novel ionization methods for characterization of natural organic matter by fourier transform ion
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Electronic Theses, Treatises and Dissertations The Graduate School
2011
Novel Ionization Methods forCharacterization of Natural Organic Matterby Fourier Transform Ion CyclotronResonance Mass SpectrometryDavid Christopher Podgorski
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF ARTS AND SCIENCES
NOVEL IONIZATION METHODS FOR CHARACTERIZATION OF NATURAL
ORGANIC MATTER BY FOURIER TRANSFORM ION CYCLOTRON
RESONANCE MASS SPECTROMETRY
By
DAVID CHRISTOPHER PODGORSKI
A Dissertation submitted to the
Department of Chemistry and Biochemistry in partial fulfillment of the
requirements for the degree of Doctor of Philosophy
Degree Awarded:
Fall Semester, 2011
Copyright © 2011
David Christopher Podgorski All Rights Reserved
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David C. Podgorski defended this dissertation on August 11, 2011.
The members of the supervisory committee were:
William T. Cooper
Professor Directing Dissertation
Markus Huettel University Representative
Naresh Dalal
Committee Member
John G. Dorsey Committee Member
Alan G. Marshall
Committee Member
The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.
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To my family,
and
To my son,
Ezekiel Isaac Podgorski
whose unconditional love is my source of inspiration to strive for excellence.
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ACKNOWLEDGEMENTS
First and foremost, I thank Dr. William T. Cooper. He was
enthusiastic about me joining his group from the first day we met. Dr.
Cooper made me feel wanted when no one else seemed to want a student
from a small liberal arts university with an average G.P.A. and minimal
research experience. Furthermore, Dr. Cooper brought me in early and
immediately placed me on projects which emphasized his confidence in
my abilities. I was provided opportunities to travel to several different
countries and many conferences. There is no doubt that he and I had our
highs and lows and the occasional, rather, frequent falling out. Even
after our worst moments he came in the next morning with a positive
attitude and as if nothing happened when other advisors would have
asked me to leave their group. I have the utmost respect for him and am
grateful for all I was given.
I am most grateful to Dr. Alan Marshall for his willingness to
essentially invite me to become part of his group (unofficially). I will never
forget when he listed the grades on the board the day after our first mass
spectrometry test. If the grades were written in proportional intervals
from the top of the board, mine would have been under the building.
That was probably my all time low of graduate school. Yet, he never gave
up on me. He always made time to meet with me several times a week to
answer questions and with his help I survived. Even after I proved to him
how much I lacked in book smarts, he invited me to come out to the
Magnet Lab and find someone in his group to learn how to obtain quality
data. He invited me to come to subgroup meetings, group meetings at his
house, paid my way to conferences, and even provided support for my
last two semesters (unofficially). Dr. Cooper took the chance with me by
planting the seed, and Dr. Marshall provided the fertilizer, water, and
sunlight. Without either of them I would not be who I am today. Thank
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you, Dr. Marshall. I think the phrase I will never forget for the rest of my
life was sent to me by Dr. Marshall in an email. It said, “You need to
become intament with one of the members in my group”, referring to his
desire to have me shadow someone in his group to learn FT-ICR MS.
This brings me to Amy McKenna. Amy became my intament FTMS
partner. The only word I can use to describe Amy when I first started
coming to the Magnet Lab regularly is…….. I’ll chose not to use that
word, but she was not kind. A “dirt sprayer” invaded her world and there
was nothing she could do about it because I was sent by the boss. After a
month or so of sitting next to her day after day without a word spoken
between us, I think she realized that I was not going anywhere and that
maybe if she started talking to me about the instrument and all of the
different setting, etc. that maybe I would leave. Unfortunately for her, I
would nod my head and pretended like I had the slightest clue what she
was talking about, although I didn’t, and stuck around. Eventually over
time, I gathered the courage to ask questions and began to learn. Now, I
owe almost everything I know about FTMS, operation of the mass
spectrometer and APPI to Amy McKenna. I even think she is glad that
she gave me a chance, although the she was reluctant at best. Amy even
trusts me with her favorite baby and it’s not Charleigh, Sammy or Joey,
it’s her APPI source. Through everything Dr. McKenna and I are now
great friends. Our families spend time together and we talk trash about
hockey. Amy thanks for giving this “dirt sprayer” a shot and for teaching
me all that I know.
Thanks to all of the members of the Cooper group. Rasha, I always
enjoy you tough questions when one of us presents at group meeting.
Malak, I will always be angry and jealous that you give such phenomenal
presentations and English is not even your native language. O, keep
asking Dr. Cooper for money. Those are always fun conversations to
hear. You will get it one day, just stay persistent. To the Pollack at USF,
when is the next FAME Meeting? Dan, are you almost finished with that
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dissertation of Warchant? Alli, thank you for all of your support and
always being there for me. I would not have made it without you.
I thank Ryan Rodgers for his eventual acceptance of me. Ryan took
a little longer to come around than Amy, but has been a huge advocate
for me. Ryan, thank you for your help and support.
I have never met anyone who made me think as hard a Chris
Hendrickson. The thing that is most impressive about Chris is that he
makes you think without being in direct contact with him. I am going to
have a rubber bracelet made that reads, “WWCA”. What will Chris ask?
Before you even think you have something worthwhile to show Chris he
forces you to ask yourself questions that you normally would not think
about asking. Then once you think you have everything figured out and
can answer any questions he may ask, you show him the data and ten
questions come that bring you right back to Earth. My favorite thing
about Chris is that he shows now favoritism. Chris drills me the same
way he does, Amy, Ryan, or even Dr. Marshall. Thank you Chris for
making me a better analytical chemist.
