gas sensors and sensor systems for air quality monitoring workhop at j… · lab for measurement...

LAB FOR MEASUREMENT TECHNOLOGY

Prof. Dr. rer. nat. A. Schütze

MACPoll Workshop

Gas Sensors and Sensor Systems

for Air Quality Monitoring

JRC Ispra, November 19, 2013, Ispra, IT

Andreas Schütze Saarland University, Lab for Measurement Technology

VOC-IDS

Slide Gas Sensors and Sensor Systems for Air Quality Monitoring – MACPoll workshop – JRC Ispra

Background: indoor air quality

Gas sensors for air quality

Electrochemical cells

Semiconductor gas sensors

Other technologies: mass sensitive devices, GasFETs

Gas measurement systems – more than just sensors

The three “S”

Gas measurement systems incl. signal processing and evaluation

Calibration and field testing

Performance example: ppb-level VOC identification

Novel developments

Novel sensor technologies: nanomaterials and integrated GasFETs

System self monitoring

Multifunctional multisensor systems

Conclusions







The three “S”




Novel developments




Conclusions

2

> Outline

Nov. 19, 2013

Slide Gas Sensors and Sensor Systems for Air Quality Monitoring – MACPoll workshop – JRC Ispra 3

> Background: Indoor Air Quality

Why worry about indoor air?

Safety

Gas leak detection (combustible gases, e.g. CH4)

Fire detection (various gases)

Hazardous gas detection (e.g. CO)

Malodor detection (kitchen & bathroom ventilation)

HVAC systems

Reduced air circulation for greatly reduced energy consumption

CO2 monitoring for fresh air

Increased levels of VOCs lead to sick building syndrome

Selective (formaldehyde, benzene etc.)

and sensitive (ppb level) detection

Systems have to be adapted to the specific room use scenario

Nov. 19, 2013


> Background: Indoor Air Quality

Sensor requirements

Low cost

Networked systems (in major buildings, but also private homes)

Long lifetime: >10 years without maintenance for private homes

Which sensors are used today?

Safety

Gas leak detection: human nose, Japan: MOS; pellistors: only industr.

Fire detection: various sensors, mostly optical;

gas sensor systems under development (EC, MOS, GasFET)

Hazardous gas detection: EC, MOS

Malodor detection: MOS

HVAC systems

CO2 monitoring: NDIR (in major rooms/buildings), EC, GasFET

VOCs: MOS (total VOC), GasFET (emerging)

Nov. 19, 2013


> Background: our research focus

VOC-IDS: Volatile Organic Compound Indoor Discrimination Sensor

Transnational project funded within MNT-ERA.net

Selective VOC detection, primarily formaldehyde, benzene

Novel ceramic nanomaterial metal-oxide semiconductor gas sensors

Intelligent signal processing based on temperature cycling

Networked systems connected to KNX bus

SENSIndoor: Nanotechnology based intelligent multi-SENsor System

with selective pre-concentration for Indoor air quality control

EU-FP7 project NMP.2013.1.2-1:

Nanotechnology-based sensors for environmental monitoring

Microtechnology based approach for MOS and SiC-GasFET sensors

Pre-concentration to boost sensitivity and selectivity

Integrated multi-sensor approach

Application specific priorities and field tests

Nov. 19, 2013


> Outline







The three “S”

Gas measurement systems

Signal processing and evaluation


Novel developments




Conclusions







The three “S”




Novel developments




Conclusions

Nov. 19, 2013


Electrochemical cells: amperometric gas sensors

Gas molecules are ionized at sensing electrodes

Ions are conducted through the electrolyte cell

Current is proportional to gas concentration (diffusion limited)

Sensitivity typically down to ppm, for some reactive gases sub-ppm

Partial selectivity achieved, but still relevant cross-sensitivities

Poor long term stability (especially at high temperature, low humidity)

Plus: baseline drift, sensitivity change, poisoning

7

> Gas sensors for air quality

Source: Alphasense Ltd. Quelle: Sixth Sense

Nov. 19, 2013


Metal Oxide Semiconductor (MOS), e.g. SnO2, Ga2O3, WO3

Oxygen adsorption leads to energy barrier at grain boundaries

Gas adsorption or reaction with O- influences energy barrier

Very high sensitivity (exponential effect of energy barrier on resistance)

