

Testosterone 289.2 109.1 0.016 20 20 2.65 ± 0.27 POS

Progesterone 315.1 109.3 0.016 28 20 4.66 ± 0.24 POS

Cortisol 363.1 121.1 0.016 20 20 2.46 ± 0.24 POS


OctOber 2015 AdvAncing EnvironmEntAl AnAlysis 43www.chromatographyonline.com

hormones for a secondary extraction on

the adsorbents. In this ideal situation,

the secondary extraction would elute the

hormones from the adsorbents but pre-

serve the retention of matrix compounds.

The efficiency of extractions and adsor-

bents (retention of target analytes) were

examined using target analyte percent

recoveries and sample cleanliness (for

example, effectiveness of adsorbents in

retainment of interferences).

The first step in optimizing the SPLE

method for hormone analysis is to better

understand the extraction of hormones

from whale earwax using the prescribed

SPLE adsorbents and solvent (dichloro-

methane–hexane; see below). This step

is hindered such that specific hormones

may be extracted from the earwax, but

retained by one or more adsorbents. Hor-

mones measured in the first extraction

(dichloromethane–hexane) with high

percent recoveries (>50%) would not be

sufficiently retained in the extraction

cell. Hormones with nondetects or very

low recoveries (<10%) are assumed to be

extracted, but retained by one or more

adsorbent during the first extraction

(dichloromethane–hexane). Past this

point, a series of experiments must be

designed to identify which hormones are

retained by which adsorbents and which

solvents or combinations of solvents are

necessary to elute the hormones from the

adsorbents.

The extraction efficiency of multiple

solvents and combinations of solvents

were investigated as a potential second

round extraction solvent (focusing on

hormone elution). Dichloromethane,

ethyl acetate, toluene, 1:1 dichlorometh-

ane–ethyl acetate, 2:1 ethyl acetate–

dichloromethane, and cyclohexane were

selected and examined based on litera-

ture information and polarity. Extrac-

tions were performed using a 100-mL

cell containing 15 g of Florisil, 22.5 g of

silica gel, and 7.5 g of basic alumina. All

cells were conditioned (precleaned) using

dichloromethane–hexane. Precleaning

conditions were consistent throughout

the experiments unless noted otherwise.

Adsorbents were precleaned with a 1:1

dichloromethane–hexane at 100 °C,

1500 psi, four cycles each with 2-min

static times, a 60-s purge, and 50% flush

volume. After precleaning, the cells were

spiked with a solution of target analytes

and left to equilibrate for 1 h at room

temperature.

The first extraction consisted of

dichloromethane–hexane as previously

described. The PLE conditions for each

extraction were constant at 100 °C, 1500

psi, two cycles each with 5-min static

times, a 100-s purge, and 100% flush

volume.

A following secondary extraction

examined the extraction efficiency of

different solvents (see above) and their

ability to elute hormones retained on

the adsorbents. In some specific cases, a

third extraction was also examined and

focused on very polar solvents includ-

ing ethyl acetate and acetone. The sec-

ond and third extractions used the PLE

parameters described above and only var-

ied by the solvent being examined. After

the initial extraction of dichlorometh-

ane–hexane, all subsequent extractions

were collected and analyzed separately.

The samples were concentrated to ~1 mL

using a Turbo Vap II evaporator (Biotage),

then transferred to a GC vial and blown

to dryness using compressed nitrogen

with a fine blow-down peripheral. After

drying, the sample was reconstituted in

475 mL of mobile phase—0.1% formic

acid and 5% acetonitrile in water—then

spiked with 25 mL of isotopically labeled

internal standard before analysis using

UHPLC–MS-MS.

Target analyte affinity for individual

adsorbents was examined with target ana-

lyte spike and recover experiments (n = 1).

Next, 10 g of each adsorbent were added

to a 33-mL PLE cell. After condition-

ing (with dichloromethane–hexane; see

above), each cell was spiked with a solu-

tion of target analytes as described above.

Hormones were spiked on individual

adsorbents packed into the extraction cell

and were subsequently extracted using a

range of extraction solvents. The extracts

were then concentrated and reconstituted

in the mobile phase as described above.

Target analyte recoveries were also

investigated with whale cerumen (0.25 g).

A homogenate of multiple cerumen lam-

ina created from a female bowhead whale

(estimated at 65 years of age). A 0.25-g

aliquot of the cerumen homogenate

was homogenized with sodium sulfate

(~10 g) and spiked in the extraction cell

%

100

Testosterone

RT: 2.89

289.16>109.07

7.31E6

Estradiol

RT: 2.39

273.16>107.04

2.33E5

(a)

(b)

0

%

100

0

0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

Figure 2: Chromatograms of (a) testosterone and (b) estradiol extracted from a sam-ple containing 0.25 g of earwax spiked with target analytes.



with target analyte. Cerumen samples,

which provided a source of matrix inter-

ferences, were used to examine the matrix

effects on 2:1 ethyl acetate–dichloro-

methane as the secondary extraction

solvent. Extracts containing cerumen

required a filter step to help remove

precipitation, which appeared during

blowdown. Filtering occurred after the

extract was blown down to dryness and

reconstituted in mobile phase (475 mL).

A 13-mm Acrodisc CR filter with a 0.2-

µm PTFE membrane (Pall Life Sciences)

was precleaned with 500 mL of purified

water. The samples were then drawn into

a syringe and extruded through the filter

into a clean GC vial.

A third extraction was performed on

two 66-mL cells, one with and one with-

out earwax, both contained all three

precleaned adsorbents. An extraction

of ethyl acetate–dichloromethane (2:1)

was performed on both cells. Once the

extraction was complete the cells were

allowed to cool to return to room tem-

perature. The cell that did not contain

any cerumen was extracted with ethyl

acetate and subsequently with acetone.

The cell cap of the cell containing wax

was removed and the PLE extraction

filter insertion tool was used to push

the cellulose filter below the adsorbents

upward. The layer of sodium sulfate and

wax homogenate was scraped off care-

fully to leave behind the majority of

basic alumina. After the homogenate

was removed the sample was extracted

with ethyl acetate followed by acetone.