John Quinn, for all of your patience and assistance over the years
and for teaching me the workings of a true instrumentation lab. There
was never a time where John said, I’m too busy to help you with your
(trivial) task, although I know he had 100 more important things to do.
John you have my utmost appreciation and gratitude. The same is true
for Nate Kaiser and Josh Savory. Thank you both for your time and help
when I was confused and frustrated
I would also like to thank the entire Marshall Research group for
accepting me as a part of your family. You could have made things a lot
more difficult for me; rather, you accepted me into your family and often
went out of your way to help when it was not necessary.
I thank Dr.s Brooks, Eddins and Totten for all that you did for me
at Withrow University. All of you do a phenomenal job with the little you
are provided from the university. I may have not had experience on
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advanced instrumentation, but I honestly can say that I could not have
been more prepared for what to expect in graduate school. You took an
ex-football jock that was very rough around the edges and polished him
into a research diamond. I thought that at best, I would finish school
with a B.S. and that would be a huge accomplishment. I never thought
that I could go on to earn a Ph.D. You provided me with the knowledge,
skills and confidence that have allowed me to succeed in graduate school
I would also like to thank my family. Mom, words cannot even
describe how much I love you. Without your unconditional love and
support who knows where I would be. You were always there for me and
never turned your back on me no matter how much I deserved it. You
made countless sacrifices that I am still realizing as an adult. You have
always been there to help me with Zeke and you are honestly the best
parent a child could ever hope to have. I hope that I can be half the
parent to Zeke and my children in the future as you have been to me.
Finally, Zeke. Zeke I hope you read this one day and realize that
you are what made me get out of bed some mornings. There were times
in this whole process when I wanted to give up, when the hours of work,
multiple jobs, and stress had worn me down to the breaking point. You
are the reason I started this road to earn a Ph.D. and you are the reason
why I finished it. I hope that you realize one day that all of the nights
when you did not see me, the mornings I was loading trucks at UPS, or
the weekends at the Magnet Lab were for our future. You are my
motivation in life and no matter how bad my day is at work or how tired I
am, you always bring a smile to my face. I love you Zeke and I will always
be there for you.
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TABLE OF CONTENTS
LIST OF TABLES ............................................................................ XII LIST OF FIGURES..........................................................................XIII ABSTRACT ................................................................................. XVIII
CHAPTER 1. ORGANIC MATTER ....................................................... 1 Dissolved organic matter ................................................................ 1
DOM ORIGIN AND COMPOSITION ..................................................... 2 DISSOLVED ORGANIC NITROGEN ..................................................... 4
Urea .............................................................................................. 5 DCAA ............................................................................................ 5
DFAA ............................................................................................. 5 Humic and fulvic substances .......................................................... 6 Additional DON compounds ............................................................ 6
BLACK CARBON ................................................................................ 7
BC formation ................................................................................. 7 CHARACTERIZATION OF NATURAL ORGANIC MATTER ................... 9
NMR spectroscopy ........................................................................ 11 EEMS .......................................................................................... 11 FT-ICR MS ................................................................................... 12
CHAPTER 2. CHARACTERIZATION OF DISSOLVED ORGANIC MATTER BY FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY ................................................................... 14
IONIZATION TECHNIQUES .............................................................. 15 Electrospray ionization ................................................................. 16 Atmospheric pressure photoionization ........................................... 18 Positive ion APPI ........................................................................... 19 Negative ion APPI ......................................................................... 20 Dopant-assisted APPI ................................................................... 21
FT-ICR MASS SPECTROMETRY: THEORY ....................................... 22
9.4 TESLA FT-ICR MASS SPECTROMETER AT THE NHMFL ........... 23 DOM ANALYSIS BY FT-ICR MASS SPECTROMETRY ........................ 24
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Kendrick mass sorting .................................................................. 25 Mass resolution ............................................................................ 27 Spectral complexity ...................................................................... 30 Isotope signatures ........................................................................ 31
Mass accuracy ............................................................................. 32 Dynamic range ............................................................................. 34
CONCLUSION ................................................................................... 36 CHAPTER 3. SELECTIVE IONIZATION OF DISSOLVED ORGANIC NITROGEN BY POSITIVE ION ATMOSPHERIC PRESSURE PHOTOIONIZATION COUPLED WITH FT-ICR MS ............................. 37
SUMMARY ....................................................................................... 37 INTRODUCTION ............................................................................... 37
Atmospheric pressure photoionization ........................................... 38 EXPERIMENTAL METHODS............................................................. 40
Samples ....................................................................................... 40
Mass spectrometry ....................................................................... 40 RESULTS AN DISCUSSION............................................................... 41
Lake Bradford DOM...................................................................... 42 Deep-sea marine DOM .................................................................. 44 Various DOM samples .................................................................. 46 Na+ adduct formation by (+) ESI .................................................... 47