Very low cost: manufacturing based on MEMS and screen printing

8


So

urce: F

igaro

Inc., Jap

an

Source: MicroChemical Systems S.A., Corcelles, CH Source: UST Umweltsensortechnik GmbH

Nov. 19, 2013


Metal Oxide Semiconductor (MOS), e.g. SnO2, Ga2O3, WO3

9


So

urc

e: F

igar

o I

nc.

, Ja

pan

Great robustness (> 10 years

lifetime in applications)

Poor stability

Sensor drift due to poisoning etc.

Influence of background, esp.

humidity

quantitative meas. difficult

Poor selectivity

Response is mainly due to

changing oxygen coverage

“Its surprising if the MOS sensor

does not show a reaction!”

greatest challenge for R&D

Nov. 19, 2013


Organic semiconductor materials, i.e. Phthalocyanine

p-type semiconductor esp. suitable for oxidizing gases, i.e. NOx, O3

robust, low-cost, stability and selectivity similar to MOS

10


So

urc

e: A

. S

chü

tze,

Dis

sert

atio

n, JL

U G

ieß

en, 1

99

4

Nov. 19, 2013



Other sensor technologies? Mostly still under development

Pellistors: detection of combustible gases at higher conc.

Mass-sensitive devices

Bulk or surface acoustic

Mass change on surface

leads to change of fres

High sensitivity: SAW

GHz: complex electronics

high temp. stability req.

GasFET

Gas-sens. Field Eff. Transistor

Classic Lundström FET:

Hydrogen diffusing thru Pd

Only H-containing gases

Nov. 19, 2013


> Outline







The three “S”

Gas measurement systems

Signal processing and evaluation


Novel developments




Conclusions







The three “S”




Novel developments




Conclusions

Nov. 19, 2013


> Gas measurement systems – more than sensors

The three “S”

Sensitivity

Broad spectrum

from below ppb (for malodors, ozone, hazardous VOCs)

up to 1000 ppm (gas leak, CO2)

Selectivity

False alarms are primary concern for fire detection (ratio 10:1)

VOC detection: hazardous (formaldehyde) vs. neutral (alcohol

vapor, cleaning agents) vs. wanted (odorants)

Stability

Industrial applications: maintenance interval < 6 months

Public buildings: annual or bi-annual tests (if that)

Private homes: 10 years lifetime w/o regular maintenance?

Nov. 19, 2013


0

10

20

30

Benzene

0 5 10 15 200

10

20

30

40

50

Diethyl Ether

Iso-Pentane

Methyl Alcohol

0 5 10 15 20

Methyl Tert-Butyl Ether

Co

ndu

cta

nce

[a

.u.]

Time in Temperature Cycle [sec]

70% r.h.

30% r.h.

Signal

evaluation:

1. Normalization of the response curves reduces sensor drift 2. Generation of secondary features, i.e. levels, slopes etc. 3. Suitable patterns are extracted for further evaluation

A. S

chü

tze,

A. G

ram

m, T

. R

üh

l IE

EE

Sen

sors

Jo

urn

al, V

ol.

4, N

o. 6

, 200

4

0 5 10 15 20

Propylene Oxide

Gas measurement systems – more than sensors Temperature Cycled Operation (TCO)

Nov. 19, 2013


Evaluation of sensor data based on

temperature cycling (example)

Virtual multisensor

Characteristic features of the curve shapes

(i.e. slope at the end of the high

temperature phase and curvature during

the low temperature phase) are evaluated, to

discriminate between different gases in

several steps.

Note: the decision tree reflects the chemical

composition of the solvents starting with the

alkane pentane and the aromatic benzene

(both pure CH-compounds), then the alcohol

(R-COH) and finally the three ether

compounds (R1-O-R2). This indicates that

an expansion might be possible to classify

many different molecules.