We began this novel method develop-

ment with four hormones: cortisol,

estradiol, progesterone, and testosterone.

Hormone retention of each adsorbent

was examined by placing 10 g of each

adsorbent into a 33-mL extraction cell.

The cells were conditioned with dichlo-

romethane–hexane and then extracted

with a range of different solvents.

Analysis revealed that cortisol was well

retained by all three adsorbents and pre-

liminary results suggest that no solvent

or combination was capable of eluting

cortisol. Solvents and solvent combina-

tions examined including 1:1 dichloro-

methane–ethyl acetate, 2:1 ethyl acetate–

dichloromethane, ethyl acetate, toluene,

ethyl acetate–toluene, and cyclohexane.

The 2:1 ethyl acetate–dichloromethane

solvent was selected over other solvents

based on its ability to elute estradiol, pro-

gesterone, and testosterone. Toluene was

capable of eluting select hormones, but

required extensive blow-down time (3×

as compared to 2:1 ethyl acetate–dichlo-

romethane). Again, all solvent and sol-

vent combinations were unable to elute

cortisol off the adsorbents.

Adsorbent experiments suggest that

testosterone retention was dominated by

silica gel. Spike and recovery experiments

performed with all three adsorbents

using 2:1 ethyl acetate–dichlorometh-

ane provided greater than 50% recovery.

Percent recoveries were calculated by

dividing the measured concentrations

in the sample extract by expected con-

centrations and multiplying the quotient

by 100%. A second experiment was per-

formed in which cerumen (0.25 g ceru-

men homogenized with sodium sulfate)

was loaded into a precleaned 66-mL

cell and spiked with target analytes and

left to equilibrate for 1 h. After it was

extracted with 2:1 ethyl acetate–dichlo-

romethane, the wax and sodium sulfate

homogenate were extruded from the top

of the cell and the three adsorbents were

extracted with ethyl acetate. The ethyl

acetate extraction provided a greater than

40% recovery of testosterone (Figure 2a).

Adsorbent experiments suggest that

estradiol retainment was dominated by

Florisil. Spike and recovery experiments

(without cerumen) performed with all

three adsorbents using 2:1 ethyl acetate–

dichloromethane provided greater than

80% recovery. Spike and recovery experi-

ments (with cerumen) performed with all

three adsorbents using 2:1 ethyl acetate–

dichloromethane provided greater than

55% recovery. Again, a third extraction

using ethyl acetate was performed after

removing the sodium sulfate and wax

homogenate. The ethyl acetate extraction

provided a greater than 160% recovery of

estradiol (Figure 2b). The higher estradiol

recoveries were most likely caused by the

native estradiol present in the female whale.

Conclusion

Preliminary data suggest that hormones

can be measured in whale earwax using

an SPLE technique with a combina-

tion of extractions. Further parameters

will be examined to improve the overall

extraction efficiency, including the per-

cent of flush volume, number of cycles,

and static time. Future analysis will

also include isotopically labeled target

analytes to serve as surrogate standard.

Surrogates will help correct for target

analyte lose and variability in sample

preparation.

References

(1) B. Subedi, L. Aguilar, E. Robinson, K.

Hageman, E. Björklund, R. Sheesley, and

S. Usenko, TrAC, Trends Anal. Chem. 68,

119–132 (2015).

(2) S.E. Moore, J. Mammal. 89, 534–540

(2008).

(3) C. Granier, U. Niemeier, J.H. Jungclaus, L.

Emmons, P. Hess, J.-F. Lamarque, S. Wal-

ters, and G.P. Brasseur, Geophys. Res. Lett.

33(13), L13807 (2006).

(4) G.C. Hays, A.J. Richardson, and C. Rob-

inson, Trends in Ecology & Evolution 20,

337–344 (2005).

(5) D.K. Perovich and J.A. Richter-Menge,

Annual Review of Marine Science 1, 417–441

(2009).

(6) C.M. Gabriele, C. Lockyer, J.M. Straley,

C.M. Jurasz, and H. Kato, Mar. Mammal

Sci. 26, 443–450 (2010).

(7) A. Jonsgard in The Biology of Marine Mam-

mals, H.T. Andersen, Ed. (Academic Press,

New York, 1969), pp. 1–30.

(8) P.E. Purves, Discovery Reports 27, 293–302

(1955).

(9) E.M. Robinson, S.J. Trumble, B. Subedi, R.

Sanders, and S. Usenko, J. Chromatogr. A

1319, 14–20 (2013).

(10) S.J. Trumble, E.M. Robinson, M. Berman-

Kowalewski, C.W. Potter, and S. Usenko,

Proc. Natl. Acad. Sci. U.S.A. 110, 16922–

16926 (2013).

(11) S. Usenko, D.H. Landers, P.G. Appleby,

and S.L. Simonich, Environ. Sci. Technol.

41, 7235–7241 (2007).

(12) C.J. Hogg, E.R. Vickers, and T.L. Rogers, J.

Chromatogr. B: Anal. Technol. Biomed. Life

Sci. 814, 339–346 (2005).

Sascha Usenko is with the Department

of Chemistry and Biochemistry and the

Department of Environmental Science at

Baylor University in Waco, Texas. Zach C. Winfield is with the Department

of Chemistry and Biochemistry at Baylor

University. Stephen J. Trumble is

with the Department of Biology at Baylor

University. Nadine Lysiak is with the

Department of Environmental Science at

Baylor University. Direct correspondence

to: [email protected] ◾



Kevin A. Schug, Doug D. Carlton Jr., and Zacariah L. Hildenbrand

Analytical Efforts Toward Monitoring Groundwater in Regions of Unconventional Oil and Gas Exploration

Gas chromatography (GC), inductively coupled plasma–mass

spectrometry (ICP-MS), ICP–optical emission spectrometry (OES),

and other bulk analysis methods are applied to groundwater in

proximity to unconventional oil and natural gas extraction activities.