CONCLUSION ................................................................................... 48 CHAPTER 4. APPI FT-ICR MS CHARACTERIZATION OF WASTEWATER-DERIVED DISSOLVED ORGANIC NITROGEN AFTER ADVANCED OXIDATION TREATMENT AND ALGAL BIOREMEDIATION ........................................................................... 50 SUMMARY ....................................................................................... 50
INTRODUCTION ............................................................................... 50 EXPERIMENTAL METHODS............................................................. 53
Samples ....................................................................................... 53 Advance oxidation treatment ......................................................... 54 Mass spectrometry ....................................................................... 54
RESULTS AND DISCUSSION ............................................................ 55 Untreated vs. treated DON ............................................................ 55
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Untreated and treated wastewater remediated by algae .................. 61 CONCLUSION ................................................................................... 67
CHAPTER 5. CHARACTERIZATION OF REACTIVE AND REFRACTORY DISSOLVED ORGANIC NITROGEN IN A STORMWATER TREATMENT AREA BY APPI FT-ICR MS ............................................................... 69 SUMMARY ....................................................................................... 69 INTRODUCTION ............................................................................... 70
EXPERIMENTAL METODS ............................................................... 72 Samples ....................................................................................... 72 Bioassays..................................................................................... 72 Extraction .................................................................................... 73 Mass spectrometry ....................................................................... 74
RESULTS AND DISCUSSION ............................................................ 75
Bioassay results ........................................................................... 75
Characterization of DON by APPI FT-ICR MS ................................. 76 Kendrick analysis ......................................................................... 79 van Krevelen analysis ................................................................... 76
CONCLUSION ................................................................................... 85 CHAPTER 6. CHARACTERIZATION OF PYROGENIC BLACK CARBON BY DESORPTION ATMOSPHERIC PRESSURE PHOTOIONIZATION
FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY ............................................................................. 87 SUMMARY ....................................................................................... 87 INTRODUCTION ............................................................................... 88 EXPERIMENTAL METHODS............................................................. 90
Samples ....................................................................................... 90 DAPPI source ............................................................................... 91 Mass spectrometry ....................................................................... 92 Data analysis ............................................................................... 94 Nuclear magnetic resonance spectroscopy ..................................... 95 Elemental analysis ....................................................................... 96
RESULTS AND DISCUSSION ............................................................ 96
Parent oak ................................................................................... 96 Oak combusted at 250 °C ............................................................. 98
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Oak pyrolyzed at 400 °C .............................................................. 100 DBE distribution ......................................................................... 102 Oxygen class distribution............................................................. 104
CONCLUSION .................................................................................105 REFERENCES ................................................................................107 BIOGRAPHICAL SKETCH...............................................................127
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LIST OF TABLES Table 1.1. Elemental Composition of SRFA, PLFA and NLFA ....................................... 4 Table 1.2. Properties of black carbon as a function of increased temperature .............. 8 Table 5.1. Three nitrogen-containing homologous series identified from m/z 432.00-
432.30. All three series exhibit the substitution of CH4 for O and the corresponding 36.4 mDa mass difference ....................................................................................79
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LIST OF FIGURES
Figure 1.1. Structure of black carbon as a function of increased temperature. Highly
aromatic char eventually forms planar graphite sheets. Graphite sheets may either for randomly distributed stacks or bowled structures ............................................ 9
Figure 2.1. Negative ion electrospray ionization FT-ICR broadband mass spectrum of
Lake Bradford DOM. Here, ions are detected for 5.6 s providing the resolving power needed to resolve and assign exact molecular formulas to the more than 15,000 peaks in the spectrum .........................................................................................15
Figure 2.2. Schematic of electrospray ionization (Figure from
www.bris.ac.uk/nerclsmsf/techniques/hplcms.html.). 1-2 kV are applied to the tip of a capillary. Ions desolvate and undergo a series of columbic explosions to form intact gas-phase ions ...........................................................................................17
Figure 2.3. Two-dimensional schematic of the APPI ion source coupled to the 9.4 Tesla
FT-ICR mass spectrometer at the NHMFL (Figure modified from Purcell et al. 2006). The krypton vacuum ultraviolet gas discharge lamp is drawn on the z-axis along with the heated metal capillary. In practice, the three assemblies are mutually orthogonal ...........................................................................................................19
Figure 2.4. Schematic of the 9.4 Tesla FT-ICR mass spectrometer located at the
National High Magnetic Field Laboratory, Tallahassee, Florida. Six stages of differential pumping are used to reduce the base pressure in the ICR cell to 10-10
Torr to minimize collisions between ions during excitation/detection. (Figure provided by the Marshall Research group courtesy of John Paul Quinn) ................25
Figure 2.5. Expanded mass spectral segments of Suwannee River fulvic acid produced