Decision 3

Synthetic air, Iso-Pentane,

Benzene, Methyl Alcohol,

Methyl Tert-Butyl Ether,

Diethyl Ether, Prop. Oxide

Decision 4

Decision 1

Decision 2

Methyl 4-Butyl Eth.

clean air &

interferents

Propylene Oxide

Diethyl Ether

Benzene

Iso-Pentane

Methyl Alcohol

T-Cycle 2

T-Cycle 1

So

urc

e: A

. S

chü

tze,

A. G

ram

m, T

. R

üh

l IE

EE

Sen

sors

Jo

urn

al, V

ol.

4, N

o. 6

, 2

00

4

16

Gas measurement systems – more than sensors Temperature Cycled Operation (TCO)

Nov. 19, 2013


Gas measurement systems – more than sensors Temperature Cycled Operation (TCO) – hardware

• Heater temperature control Heater resistor RH(T) controlled for exact temperature control of (micro-)hotplates

• Sensor read-out with large dynamic range for MOS, GasFET and pellistor type sensors

Hardware platform GasTON for exact temperature control and large dynamic

range data acquisition – Gas sensor T-cycle Operating uNit

0 2 4 6 8 10 12242

243

244

245

246

247

248

249

250+120°C

-20°C

ambient temperature

-20°C

time [h]

0°C

R1

Rpot

Radj

R2

RH

R3

R4

R6

R5

_

+

UV

now commercialized “OdorChecker”

by 3S GmbH (spin-off of LMT)

Nov. 19, 2013


Hardware platform for Electrical Impedance Spectroscopy

Hardware concept using ETFE (Empirical Transfer Function Estimate)

Sensor excitation Sensor response

𝒁 𝜔 =𝑼 𝜔

𝑰 𝜔=𝑈𝑆𝑒𝑛𝑠𝑜𝑟(𝜔)𝑈𝑅𝑟𝑒𝑓(𝜔)

𝑅𝑟𝑒𝑓

M. Schüler, S. Darsch, P. Reimann, A. Schütze, „Low-cost impedance spectroscopy for semiconductor gas sensors – a hardware concept“ IWIS, Chemnitz, 13.-15. October 2010

18

Gas measurement systems – more than sensors Electrical Impedance Spectroscopy (EIS) for MOS

Nov. 19, 2013


Gas measurement systems – more than sensors Gate Bias Cycled Operation (GBCO) for GasFETs

0 20 40 60 80

200

220

240

260

280

tem

pe

ratu

re [

°C]

time [s]

set temperature

actual temperature

applied gate bias

-1

0

1

2

3

ga

te b

ias

[V

]

• Heater temperature control

Heater resistor RH(T) controlled for exact temperature control of GasFET

• Sensor read-out: voltage drop VD measured at constant current ID

• Gate voltage VG varied to

influence the operating point

TCO hardware platform extended to allow Gate Bias Variation

C. Bur et al.: Combination of Temperature Cycled and Gate Bias Cycled Operation to enhance the Selectivity of SiC-FET

Gas Sensors, Proc. Transducers 2013 & Eurosensors XXVII; Barcelona, Spain, June 16 - 20, 2013

Nov. 19, 2013


Target appl.

E. L

eon

hard

t, Th

esis pro

ject, UdS

-LM

T, 2

00

7.

Many possibilities for

optimization:

• Sensor selection

• Operating mode • TCO

• EIS

• GBCO

• Data acquisition

• Signal preprocessing

• Feature extraction

• Separation

• Classification

…and always testing under real

application conditions (field

testing)! DatennormierungM e r k m a l s e x t r a k t i o n

sensors

electronics

normalization

feature extract.

discrimination/

separation

classification

gas test system

raw data

TCO/EIS

Sig

nal p

rocessin

g

dynam

ic

meas.