The United States has experi-

enced a dramatic shift in eco-

nomic inf luence over the past 10

years with the widespread engineering

advances that have allowed unconven-

tional oil and gas (UOG) extraction to

become more efficient and cost-effec-

tive. Small rural towns have become

industry hubs overnight as a result of

the hydrocarbons trapped beneath the

ground. Educators have increased the

number of engineering and technical

programs available to students in an

effort to meet the demand for a quali-

fied workforce. Stories of multigenera-

tion ranchers becoming millionaires

overnight through the leasing of their

land and mineral rights puts the televi-

sion show The Beverly Hillbillies in a

more current light.

Regulations related to UOG activity

are currently left to each state, which

creates disparities in environmen-

tal testing and monitoring across the

United States. For example, Colorado

and Illinois require baseline groundwa-

ter testing before drilling commences,

while Pennsylvania only suggests base-

line measurements in the event that a

dispute arises after drilling. The list

of organic compounds Colorado has

chosen to monitor through chromato-

graphic methods are total petroleum

hydrocarbons, benzene, toluene, ethyl

benzene, and xylenes (BTEX), polycy-

clic aromatic hydrocarbons (PAH) plus

benzo[a]pyrene, and dissolved gases.

These analytes have been the focus

of analyses for years. Their presence

in water is hypothesized to indicate

an adverse environmental interaction

with hydrocarbon extraction opera-

tions. However, their sources can still

be convoluted and may not be wholly

specific to UOG.

Within the past decade, a mix of ana-

lytical methods has been developed or

applied to establish an understanding of

the impact, if any, that UOG activity is

having on groundwater in the vicinity.

This article discusses chromatographic

methods applied for particular organic

compounds and considerations to assist

in method development. A section is

also dedicated to spectroscopic meth-

ods for detection and quantification of

metals and ions in water samples that

are relevant to UOG activity. Collec-

tions from our groundwater research

are highlighted in each section to dem-

onstrate the application.

Chromatography

for the UOG Field

The ideal situation for creating a

method would be to work with a

system of known knowns (1) or com-

pounds that are expected to be present



and are positively identified. Current

researchers must also have the capabili-

ties to work with unknown knowns or

the ability to determine unexpected

identifiable compounds, and unknown

unknowns, compounds unexpected and

without standards, Chemical Abstracts

Service (CAS) numbers, or absent in

databases. For example, these may

include proprietary polymers or surfac-

tants developed primarily for the UOG

field. A hesitation that may be encoun-

tered are known unknowns, which are

expected compounds not detected.

The internal debates are made up of

how confident the compound is to be

a “known;” is the concentration too

low to detect in the given sample, or

is the method inadequate? In the dis-

cussion to follow, there are very few

“knowns” to be expected when moni-

toring groundwater possibly impacted

by UOG activity.

Gas Chromatography

Gas chromatography (GC) methods

have been at the forefront for analysis

of organic compounds in groundwater

and UOG wastewater (UOGWW) (2).

While there is the potential for non-

volatile organic additives such as sur-

factants to be present, the majority of

hydraulic fracturing additives or shale

formation compounds of health or

environmental concern are GC amena-

ble. In a 2011 Congressional report, 24

organic hydraulic fracturing additives

are listed as “Chemical Components

of Concern,” of which 23 are GC ame-

nable without the need for derivatiza-

tion. Some of these include BTEX, die-

sel, and naphthalene, which have been

suggested for baseline measurements

by various states.

Numerous Environmental Protec-

tion Agency (EPA) and state regulatory

methods have been established using

GC for these and a multitude of other

compounds of concern over the past

50 years. While a mix of regulatory

methods can be found that include a

subset of these compounds, the lack of

a single dedicated standard approach

to effectively extract and separate a

probable list of compounds in ground-

water or UOGWW is a complicating

factor that has slowed research. Most

off icially standardized versions of

these methods are less capable of the

throughput needed to prepare and ana-

lyze a large number of samples in a lim-

ited timeframe.

Dissolved Gas Analysis

The earliest efforts to assess the impact

of UOG activity on groundwater was

through the measurement of dissolved

gases, specif ically methane, ethane,

propane, butane, and pentane (C1, C2,

C3, C4, and C5, respectively) in ground-

water from regions within close prox-

imity of UOG drilling sites (3). Meth-

ane is the most abundant component

of natural gas extracted for energy pur-

poses, with ethane and propane com-

prising the majority of the remaining

small fraction. The hypothesis is that

if there is a failure in the integrity of

the protective casing of the UOG well

(4,5) or if induced fractures in the shale

create interconnectivity with the over-

lying aquifer, the natural gas would be

the most abundant and mobile species

to detect in groundwater.

Two types of methane can be

measured in groundwater (6). The

most common type found in shallow

groundwater is biogenic methane, a by-

product of bacterial metabolism. Ther-

mogenic methane is the other type, the

primary target of UOG recovery. This

methane gas is formed by the presence

of decomposing organic matter under

high temperatures and pressures over a

long period of time (that is, from deep

geological formations). Because of the

different implications for each type of

natural gas, methane measured in shal-

low groundwater must include further

investigations to distinguish between

biogenic or thermogenic origins.

The origin of the measured methane

can be determined either through iso-

topic abundances of carbon-13 (13C),

deuterium (2H), or the methane to

ethane and propane ratio (7). A ratio

of methane to higher chain hydrocar-

bons of less than approximately 100

suggests thermogenic gas (3). Both of

these approaches have even been found

to not only identify thermogenic meth-

ane, but also distinguish between dif-

ferent natural gases produced in differ-

ent geological formations (3,8,9).