by negative ESI FT-ICR MS. 2.0157 Da spacings represent compounds that differ by two hydrogen atoms, indicative of compounds that differ by the addition of one non aromatic ring or double bond (DBE values). 14.01565 Da spacings (bottom) represent members of a homologous series which differ only in alkylation (CH2) ....27
Figure 2.6. Theoretical resolving power for FT-ICR mass spectrometry (Figure modified
from Marshall et al. 1998). Because of the complexity DOM, a minimum resolving power much be achieved to facilitate separation and correct identification of isobaric species. The 3.4 mDa split occurs between species with 36 Da nominal mass, but differing by SH4 and C3. The overlap between SH3
13C and C4 occurs between species weighing 48 Da ....................................................................................................28
Figure 2.7. Broadband positive ion APPI FT-ICR MS at 9.4 Tesla. 26,359 mass spectral
peaks above 6σ the signal-to-noise ratio baseline rms noise are observed from 400 < m/z < 1100 with m/∆m 50% = 900,000 at m/z 687, currently the world record for resolving power at 9.4 Tesla of a complex mixture.................................................29
Figure 2.8. Broadband negative ion APPI 9.4 T FT-ICR mass spectrum of Lake Bradford
DOM. More than 25,000 mass spectral peaks are resolved at 6σ baseline rms noise at an average resolving power, m/∆ 50% > 600,000. Inset: m/z ~ 0.1 expanded mass spectral segment that shows the spectral complexity of DOM ................................31
Figure 2.9. An m/z ~ 2 Da expanded mass spectral segment of negative ion APPI FT-
ICR mass spectrum of Lake Bradford DOM at m/z 423 showing the monoisotopic
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peak for [C20H23O10-H]- with corresponding 13C1 and 18O1 isotopic signatures. The signatures of heavy isotopes are used to confirm the assignment of molecular formulas ..............................................................................................................32
Figure 2.10. Internal calibration mass accuracy for more than 10,000 mass spectral
peaks observed at 10 times the signal-to-noise ratio baseline rms noise collected by APPI FT-ICR MS at 9.4 T for European crude. Calculation of the rms mass error for all observed peaks across 350 < m/z <1025 was 260 ppb .....................................34
Figure 2.11. Expanded mass spectral segment of Lake Bradford DOM by negative ESI
FT-ICR. The dynamic range of FT-ICR MS enables simultaneous detection of peaks with low and high signal-to-noise peaks (zoom inset) ............................................35
Figure 3.1. Broadband negative electrospray 9.4 T FT-ICR mass spectrum of Lake
Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 412.0 ..43 Figure 3.2. Broadband positive ion atmospheric pressure photoionization 9.4 T FT-ICR
mass spectrum of Lake Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 414.0 ..........................................................................................44
Figure 3.3. FT-ICR MS m/z expanded mass spectral segments for deep-sea marine
DOM produced by positive ion APPI (top) and negative ESI (bottom). For APPI, the most abundant have even nominal mass, e.g., [CcHhN1Oo + H]+. For ESI, the most abundant ions have odd nominal mass, e.g., [CcHhOo - H]- ....................................45
Figure 3.4. Two m/z ~ 0.01 expanded mass spectral segments for deep-sea marine
DOM produced by APPI (left) and ESI (right). Compounds with a common neutral formula were selected. Note that S/N ratio is more than 10-fold (top) or (5-fold (bottom) higher for APPI than ESI for the same neutral compound ........................46
Figure 3.5. Histogram depicting the percent relative abundances for all nitrogen-
containing species representative of five distinct DOM sources for positive ion APPI and negative ion ESI ............................................................................................47
Figure 3.6. An m/z ~1 expanded segment of the positive ion ESI 9.4 T FT-ICR mass
spectrum of SRFA. Each sodium adduct is separated by 2.4 mDa from the compound of the same nominal mass, but differing in composition by substitution of NaH for C2. The elemental compositions that contain Na are highlighted with an (*) ........................................................................................................................48
Figure 4.1. van Krevelen diagram of wasterwater-derived DON compounds before (top)
and after (bottom) treatment by AOP. AOP degrades the aromatic (yellow) and condensed aromatic (red) DON compounds .......................................................... 57
Figure 4.2. DBE vs. carbon number plots of wastewater-derived DON before (top) and
after (bottom) AOP. A shift to lower DBE is observed after AOP caused by degradation of aromatic compounds. The increase of compounds at low DBE and high carbon number may be the product of reactions between partially oxidized compounds ..........................................................................................................58
Figure 4.3. Class graph of the most abundant DON species in wastewater before and
after treatment. A shift to lower heteroatom number is indicative of degradation of large compounds .................................................................................................59
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Figure 4.4. van Krevelen diagrams of individual nitrogen classes before (top) and after (bottom) AOP. N2 and N3 compounds are removed after AOP. The removal of N2 and N3 compounds are consistent with degradation of large compounds by AOP. The N1 class remains mostly unchanged although there is a slight addition of compounds with high H:C and low O:C. Although N1 compounds are most likely degraded after AOP it is likely that degraded N2 and N3 compounds have the same composition as the original N1 compounds before AOP. Therefore, the N1 class has similar compositional coverage before and after AOP ........................................................ 60
Figure 4.5. van Krevelen diagrams of wastewater-derived DON untreated (black),
untreated after algal remediation (green), after treatment (blue), and after treatment and algal remediation (red). No change is observed for untreated wastewater before and after algal remediation. After treatment, formulas with relatively high H:C and low O:C are lost and formulas with low H:C and high O:C are added ..................... 62
Figure 4.6. van Krevelen diagrams of the formulas that appear only in treated
wastewater before algal remediation (top) and formulas only detected in the sample after algal remediation (bottom). Algae remove compounds with relatively high H:C and low O:C and release compounds with low H:C and high O:C........................... 63
Figure 4.7. Class graph of the most abundant nitrogen species in treated wastewater
before and after algal remediation. After remediation, a shift to higher oxygen class is observed. Furthermore, there is a significant decrease in the abundant N1O1 and N1O5 classes observed in the treated sample before algae ...................................... 64
Figure 4.8. FT-ICR MS enables characterization of individual heteroatom classes. A