Cla

ssific

atio

n

Sta

tic

meas. Gas measurement systems – more than sensors Temperature Cycled Operation – system design

Nov. 19, 2013


Calibration: novel gas mixing system for VOC testing down to sub ppb-level

21


Nov. 19, 2013


Novel gas mixing system: results of first sensor tests

23


10000 20000 30000 40000 500003,0x10

-3

4,0x10-3

5,0x10-3

6,0x10-3

7,0x10-3

8,0x10-3

9,0x10-3 cond. UST 1000

conc. formaldehyde

time [s]

co

nd

ucta

nce

[A

/V]

upper range value of ADC

1E-4

1E-3

0,01

0,1

1

10

co

nce

ntr

atio

n [

pp

m]

40000 50000

4,1x10-3

4,2x10-3

cond. UST 1000

conc. formaldehyde

time [s]

co

nd

ucta

nce

[A

/V]

Sensor reaction

to 1 ppb

formaldehyde

Relevance?

Legal limits in

France:

Formaldehyde

25 ppb in 2015;

Benzene

0.6 ppb in 2016

Nov. 19, 2013


> Indoor Air Quality monitoring

MNT-ERA.net project VOC-IDS

Volatile Organic Compound Indoor Discrimination Sensor

Scenario specific detection of hazardous VOC

Integration of sensor system into KNX building automation networks

24 Nov. 19, 2013




Example for selective detection of VOCs in interfering background

Classification of Formaldehydye, Benzene, Naphthalene in presence of ethanol

25

interferent

concentrat.

relative

humidity

number of

LDA steps

for charac.

Estimated

number of

LDAs

0, 0.4, 2 40%, 60% 1 1

None 40%, 60% 2 1+10(?)*1

0, 0.4, 2 None 1 (2) (1+) 5*1

target gas Concentration (ppb) humidity Interferents (EtOH ppm)

Air NA 40%, 60% none, 0.4, 2

Formaldehyde 10, 100 40%, 60% none, 0.4, 2

Benzene 0.5, 4.7 40%, 60% none, 0.4, 2

Naphthalene 2, 20 40%, 60% none, 0.4, 2

generalized classification

classification w known EtOH

classification w known r.h.

Nov. 19, 2013

Slide Gas Sensors and Sensor Systems for Air Quality Monitoring – MACPoll workshop – JRC Ispra Nov. 19, 2013

air

formaldehyde

benzene

naphthalene

w/o additional information



Example for selective detection of VOCs in interfering background

Classification of Formaldehydye, Benzene, Naphthalene in presence of ethanol

General classification possible, but noisy

Improved performance with known rel. humidity (from additional sensor)

This proves VOC identification at levels below future thresholds!

26

air

formaldehyde

benzene

naphthalene

with known rel. humidity


> Novel developments

Novel sensor materials and transducer principles

Nanotechnology for improved gas sensitive layers

Novel transducer principles

Gas ionization (can be electronically controlled!)

GasFETs commercially available

Source: Micronas

28 Nov. 19, 2013



Highly sensitive VOC detection with SiC GasFETs (SenSiC AB)

30

874

875

876

877

878

879

880

881

882

0.5

ppb1.5

ppb2.5

ppb3.5

ppb

sensor response VDS

smoothed signal

sen

so

r sig

nal V

DS

[m

V]

4.5

ppb

quasi static sensor response Pt-gate @ 220 °C

0 2 4 6 8 100

1

2

3

4 Benzene

co

nc. [p

pb

]

time [h]

866

868

870

872

874

876

878

quasi static sensor812sponse Pt-gate @ 220 °C

sen

so

r s

ig

n

al V

D S [m

V]

D S smoothed signal1 05 01 0 01 5 02 0 0

C. Bur et al.: Detecting Volatile Organic Compounds in the ppb Range with Pt-gate SiC-Field Effect Transistors,

Proc. IEEE Sensors 2013; Baltimore, USA, Nov. 3-6, 2013

Nov. 19, 2013



System integration: Gate Bias Cycled Operation for SiC-GasFETs

C. Bur et al.: Influence of a Changing Gate Bias on the Sensing Properties of SiC Field Effect Gas Sensors, IMCS 2012

0 1 2 3 4 50

40

80

120

160

200

240 5% O

2 in N

2

40 s ramp

cu

rre

nt

i DS (

µA

)

gate bias UG

(V)