GC separations coupled with f lame

ionization detection (FID) are most

commonly used for the analysis of

these light hydrocarbons. Groups

measuring these light hydrocarbons

do so in a targeted manner, meaning

they are tailored specifically for C1–C5

gases and little else. These methods are

quite sensitive and selective, but ulti-

mately lack the ability to detect a wide

suite of unknown compounds. Sample

introduction is performed through

either purge-and-trap techniques (10),

where the water is purged with an inert

gas and volatiles are trapped on a selec-

tive sorbent, or using headspace analy-

sis (11), where the sample is heated

and agitated to liberate the gas to an

open headspace in the vial, which is

then sampled. Column selection for

this analysis leans toward the use of

porous layered open tubular (PLOT)

columns, primarily those with a divi-

nylbenzene phase (10,12). As alluded

0.E+00

1.E+05

2.E+05

3.E+05

4.E+05

5.E+05

6.E+05

7.E+05

8.E+05

9.E+05

Inte

nsi

ty (

arb

. u

nit

s)

0 2 4 6 8 10 12 14 16

Time (min)

C1

C2 C

3 C4

C5

Pentane

PLOT, GC–VUV

iso-C4

iso-C5

Capillary 5ms, GC–MS

Figure 1: GC chromatogram of natural gas separated on an HP PLOT Q column (blue) and Rxi-5ms column (orange). Pentane is identified in each chromatogram.



to earlier, PLOT phases possess a great

affinity for C1–C5 hydrocarbons, but

that affinity is further extrapolated to

the C6–C8 linear and branched alkanes

and aromatics, which are also present

in natural gas, making analysis of these

larger hydrocarbons inefficient because

of long retention and excessive band

broadening. An example of this affin-

ity is demonstrated in Figure 1 with

a natural gas standard separated on a

PLOT divinylbenzene column and a

5% diphenyl capillary column.

Intricate sample collection fur-

ther complicates this specific analysis,

which typically requires additional

measurements, such as isotopic analy-

sis, for results that can enable sourcing

of the natural gas in the water (that

is, a comparison of the natural gas

isotope signature in the water with

that from the targeted shale or other

sources). Groundwater samples are

typically collected from volunteers’

water wells, in which the withdraw

rate and consistency will be variable

across the population. Agitation, along

with the pressure differential on the

water once it reaches the surface, can

cause the water to degas and skew

dissolved gas measurements to below

their actual values. For better control

during sampling, it is recommended

to use a nongas permeable tube with

a valve connected to the water well

head that f lows at a constant rate into

an evacuated bladder (for example, an

IsoFlask sampling bladder [Isotech

Laboratories]). This sampling bladder

should be preloaded with a chosen bio-

cide to reduced degradation of the gas

by bacteria, on top of being stored at

4 °C for a 14-day maximum holding

time before analysis (13).

As a complementary approach, our

group has also demonstrated the capa-

bilities of a new spectroscopic detec-

tor, the VGA-100 vacuum ultraviolet

(VUV) detector (14) (VUV Analytics),

which measures gas phase absorption

in the VUV and ultraviolet wavelength

regions (120–240 nm) to monitor dis-

solved gases in water. This universal

detector offers qualitative gas-phase

VUV spectra to accompany the quan-

titative capabilities of the C1–C5

hydrocarbons, along with N2, O2, and

CO2 if interested (15). While this work

separated C1–C5 hydrocarbons of three

water samples from the Barnett Shale

with the HP-PLOT Q column (30 m

× 0.32 mm, 20-µm df ), the deconvolu-

tion capabilities of the acquired spec-

tra could allow the compounds to be

quantified in the void volume of capil-

lary columns. More work is needed to

interface this detector with the various

sampling protocols that exist for mea-

suring natural gas in water to demon-

strate its unique qualitative and quan-

titative capabilities for routine analysis.

The blue chromatogram of Figure 1

is the previously discussed GC sepa-

ration of a natural gas standard with

the PLOT divinylbenzene column and

VUV detection.

Organic Compounds

In the vast majority of UOG reservoirs,

hydraulic fracturing is used to stimu-

late the formation. The f luids used

to open fissures in low permeability

shale formations include water, sand,

and a small percentage of chemicals.

These chemicals are a mixture of acids,

bases, salts, organic compounds, and

inorganic compounds, which serve

myriad purposes. Even though these

chemicals make up a small percentage

of the liquid used for hydraulic frac-

turing, it can account for a median of

over 10,000 kg (16) in the national

average of 2.4 million gallons of water

used per UOG well (17). This mas-

sive amount of chemicals is trucked to

the pad site, stored and mixed onsite,

and injected for hydraulic fracturing

operations. Then, up to 30% of the

water resurfaces during the f lowback

period before production begins. The

storage, use, and collection of these

chemicals, mixed hydraulic fractur-

ing f luids, other chemicals involved in

equipment cleaning and drilling pro-

cesses, and the resulting f lowback are

all possible sources for groundwater

contamination through controllable

surface activities (18). Casing and

cement failures (19) are a subsurface

possibility for f luid introduction to

the aquifer system, an event with little

operator control, but which occurs at

varying rates reported to be from 3%

(20) up to as many as 12% of wells

within the first five years (21).

The majority of hydraulic fractur-

ing additives can be found in lists that

have been becoming more populated

over the recent years. One of the ear-

liest lists (22) was found in a report

by the US House of Representatives

Committee on Energy and Commerce.

This included over 750 unique addi-

tives found in more than 2500 prod-

ucts available to be used for hydraulic

fracturing from 2005 to 2009. Addi-

tional pertinent information included

in the report are the number of prod-

ucts in which the compound is found,

a table of additives that are health or

environmental risks, and highlights

of statistics for use of specific com-

pounds of concern like 2-butoxyetha-

nol. FracFocus (www.fracfocus.org),

instated in April 2011, is the national

hydraulic fracturing chemical registry.

In the US, 28 states require chemical

disclosure of hydraulic fracturing f lu-

ids, of which 22 are using FracFocus.

It currently contains more than 80,000

disclosure documents from more than

1000 companies. This registry pro-

vides the operator, location, depths,

chemicals, and mixed concentrations

used in hydraulic fracturing activities

(23). The companies that disclose this

information are able to protect trade

secrets with the ability to report some

additives as “proprietary polymers” or

under similar designations. A review

of the FracFocus database from Janu-

ary 2011 through February 2013 (16)

revealed more than 37,000 logs, which

included chemical disclosure of 692

unique ingredients, of which 11% were

deemed trade secrets.

GC coupled to mass spectrometry

(MS) has been the workhorse used to

separate, detect, and possibly identify

volatile and semivolatile compounds

present in groundwater, after appro-

priate sample preparation (2). The

MS detector is practically a require-

ment when surveying groundwater for

contaminants related to UOG, even

when performing targeted analysis for

specific compounds. The qualitative

information gained from the MS detec-

tor is invaluable in confirmation and

unknown identif ication. The poten-

tially complex mixture of compounds

in groundwater impacted by UOG has

generated false positives when using

the suggested FID in EPA methods



used for general groundwater (24).