shift to lower O:C of the N1O5 class is observed in the treated sample after algal remediation (bottom). The trend to lower O:C in the N1O5 class differs from the overall trend observed for all N classes; however, the compounds removed after remediation (top) are not aromatic in nature and may be bioavailable to algae.......65
Figure 4.9. Class graph of the oxygen species in treated wastewater before and after
algal remediation. There is an increase in relative abundance of O5-O12 classes indicative of a release of highly oxygenated DOC compounds by algae ...................66
Figure 4.10. van Krevelen diagram of DOC formulas unique to the treated sample
before (top) and after (bottom) algal remediation. The trend of removal of compounds with relatively high H:C and low O:C, and input of compounds with low H:C and high O:C see in the plots for DOC are similar to those observed for DON ...............67
Figure 5.1. Percentage of total assigned formulas verse nitrogen class comparison of
positive and negative ion atmospheric pressure photoionization in generating organic nitrogen ions ...........................................................................................77
Figure 5.2. a) Broadband positive ion APPI FT-ICR mass spectrum of Caloosahatchee
River DOM and; b) A m/z = 0.3 expanded mass spectral segment at m/z 432 with formulas containing N1 (●) and N3 (■) labeled .......................................................78
Figure 5.3. a) Kendrick plot of the nitrogen-containing formulas assigned in both the
original (T0) and incubated sample (T5) from the Caloosahatchee River during the wet season; and b) Kendrick plot of assigned formulas only in T0 (♦) and those only in T5 (■) ...............................................................................................................81
Figure 5.4. a) A van Krevelen diagram of nitrogen-containing formulas identified in
both the original (T0) and incubated sample (T5) from the Caloosahatchee River. B)
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A van Krevelen diagram of nitrogen containing formulas identified only in T0 (♦) and those only in T5 (■) ...............................................................................................84
Figure 6.1. Schematic of Thermo LCT source converted for DAPPI experiments (Figure
from modified from Purcell et al. 2006). The sample is placed directly the path of the heated solvent spray for thermal and chemical desorption. Desorbed neutrals undergo either direct photoionization, proton transfer, or charge exchange and enter the mass spectrometer through a heated metal capillary .......................................92
Figure 6.2. Broadband (-) DAPPI LTQ mass spectra of the parent oak, oak combusted
at 250 °C, and oak pyrolyzed at 400 °C. The optimum solvent plume temperature was determined for each sample. Top: The mass spectrum is typical of fresh, labile organic biomass. Middle and Bottom: The mass spectra exhibits a broad pseudo-Gaussian distribution as the biomass is thermally degraded. ................................93
Figure 6.3. van Krevelen diagram of elemental H:C vs. O:C ratios. Molecular formulas
with similar chemical characteristics tend to aggregate in specific regions. van Krevelen plots of different samples may be compared to determine changes in chemical composition. Formulas with Aromaticity Index (AI) values > 0.5 are considered aromatic, and those with AI > 0.67 condensed aromatic. .....................95
Figure 6.4. (Top) van Krevelen diagram for the elemental compositions assigned to
parent oak by DAPPI FT-ICR MS. The molecular formulas aggregate in regions of the diagram typical of wood, i.e., lignin, protein, and cellulose. A few formulas are associated with aromatic compounds, i.e., A.I. > 0.5. (Bottom) NMR spectrum for parent oak. The spectrum is dominated by the O-alkyl peak, 60-110 ppm, with only minor contribution from the aromatic peak, 110-160 ppm. (* bulk O:C and H:C ratios determined by elemental analysis) ..............................................................98
Figure 6.5. (Top) van Krevelen diagram for the elemental compositions assigned to oak
combusted at 250 °C by DAPPI FT-ICR MS. Molecular formulas characteristic of aromatic and condensed aromatics, i.e., AI > 0.5 and AI ≥ 0.67, are formed relative to the parent oak. Although the elemental compositions associated with proteins disappear relative to parent oak, compounds with high O:C and H:C associated with cellulose remain in oak 250. (Bottom) NMR spectrum of oak 250, showing a decrease in the O-alkyl peak and increase in the aromatic peak relative to the parent oak. (* bulk O:C and H:C ratios determined by elemental analysis) ........... 100
Figure 6.6. (Top) van Krevelen diagram of the molecular formulas assigned to oak
pyrolyzed at 400 °C. Molecular formulas exhibit lower O:C and H:C ratios relative to parent oak and oak 250, due to depolymeriztion of cellulose and dehydration and deactylation of lignin and cellulose. Approximately half of elemental compositions assigned for to oak 400 have an AI > 0.55. (Bottom) NMR spectrum of oak 400. The spectrum is dominated by the aromatic peak. There almost no O-alkyl contribution relative to the parent oak and oak 250. (* denotes bulk O:C and H:C ratios determined by elemental analysis) ...................................................................... 101
Figure 6.7. Double bond equivalents (DBE) relative abundance distribution for parent
oak, oak 250, and oak 400. The parent oak has a relatively low DBE range, and oak 250 exhibits a bimodal distribution. Oak 400 is characterized by elemental compositions with relatively high DBE, the result of further thermal degradation of lignin and cellulose ............................................................................................ 103
Figure 6.8. Percent relative abundance for various oxygen classes. Oak 250 has a
bimodal distribution with the first distribution, and formulas in the O4-O11 classes
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are most likely thermally degraded lignin and cellulose compounds. Formulas in the O12-O20 classes represent residual cellulose that is not thermally degraded and partially oxidized lignin and cellulose. Oak 400 formulas in the lower oxygen classes are the result of deactylation caused by thermal degradation .............................. 104
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ABSTRACT Natural organic matter (NOM) exists as a highly functionalized,
polydisperse and complex mixture of organic compounds derived from
decaying plan and animal detritus. NOM has been characterized by
Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR
MS) for approximately the past 10 years. Over that time advancements in
transfer optics and ICR cell technology have resulted in improvements in
sensitivity, dynamic range, mass accuracy, and signal-to-noise; however,
ionization techniques for NOM characterization have not improved
significantly. Typically, NOM is ionized by negative ion electrospray (ESI).
ESI is amenable to NOM characterization because the majority of NOM is
highly polar; however, important fractions of NOM are not ionizable by
ESI and are therefore remain uncharacterized at the molecular level.
The work presented is devoted to novel ionization methods for two
of the most under characterized fractions of NOM by FT-ICR MS.