200°C

0 1 2 3 4 50

40

80

120

160

200

240

cu

rre

nt

i DS (

µA

)

gate bias UG

(V)

400 ppm CO

40 s ramp

200°C

0 1 2 3 4 50

50

100

150

200

250

300

150°C

cu

rre

nt

i DS [

µA

]

gate bias UG [V]

5% O2 in N

2

40 s ramp

0 1 2 3 4 5

10

20

30

40

50

60

70

80

90

cu

rre

nt

I DS [

µA

]

gate bias UG [V]

5% O2 in N

2

120 s ramp

200°C

02040608089.1201400

31 Nov. 19, 2013



A. Schütze et al.: Improving MOS Virtual Multisensor Systems by Combining Temperature Cycled Operation with Impedance Spectroscopy, ISOEN 2011

• Electrical Impedance Spectroscopy achieves similar improvement in selectivity

as Temperature Cycled Operation – but time scale is completely different!

TCO data EIS data

shifts caused by 100 ppb

ethene in 1% methane

32 Nov. 19, 2013



Sensor self-monitoring with combination of TCO and EIS

A.

Sch

ütz

e et

al.

: Im

pro

vin

g M

OS

Vir

tual

Mult

isen

sor

Sy

stem

s by

C

om

bin

ing

Tem

per

atu

re C

ycl

ed O

per

atio

n w

ith

Im

ped

ance

S

pec

tro

scop

y, I

SO

EN

20

11

classification classification

feature extraction

feature extraction

discrimination discrimination

output

diagnostic

T-cycle EIS

d e

c i s

i o n

p r o

c e

s s

yes

no

difference d e

c i s

i o n

p r o

c e

s s

hardware for EIS

- poor mobility

- high cost

- long acquisition time

hardware for TCO

operation available

at reasonable cost

33 Nov. 19, 2013



MOS-IR measurement system:

Gas filled chamber (9 cm length)

MOS gas sensor (MICS 5131, e2v)

Transmission: Thermopile (HIS A21

F4.26, Heimann Sensors)

Ambient condition monitoring (p,

r.h./T)

Electronics controlled by a

microcontroller

Configuration settings set by a GUI

(LabVIEW)

Data evaluation offline using Matlab

K. Kühn et al.: Investigations on a MOX Gas Sensor as an Infrared Source for an IR-based Gas Sensing System, IMCS 2012

34 Nov. 19, 2013



Transmission signal, fA = 6 Hz square wave mod. of the MOS gas sensor (MICS 5131, SGX)

600k

800k

1M

1M

Transmission signal CO2 (Thermopile detector)

DF

T m

agnitude |Y

(fA)|

² (a

.u.)

0 5,000 10,000 15,000 20,000 25,000 30,0000

100

200

300

400

75 % r.h.

RT

CO2 C

2H

4 NH

3 NO NO

2 CO

Carr

ier

gas [ppm

]

Time (s)

1.2M

05k10k15k20k25k

CO

2 [ppm

]

K.

Küh

n e

t al

.: I

nv

esti

gat

ion

s o

n a

MO

X G

as S

enso

r as

an

Infr

ared

So

urc

e fo

r an

IR

-bas

ed G

as S

ensi

ng S

yst

em,

IMC

S 2

012

35 Nov. 19, 2013


> Conclusions

Gas sensor systems are more than just sensors

Sensors are highly sensitive, but often lack selectivity and

stability – especially for quantitative measurements

Intelligent multisensor/dynamically operated sensor systems

address existing drawbacks to broaden application spectrum

Application specific development still required

Field testing is a must for chemical sensor systems

Hardware for combined EIS-TCO currently in field test

Field tests of VOC-IDS sensor systems are starting now

Chemical sensor systems are complementary to analytical

techniques for Air Quality Monitoring

They can become ubiquitous due to low cost and portability

36 Nov. 19, 2013

LAB FOR MEASUREMENT TECHNOLOGY

Prof. Dr. rer. nat. A. Schütze

Thank you for your attention.

VOC-IDS

gas sensors and sensor systems for air quality monitoring workhop at j… · lab for measurement...

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