The overwhelming majority of the

capability of unknown identification

with GC–MS begins with the electron

ionization (EI) source (1). The EI source

generates diagnostic fragment ions of

the compound in a systematic man-

ner. The resulting spectra can then be

matched across a number of mass spec-

tral libraries, generated by the National

Institute of Standards and Technology

(NIST), National Institute of Health

(NIH), and the EPA, among others.

Another ionization source, chemical

ionization (CI), can be used to comple-

ment the EI-resulting data. The molec-

ular ion for the compound is generally

preserved by CI. CI can be described

as a softer ionization technique; there-

fore it generates fewer fragments and

is not used as the primary source for

unknown identification. An MS detec-

tor capable of CI can also possess the

ability for negative chemical ionization

(NCI), a selective ionization technique

effective toward ionizing halogenated

compounds. This selective ionization

is a detriment to broad surveying of

unknown compounds, but it is a valu-

able tool for researchers investigating

halogenated species.

Fragmentation information from

tandem MS (MS-MS) for further con-

fidence in identification can be gener-

ated by ion-trap or triple-quadrupole

MS detectors. High resolution and

accurate mass (HR-AM) analysis of

ions for an additional identification

vector can be achieved with time-

of-f light (TOF) and orbital trap MS

detectors. Hybrid MS detectors can

also be found to combine the MS-MS

capabilities with HR-AM on the back-

end, as with a Q-TOF or Q-orbital trap.

A portion of a Texas well water study

conducted by our group included iden-

tification of the volatile and semivola-

tile compounds in groundwater across

the Barnett Shale region, shown with

sampling locations in Figure 2. A GC–

MS method was developed to provide

appropriate sensitivity and good sam-

ple throughput. The aim of the method

was to extract and separate the greatest

number of compounds from a small

volume of water with minimal sample

preparation. Sample preparation on the

front end included a 2-mL ethyl ace-

tate extraction from 5 mL of ground-

water, shaken 1 min in a screw-top

vial. GC–MS analysis was performed

using a 30 m × 0.25 mm, 0.25-µm

df Rxi-5ms (Restek) column with a

single-quadrupole MS detector using

an EI source. The “5” column, or 5%

diphenyl, 95% PDMS stationary phase,

is typically regarded as a general pur-

pose column and has retention charac-

teristics quite comparable to columns

in which Kovat’s retention indices

were calculated. The retention index

of an unknown peak can be a valuable

piece of data to narrow down possibili-

ties of detected unknown compounds

(25). Separation and MS parameters

were set for the detection of 35 tar-

get compounds in our method. These

compounds were chosen based on their

popularity of use, possible health and

environmental effects, detection in

previous research, and GC amenability.

These compounds consisted of various

alcohols, aromatics, and other hydro-

carbons. MS settings, summarized in

Table I, included groups of selected

ion monitoring (SIM) events for the

base peaks of our target compounds,

coupled with full spectral scanning for

the confirmation of measured peaks by

SIM, as well as the possible identifi-

cation of unknowns through spectral

matching. These acquisition groups

were typically around 2 min each in an

effort to keep the SIM ion count low

to maintain an effective MS duty cycle.

The scanning parameters also changed

with each acquisition group, in that

the concluding m/z increased from

100 to 400 over the time of the sepa-

ration. The assumption used was that

compounds eluted earlier from the col-

umn would be lighter than those eluted

later; reducing the acquisition window

reduces noise in the spectrum.

Initial application of this method

detected methanol and ethanol in some

groundwater samples. These detections

were unable to be quantified with data

at the time because of poor retention on

the Rxi-5ms column and a fair amount

of background noise from permanent

gases like N2 and O2 when monitoring

their base peak, 31 m/z. This led our

team to develop a method to address

both of these problems.

A mid-polarity GC column was

chosen for the complementary analy-

sis. The team still wanted to maintain

the ability to adequately retain linear

hydrocarbons if present, so working

with a 100% PEG column was nearly

out of the question, even though it

maintains a great selectivity for these

alcohols. The 30 m × 0.32 mm, 1.20-

µm df Phenomenex ZB-BAC2 column,

developed and marketed for blood

alcohol analysis, was chosen for its

retention and selectivity for these two

alcohols and other solvents, along with

the possibility of using a second paired

column, the Phenomenex ZB-BAC1

column, for confirmation if needed.

N

miles

2013 sample site

2015 sample site

Oil and gas well

County

Barnett Shale

6030150

Figure 2: Map of the Barnett Shale region, UOG wells, and sampling locations from reports in references 26 and 27.



The method also incorporated FID to

help reduce the background noise while

detecting the light alcohols. A static

headspace injection technique was cho-

sen to effectively extract the analytes of

interest and reduce background. A salt

solution was added to the water sample

to reduce the solubility of the alcohols

in groundwater. Samples were agitated,

heated, and injected automatically

using an AOC-5000plus autosampler

(Shimadzu Scientific Instruments).

In our initial study of Barnett Shale

groundwater in 2011 (26), 29 of the

100 samples contained methanol at

concentrations as high as 329 mg/L

and 12 samples contained ethanol at

levels as high as 11 mg/L. These detec-

tions had no correlation with distance

to UOG wells. Numerous industrial

processes use these alcohols, and they

can be produced through a range of

biological pathways, so identifying the

sources for the occurrences was not

practical with the limited data.

A follow-up study in 2014 expanded

the research to 550 groundwater sam-

ples across 13 counties in north Texas

(27), shown in Figure 2. Additional

compounds were detected in this study

in addition to the methanol and etha-

nol from the previous studies. Alcohols

included methanol (35 wells), etha-

nol (240), isopropyl alcohol (8), and

propargyl alcohol (155). Ethanol and

propargyl alcohol had a positive corre-

lation with each other. These are both

ingredients in hydraulic fracturing f lu-

ids and were detected at a higher fre-

quency than expected in the most pro-

ductive counties based on chi-squared

analysis. Chloroform, dichloromethane,

and trichloroethylene were detected in

330, 122, and 14 wells, respectively.