Dissolved organic nitrogen (DON) may be selectively ionized by positive
ion atmospheric pressure photoionization. Typically, DON is not
characterized by FT-ICR MS because ESI does not efficiently ionize DON
relative to the C, H, and O component of NOM. Black carbon, including
biochar may be ionized by desorption atmospheric pressure
photoionization. Biochar has defied molecular level characterization by
FT-ICR MS because, as temperature of thermal degradation increases,
the solubility of char in common solvents decreases.
Chapter 1 is a brief introduction to natural organic matter
including a short overview of two major components of DOM that remain
largely uncharacterized at the molecular level, dissolved organic nitrogen
and black carbon. Chapter 2 is a brief introduction to FT-ICR MS
principles and establishes why FT-ICR MS is necessary for
characterization of complex mixtures such as DOM.
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In Chapter 3, positive ion APPI is established as a selective
ionization method for DON. DON has an important role in
biogeochemistry; however, it remains largely uncharacterized by FT-ICR
MS due to inefficient ionization relative to dissolved organic carbon
(DOC). Positive ion APPI dramatically increases S/N of DON ions
compared with negative ion ESI. Extensive molecular characterization of
DON may now be conducted, including tandem mass spectrometry to
reveal structural information about DON ions of a single m/z.
Chapter 4 and 5 are applications of positive ion FT-ICR MS for
characterization of wastewater-derived DON before and after treatment
by advanced oxidation processes and algal remediation, and
characterization of DON treated by microbes. An important factor in the
bioavailability of organic nitrogen is composition. Large, aromatic
compounds that are not available to algae for uptake in the untreated
sample are degraded to more labile compounds that are bioavailable.
Furthermore, labile DON may be used by microbes and converted to
refractory compounds.
Chapter 6 focuses on biochar, an important natural product for
the agricultural and fuel industries. Black carbon also represents a
significant long-term sink for atmospheric carbon. Characterization of
biochar is important for understanding how it interacts in the
environment. Many questions are yet to be answered about how char is
degraded after initial formation. To date, only the water-soluble fraction
of char is characterized at the molecular level by FT-ICR MS. As the
temperature of char formation increases, chars become insoluble in
common solvents. In Chapter 5, the implementation of desorption
atmospheric pressure photoionization (DAPPI) to characterize intact
chars is described. DAPPI is an ambient ionization method that does not
require sample preparation or separation. The elemental composition of a
parent oak, oak combusted at 250 °C, and oak 400 °C are determined by
DAPPI coupled to FT-ICR MS. The data show the parent material is
xx
mostly composed of lignin- and cellulose-like compounds. As the oak is
thermally degraded, the compounds become more aromatic. At 400 °C
the oak has lost all of its original identifiable components and is
composed of mostly aromatic compounds.
1
CHAPTER 1
ORGANIC MATTER
Natural organic matter (NOM) exists as a highly functionalized,
polydisperse and complex mixture of organic compounds derived from
decaying plant and animal detritus. Dissolved organic matter (DOM) is
operationally defined as the fraction of NOM that passes through a 0.2-
1.0 µm filter.1 DOM may be subcategorized into humic and nonhumic
substances. Nonhumic substances are those that contain biochemically
identifiable compound classes, e.g. simple sugars, fatty acids,
carbohydrates, and peptides.2-3 Humic substances are the
uncharacterized portion of DOM composed of individual compounds, i.e.
degraded plant and animal detritus. Humic substances are the
recalcitrant fraction of DOM that are resistant to additional microbial
degradation. Humic substances may be further classified as humic acids
(HA), fulvic acids (FA) and humin. HA is base-soluble and acid-insoluble,
FA is soluble at any pH, and humin is insoluble across the pH scale.
DOM interacts in many biogeochemical processes in marine and
terrestrial aquatic ecosystems, e.g. metal redox cycling, contaminant
transport, microbial growth, gas exchange in surface waters, and the
carbon cycle.4-10 The total amount of carbon stored in DOM represents
one of the largest reservoirs on Earth. Dissolved organic carbon (DOC)
accounts for an active carbon pool that is approximately equal to
atmospheric carbon dioxide (6.8 x 1017 g C).11-12
Due to its complex nature, DOM has defined complete molecular
characterization. Chromatographic methods such as reversed-phase
liquid chromatography and capillary electrophoresis are able to
characterize less than 10% of DOM as intact amino acids, sugars and
2
small aromatic and phenolic constituents. Size exclusion
chromatography may provide bulk information about molecular size and
polarity of DOM.13 Nuclear magnetic resonance spectroscopy only
provides information about functional groups associated with DOM.
Ultrahigh resolution Fourier transform ion cyclotron resonance mass
spectrometry provided the first insight about the composition of DOM at
the molecular level.14-18 Ultrahigh resolving power (m/∆m50% > 600,000 at
m/z 500) and sub-ppm mass accuracy enable the ability to assign
unique molecular formulas to thousands of DOM ions in an individual
mass spectrum.
DOM origin and composition
DOM is a product of several biogeochemical processes. The
composition of DOM is dependent upon these processes and the source
from which it is derived. Biomass produced from primary producers such
as terrestrial and aquatic plants, algae, and photosynthetic bacteria are
significant precursors for DOM. However, DOM is also derived from
secondary producers. Bacterial biomass produced by heterotrophic
bacteria in secondary production processes, exceeds plant biomass on
Earth.19 Furthermore, fungi are another secondary production source.
Therefore, DOM derived from secondary production sources is as
significant as DOM derived from primary production sources.
The proximately to the source is another important factor on DOM
composition. There are two classifications of DOM based on origin.
Autochthonous DOM originates at the source and allochthonous DOM is
derived far from a source and transported to the source by rivers,
streams, etc. Autochthonous sources of DOM are more significant in
samples where DOM inputs from allochthonous sources are minimal,
e.g., a lake with algal blooms. Allochthonous DOM is subject to more
degradation and removal processes during transport.
3
The trophic status of an aquatic environment affects DOM
concentration and composition. Aquatic systems may be either
eutrophic, oligotrophic, or mesotrophic. Eutrophic aquatic systems are
rich in plant nutrients and are therefore highly productive. Vast
quantities of suspended algae are present that serve as the base for
production of organic matter and subsequent formation of DOM. Due to
the productivity and subsequent decomposition of organic matter, much
of the oxygen is depleted at the lower depths from microbial respiration.
Eutrophic systems typically contain 10 mg L-1 DOM. Oligotrophic aquatic
systems are the contrast to eutrophic systems. Oligotrophic systems
contain very low concentrations of nutrients required for plant growth
and therefore the productivity of these systems is very low. Only small
quantities of organic matter are produced from minimal aquatic life
present in the system. Oligotrophic systems typically contain 2 mg L-1
DOM. Mesotrophic aquatic systems are the intermediate to eutrophic
and oligotrophic systems. Typical DOM concentrations are 2-4 mg L-1.
The major elements found in DOM include hydrogen, oxygen,
nitrogen; minor elements include halogens, sulfur, and phosphorus.
Table 1.1 shows the elemental compositions of Suwannee River fulvic
acid (SRFA), Nordic Lake fulvic acid (NLFA) and Pony Lake fulvic acid
(PLFA) provided by the International Humic Substances Society. The
Suwannee River flows through the forests and swamps of South Georgia
and North Florida before it empties into the Gulf of Mexico. The DOM is
rich in terrestrial derived DOM consisting of degraded lignin and tannin
with DOC concentrations ranging from 25 to 75 mg L-1. NLFA is isolated
from Lake Hellrudmyra located outside of Oslo, Norway. Lake
Hellrudmyra is a relatively small glacial lake (3.2 acres) located on the
side of a mountain. DOC concentrations range from 10 to 25 mg L-1.
Lake Pony is a saline pond located on Cape Royds in the McMurdo
Sound area of Antarctica. PLFA is formed entirely from lignin-free
biomass. The composition is representative of aquatic fulvic acids with
4
negligible input of terrestrial derived organic matter. Here, DOC
concentration may be very high, ~100 mg L-1. Although the amount of
carbon and hydrogen in each sample is similar, the amount of oxygen,
nitrogen, and sulfur differ drastically from the two aquatic systems with
input of terrestrial organic matter compared with the system without
terrestrial input, representative of the source dependency of DOM.
Table 1.1. Elemental Composition of SRFA, PLFA and NLFA.
Property SRFA NLFA PLFA
Elemental Analysis (wt%) Carbon 52.44 52.31 52.47
Hydrogen 4.31 3.98 5.31 Oxygen 42.20 45.12 31.38 Nitrogen 0.72 0.68 6.51 Sulfur 0.44 0.46 3.03 Phosphorus <0.01 <0.01 0.55
Dissolved Organic Nitrogen
Dissolved organic nitrogen (DON) represents less than 10% of the
total organic matter pool. Until recently, dissolved organic nitrogen (DON)
was believed to be largely unreactive, therefore, studies focused on the
dissolved inorganic fraction.20 However, it is now known that
approximately 60% to 69% of the total dissolved N in lakes, rivers, and
ocean waters is in the form of DON.21 Furthermore, a large fraction of
DON is bioavailable.22-26 There is a crucial need to understand the
composition and movement of DON through the biogeochemical cycle.
DON is a heterogeneous mixture of compounds composed of labile
functionalities which turn over on the order of days to weeks and
recalcitrant compounds which may exist for months to hundreds of
years.21 Labile DON is less abundant in the environment than
recalcitrant DON compounds; however, labile DON is far more important
5
as a source of N in the biogeochemical cycles of aquatic systems. A
number of compounds are identified within the DON pool including,
urea, dissolved combined amino acids (DCAA), dissolved free amino acids
(DFAA), humic and fulvic substances, and nucleic acids. The remainder
of the DON pool is a mixture of unidentified compounds.
Urea
Urea is a low molecular weight organic compound which is a
product of organic matter decomposition and excretion from organisms.
Concentrations of urea range widely from 0 to 3 µM. Open ocean systems
tend to contain the least urea (<0.3 µM). Coastal systems tend to contain
slightly higher concentrations (<0.7 µM). Estuary and river systems
contain the most urea (<3 µM). Urea is extremely labile with turnover
rates on the timescale of days.27
DCAA
The chemical structure of DCAA is largely unknown but can
include proteins and oligopeptides,28 amino acids bound to humic and
fulvic substances,29-30 and amino acids adsorbed to clays and minerals.31
DCAA are the largest identifiable pool of DON in aquatic systems.32
Concentrations range from 0.15 to 4.20 µM and represent approximately
7% of the total DON pool. The turnover rates for these for DCAAs are on
annual timescales.33
DFAA
Primary produces, many of which have large intracellular pools of
amino acids that may be released are the main source of DFAA in
aquatic systems.34-35 Diatoms show the highest rates of DFAA excretion
during exponential growth.36 Furthermore, the types of amino acids may
change in some diatoms with growth stage, e.g. exponential vs.