These chlorinated compounds are

not disclosed ingredients in hydraulic

fracturing f luid, but have been identi-

fied in UOGWW (28) and dichloro-

methane has been suggested (29) to be

present during drilling operations as

a degreaser for equipment. The study

also found that 381 samples contained

at least one aromatic of the BTEX

class, with 10 samples containing all

four species. Benzene was detected in

34 wells, toluene in 240, ethyl benzene

in 22, and at least one xylene isomer

in 240 water well samples. The BTEX

compounds collectively can be found

in hydrocarbon fuels, refined or unre-

fined, and some are used individually

as industrial solvents, even as hydraulic

fracturing additives.

All of the compounds mentioned

above can be linked directly or indi-

rectly to UOG operations. The fact that

these compounds are fairly common in

the industrial or agricultural setting

in which this research was conducted,

renders it impossible to implicate UOG

as the source of the contaminants with

absolute confidence. It is expected that

the only definitive manner in which

to conclusively attribute UOG as a

source of groundwater contamination

would be through the detection of pro-

prietary tracers (30), suggested to be

f luorinated compounds exotic enough

to assist each company with MS detec-

tion for internal monitoring.

Spectroscopy

Chromatography has been at the fore-

front of advanced analytical chemistry

to tackle the challenges of analyzing

complex mixtures related to UOG that

possibly could be encountered during

research. The previously discussed

approaches are appropriate when iden-

tifying individual compounds, but

there are situations when monitoring of

bulk chemical classes yields adequate

information. Many metals and ions

can also be determined spectroscopi-

cally (31). For the most part, the opera-

tional costs for these methods are less

than chromatography–MS methods,

less technical to operate, and can even

be performed portably. Spectroscopic

approaches associated with UOG

have included UV–vis spectroscopy,

infrared (IR) spectroscopy, and opti-

cal emission spectroscopy (OES). Yet,

many of these methods can fall victim

to interferences from chemically simi-

lar compounds or ions since they are

being measured in bulk solution with-

out prior sample preparation. These

methods are also typically intended

for oil field waste waters or produced

water, both of which commonly con-

tain higher concentrations of the ana-

lyte than ever expected in compro-

mised groundwater.

Absorbance methods measured in

the UV–vis region have been used for

quantitating anionic surfactants (32),

barium (33), boron (34), iron (35),

sulfate (36), sulf ide (37), and total

petroleum hydrocarbons (TPH) (38).

Anionic surfactants can be monitored

Table I: MS programing for targeted and untargeted GC–MS analysis for ingredients in hydraulic fracturing fluids

Start (min) End (min) Acq. Mode Start m/z End m/z SIM SIM SIM SIM SIM SIM SIM SIM

1.401.40

2.252.25

ScanSIM

40.0 100.0 31.1 55.1 29.1 49.1

2.25 2.88 Filament off to allow ethyl acetate solvent to be eluted

2.882.88

5.785.78

ScanSIM

40.0 200.0 78.1 56.1 31.1 45.1 91.1 44.1 130.1 83.1

5.785.78

6.356.35

ScanSIM

40.0 200.0 91.1 57.1 29.1 105.1 44.0

6.356.35

7.307.30

ScanSIM

40.0 250.0 45.1 57.1 59.1 68.1 73.0 91.1 105.15 103.0

7.307.30

8.508.50

ScanSIM

40.0 300.0 128.1 142.15

8.508.50

11.0011.00

ScanSIM

40.0 400.0 45.1 14.15 213.1

11.00 13.00 Scan 40.0 400.0



near 655 nm as a complex with methy-

lene blue according to EPA Method

425.1. Chloride solutions have been

shown to give false positives with this

approach (39). Barium has been mea-

sured as low as 2 mg/L as a precipitate

after adding a sodium sulfate mixture

and quantified at 450 nm. Strontium,

silica, and calcium are the most det-

rimental interferences that may be

encountered using this approach.

Boron is measured at levels above

2 mg/L as the reaction product with

carmine at 605 nm. Iron is monitored

with the colorimetric phenanthroline

indicator at 510 nm after the reagent

has converted most forms of iron to a

soluble ferrous iron. Iron can be mea-

sured down to 0.1 mg/L with the most

common interference being a cumu-

lative concentration of Ba2+ and Sr2+

greater than 50 mg/L. Sulfate at levels

greater than 2 mg/L can be measured

at 450 nm through a turbidimetric

method after precipitation as barium

sulfate. However, barium, magnesium,

and silica present in the water sample

can interfere with the accuracy of

these results. Sulfide can be detected

spectroscopically down to 0.01 mg/L

at 665 nm after reacting with N,N-

dimethyl-p-phenylenediamine sulfate

to form methylene blue. A semiquan-

titative method for TPH, which is a

cumulative measurement of hydrocar-

bons ranging from C6 to C36, uses an

immunoassay in which the hydrocar-

bons and enzyme compete to bind to

antibodies immobilized on the cuvette.

Measuring this absorbance at 450 nm

yields a sensitivity equivalent to at least

2 mg/L diesel fuel. Chlorine present in

solution can interfere with the assay.

TPH can also be measured by IR

spectroscopy. Previously, a method

consisting of serial extractions with

f luorocarbon-113 (CFC-113) and silica

drying has been shown to be able to

generate an extract adequate to quan-

tify the absorbance of C-H stretches at

2950 cm-1. Since CFC-113 is an ozone-

depleting substance, the EPA has dis-

continued the method and suggests

using the ASTM International Method

D7006-04, which uses S-316 as a CFC

substitute.

OES has become the chosen tech-

nique for measuring dissolved metals

in UOGWW (2). Metals of interest

such as Ba, Sr, Fe, Na, Ca, and Mg are

easily in the milligram-per-liter con-

centration range, comfortably above

detection limits for inductively coupled

plasma (ICP)-OES. The alkali and

alkaline earth metals can be at levels of

hundreds to thousands of milligrams

per liter depending on the contribu-

tion of formation water to the overall

UOGWW mixture. These excessive

concentrations can be detrimental to

an ICP-MS, accepted to be more sen-

sitive than the ICP-OES. Most ICP-

OES instruments come with the option

to change between axial and radial

viewing modes to assist in measuring

samples across a wide concentration

range, resulting in a much wider lin-

ear range for quantification than ICP-

MS (40). Great attention needs to be

taken in wavelength selection for the

ICP-OES to ensure there is no spectral

overlap from other metals at high con-

centrations or unexpected interferences

from other hydraulic fracturing addi-

tives. Atomic absorption could also be

implemented, but lacks the multiele-

ment throughput of ICP-OES.

The majority of research and appli-

cation notes involving metals analysis

with ICP-OES have been toward pro-

filing UOGWW. These samples are

currently a national disposal issue, a

challenging matrix to overcome when

making measurements, and possess a

set of inorganic “known knowns” like

brine salts to target. Our more recent

investigation (27) of water quality over-

lying the Barnett Shale used ICP-OES

(ICPE-9000 from Shimadzu Scientific

Instruments, Inc.) for measurement of

13 metals most relevant to UOG explo-

ration and that exhibited minimal

spectral interferences. Strontium was

the only metal determined to be above

the 4.0 mg/L maximum contaminant

level (MCL) by ICP-OES. Standard

addition was used for quantification to

overcome unpredictable matrix effects

that had previously been observed (41).

ICP-MS is another approach for

elemental analysis, primarily for trace

metals in water. The MS detector is

more sensitive than using OES, but

has a limited dynamic range. Our

groundwater studies (26,27) have used

ICP-MS (Varian 820 ICP-MS) for the

quantification of arsenic and selenium.

Arsenic with an MCL of 10 µg/L and

selenium at 50 µg/L warrant the addi-

tional sensitivity of ICP-MS for ade-

quate quantitation. Strontium and bar-

ium were also measured by ICP-MS in

2011 because of instrument availability

(26). In 2011, As, Se, and Sr each were

shown to have a negative correlation

with proximity to UOG wells (that is,

higher values in water wells closer to

UOG wells). The most plausible con-

clusion was that an increased pH and

mechanical vibrations from the neigh-

boring UOG activity liberated iron

oxide that had complexed these metals

in poorly maintained water wells. In

2011, 29 of the 100 samples exceeded

the EPA MCL for arsenic, but only 10

of the 550 samples in 2014 exceeded

the limit. It is hypothesized that the

reduction in UOG exploration between

the sampling campaigns reduced sub-

surface vibrations, in turn reducing

the amount of dissolved arsenic. The

water quality in 2014 was also found to

be a less reducing environment, which

would decrease the solubility of arsenic

in groundwater.

Concluding Remarks

Signif icant efforts have been made

in this decade, by predominately aca-

demic institutions, to understand the

environmental, social, and economic

effects of UOG exploration. The col-

laborations that have formed through

this multifaceted research have gener-

ated astounding conclusions to date,

but nearly all have commenced with-

out technical or chemical advice from

industrial partners. The guidance

from drilling operators would allow for

more focused and efficient analytical

methods and more effective conclu-

sions, which could in turn ease the

public opinion of UOG. The detec-

tion of most of these aforementioned

compounds can occur in groundwa-

ter through avenues other than UOG,

convoluting the ability to identify the

source. The burden has been on the

researcher to present exhaustive evi-

dence if contamination from UOG has

been suggested, but operators are able

to merely discredit the research and rely

on the uncertainty of these other pos-

sibilities. It is expected that overcoming



this hurdle will only happen once pro-

prietary chemicals or tracers are incor-

porated, so that contamination events

can be clearly attributed to a particu-

lar UOG process and operation. Until

then, conclusions will continue to be

deduced through disproving all other

possibilities, which is not an efficient

route to take.

References

(1) S. Stein, Anal. Chem. 84, 7274–7282 (2012).

(2) D.D. Carlton Jr., Z.L. Hildenbrand, B.E.

Fontenot, and K.A. Schug, in Hydrau-

lic Fracturing Impacts and Technologies, V.

Uddameri, A. Morse, and K.J. Tindle, Eds.

(Taylor & Francis Group, New York, 2016),

pp. 115–132.

(3) S.G. Osborn, A. Vengosh, N.R. Warner,

and R.B. Jackson, Proc. Natl. Acad. Sci.

U.S.A. 108, 8172–8176 (2011).

(4) R.E. Jackson, A.W. Gorody, B. Mayer, J.W.

Roy, M.C. Ryan, and D.R. Van Stempvoort,

Ground Water 51, 488–510 (2013).

(5) A. Kissinger, R. Helmig, A. Ebigbo, H.

Class, T. Lange, M. Sauter, M. Heitfeld, J.

Klünker, and W. Jahnke, Environ. Earth Sci.

70, 3855–3873 (2013).

(6) D.A. Stolper, M. Lawson, C.L. Davis, A.A.

Fereira, E.V. Santos Neto, G.S. Ellis, M.D.

Lewan, A.M. Martini, Y. Tang, M. Schoell,

A.L. Sessions, and J.M. Eiler, Science 344,

1500–1503 (2014).

(7) S.G. Osborn and J.C. McIntosh, Applied

Geochemistry 25, 456–471 (2010).

(8) R.B. Jackson, A. Vengosh, T.H. Darrah,

N.R. Warner, A. Down, R.J. Poreda, S.G.

Osborn, K. Zhao, and J.D. Karr, Proc. Natl.

Acad. Sci. U.S.A. 110, 11250–11255 (2013).

(9) M. Schoell, Geochim. Cosmochim. Acta 44,

649–661 (1980).

(10) L. Chambers, OI Analytical, Application

Note 37920312, (2012).

(11) F. Hudson, US EPA, RSK SOP-175, (2004).

(12) Z. Ji, Agilent Technologies, Application

Note 228-387, (2000).

(13) Isotech Laboratories, Collection of Ground-

water Samples from Domestic and Munici-

pal Water Wells for Dissolved Gas Analysis

http://www.isotechlabs.com/customersup-

port/samplingprocedures/IsoBagSM.pdf

(August 6, 2014) (2011).

(14) K.A. Schug, I. Sawicki, D.D. Carlton Jr., H.

Fan, H.M. McNair, J.P. Nimmo, P. Kroll,

J. Smuts, P. Walsh, and D. Harrison, Anal.

Chem. 86, 8329–8335 (2014).

(15) L. Bai, J. Smuts, P. Walsh, H. Fan, Z.

Hildenbrand, D. Wong, D. Wetz, and K.A.

Schug, J. Chromatogr. A 1388, 244–250

(2015).

(16) Analysis of Hydraulic Fracturing Fluid

Data from the FracFocus Chemical Dis-

closure Registry 1.0, (U.S. Environmental

Protection Agency, Washington, D.C.,

EPA/601/R-14/003, i-155, 2015).

(17) R.B. Jackson, E.R. Lowry, A. Pickle, M.

Kang, D. DiGiulio, and K. Zhao, Environ.

Sci. Technol. 49, 8969–8976 (2015).

(18) A. Vengosh, R.B. Jackson, N.R. Warner,

T.H. Darrah, and A. Kondash, Environ. Sci.

Technol. 48, 8334–8348 (2014).

(19) C. Brufatto, J. Cochran, L. Conn, and

D. Ower, Oilfield Review Autumn, 62–76

(2003).

(20) R.D. Vidic, S.L. Brantley, J.M. Vandenboss-

che, D. Yoxtheimer, and J.D. Abad, Science

340, 1235009 (2013).

(21) A.R. Ingraffea, M.T. Wells, R.L. Santoro,

and S.B.C. Shonkoff, Proc. Natl. Acad. Sci.

U.S.A. 111, 10955–10960 (2014).

(22) United States House of Representatives

Committee on Energy and Commerce,

http://democrats.energycommerce.house.

gov/sites/default/files/documents/Hydraulic-

Fracturing-Chemicals-2011-4-18.pdf, (2011).

(23) www.fracfocus.org/chemical-use (April 1,

2014).

(24) D.C. DiGiulio, R.T. Wilkin, C. Miller, and

G. Oberley, Investigation of Ground Water

Contamination Near Pavillion, Wyoming,

(U.S. Environmental Protection Agency,

Washington, D.C., 2011).

(25) Shimadzu Europa GmbH, Linear Retention

Index Function (LRI) in GCMSsolution 2.4

http://www2.shimadzu.com/applications/

gcms/Appl_GCMS_Ret-index_07C_066_

en.pdf (January 23, 2013).

(26) B.E. Fontenot, L.R. Hunt, Z.L. Hilden-

brand, D.D. Carlton Jr., H. Oka, J.L. Wal-

ton, D. Hopkins, A. Osorio, B. Bjorndal,

Q.H. Hu, and K.A. Schug, Environ. Sci.

Technol. 47, 10032–10040 (2013).

(27) Z.L. Hildenbrand, D.D. Carlton Jr., B.E.

Fontenot, J.M. Meik, J.L. Walton, J.T. Tay-

lor, J.B. Thacker, S. Korlie, C.P. Shelor, D.

Henderson, A.F. Kadjo, C.E. Roelke, P.F.

Hudak, T. Burton, H.S. Rifai, and K.A.

Schug, Environ. Sci. Technol. 49, 8254–

8262 (2015).

(28) T.D. Hayes and B.F. Severin, “Characteriza-

tion of Flowback Waters from the Marcel-

lus and Barnett,” Gas Technology Institute

(2012).

(29) T. Colborn, K. Schultz, L. Herrick, and C.

Kwiatkowski, Hum. Ecol. Risk Assess. 20,

86–105 (2014).

(30) S.K. Ritter, C&EN 92, 31–33 (2014).

(31) Hach Company, Hydraulic Fracturing Water

Analysis Handbook, www.hach.com/asset-

get.download.jsa?id=17255280808 (July 29,

2014) (2013).

(32) P.R. Wright, P.B. McMahon, D.K. Mueller,

and M.L. Clark, U. S. Geological Survey,

Data Series 718, (2012).

(33) Hach Company, Barium, Turbidimetric

Method 10251 www.hach.com/asset-get.

download.jsa?id=9595814105 (July 29,

2014).

(34) Hach Company, Boron Carmine Method

10252 www.hach.com/asset-get.download.

jsa?id=9595814103 (July 29, 2014).

(35) Hach Company, Iron, Total Method


jsa?id=9595814104 (July 29, 2014).

(36) Hach Company, Sulfate Method 10248

w w w.hach .c om /a s s e t- g e t .dow n load .

jsa?id=9595814106 (July 29, 2014).

(37) Hach Company, Sulfide Methylene Blue

Method 10254 www.hach.com/asset-get.

download.jsa?id=11122402601 (July 29,

2014).

(38) Hach Company, TPH (Total Petroleum

Hydrocarbons) Immunoassay Method


jsa?id=7639983907 (July 29, 2014).

(39) A.L. George and G.F. White, Environ. Toxi-

col. Chem. 18, 2232–2236 (1999).

(40) PerkinElmer Inc., “Atomic Spectros-

copy — A Guide to Selecting the Appro-

priate Technique and System,” http://

www.perkinelmer.com/CMSResources/

Images/44-74482BRO_WorldLeaderAA-

ICPMSICPMS.pdf (September 10, 2013)

(2013).

(41) D.D. Carlton Jr., B.E. Fontenot, Z.L.

Hildenbrand, T.M. Davis, J.L. Walton, and

K.A. Schug, Int. J. Environ. Sci. Technol.

April, 1–10 (2015).

Doug D. Carlton Jr., and Kevin

A. Schug are with the Department

of Chemistry and Biochemistry

and the Collaborative Laboratories

for Environmental Analysis and

Remediation at The University of

Texas at Arlington in Arlington, Texas.

Zacariah L. Hildenbrand is

with the Collaborative Laboratories

for Environmental Analysis and

Remediation at The University

of Texas at Arlington and Inform

Environmental, LLC, in Dallas,

Texas. Direct correspondence

to: [email protected] ◾


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