6
stationary growth.36 DFAA concentrations range from 0.001 to 0.7 µM
and account for approximately 5% of the total DON pool.
Humic and fulvic Substances
Humic substances are the recalcitrant and most hydrophobic
component of the DON pool. Humic substances isolated from aquatic
systems originate from either terrestrial or marine environments. Humic
substances of terrestrial origin are mostly aromatic and have a higher
C:N than marine humics which have a more aliphatic character.37 The
building blocks of terrestrial humic substances are mostly lignin-like
products while marine humics are believed to be biosynthetic
compounds such as amino acids, sugars, amino-sugars, and fatty
acids.38 The percentage of nitrogen ranges from 1 to 6% in humic
substances.39-41 It is unknown exactly how N is associated with humic
substances. Schnitzer suggested that there are two types of N
associations.39 The first group contains N compounds that have distinct
and identifiable characteristics, i.e. amino acids, amino sugars,
ammonium, nucleic acid and bases, and purines. The second group
includes compounds that have N integrated into the actual humic
compound. According to Schnitzer, the total DON associated with each
group accounts for 50% of the humic associated N; however, the first
group of compounds is the most likely source of bioavailable N.
Additional DON compounds
Nucleic acids, purines, pytimidines, pteridines, methylamines, and
creatine are identified in natural aquatic systems. Dissolved
deoxyribonucleic acids (D-DNA) and dissolved ribonucleic acids (D-RNA)
are produced when bacteria die. Purines and pyrimines are heterocyclic
bases. The major purine bases found in nucleic acids are adenine and
quinine and the major pytimide bases are thymine, cytosine, and
7
Uracil.21 Some purines and pteridines are excreted as end products of N
catabolism.42 Methyamines are produced by diatoms and come in
primary, secondary and tertiary methylated homologues of NH3.21 All of
the compounds mentioned in this section are minor constituents of the
total DON pool (<1%).21
Black Carbon
Black Carbon (BC) is a product of incomplete combustion of fossil
fuels and vegetation.43 Although there is no general agreement on a
clear-cut definition or boundary, BC may be understood as a continuum
from partly charred biomass through char and charcoal to graphite and
soot particles recondensed from the gas phase (Table 1.2).44 BC has
potential importance in a wide range of biogeochemical process. BC may
represent a significant sink in the global carbon cycle,45-47 effect the
Earth’s radiative heat balance,48-49 a tracer for Earth’s fire history,50-51 a
significant source of carbon buried on soils and sediments,52-56 and act
as an important carrier of organic pollutants or heavy metals.57-60
Charred particles from the burning of biomass and fossil fuel combustion
share a relative lack of biochemical reactivity and are therefore, strongly
resistant to decomposition over a geological timescale.
BC formation
Formation of BC may occur by two different processes. The
volatiles recondense to highly graphitized, sott-BC, whereas the solid
residues form char-BC. Soot-BC forms via small molecules that are
released by pyrolysis and subsequently recombine by free radical
reactions. The randomness of these reactions results in a characteristic,
but widely varying, spectrum of products, including polycyclic aromatic
hydrocarbons and graphitic moieties. Char-BC forms during the flaming
and smoldering phases of combustion, when oxygen reacts with carbon
8
that builds on solid fuel surfaces. At this stage, fuel gases produced by
pyrolytic reaction are insufficient to maintain the flame envelope, and
oxygen must diffuse to the fuel surface to maintain combustion.
Turbulence in the combustion zone enhances this transport and
oxidation at the fuel surface provides heat evolution and heat feedback to
accelerate pyrolytic reactions and volatilization of fuel gases.61-62 BC is
formed in an exothermic reaction at temperatures greater than 250 °C.
Table 1.2. Properties of black carbon as a function of increased
temperature.
Property Slightly Charred Char Charcoal Soot Biomass
Formation Low High Temperature Plant Structures Abundant Significant Few None Reactivity High Low Size mm and larger mm to submicron submicron
The chemical structure of BC is highly aromatic relative to the
parent biomass. Carbon may form different structures including planar
graphite structures, curved or possibly closed spheres, and randomly
oriented stacks of few graphitic layers (Figure 1.1).63 The short- and
long-term order of BC depends on combustion conditions such as
temperature and moisture content of the biomass. Slightly charred and
charred biomasses retain significant identifiable characteristics of plant
structures, while charcoal and soot do not.
9
Figure 1.1. Structure of black carbon as a function of increased temperature. Highly aromatic char eventually forms planar graphite sheets. Graphite sheets may either for randomly distributed stacks or
bowled structures.
Characterization of Natural Organic Matter
Dissolved organic matter exists as a polyfunctional, heterogeneous,
polyelectrolytic complex mixture with varying molecular weight and
concentration. Therefore, identification of individual components in DOM
poses a significant challenge with most analytical methods. Until
recently, analysis was limited to characterization of bulk properties or
limited fractions not representative of the entire sample. Bulk property
measurements are useful but are not true molecular descriptors because
10
there is not an average molecule that defines the entire DOM pool. Early
bulk measurements focused on DOC and DON quantification in
seawater. The first DOC and DON measurements were reported in 1934
by Krogh and Keys.64 A sulfuric acid hydrolysis method was applied to
quantify DON and wet oxidation in aqueous chromic acid to quantify
DOC.64 For the next 60 years what was known about DOM came from
bulk measurements.
It was not until the 1980s that efficient isolation 65-66 and methods
for advanced characterization were developed for DOM analysis,
particularly in the area of NMR spectroscopy. The first high quality 1H
and 13C NMR spectra of isolated marine DOM were published in 1983.67-
68 The spectra presented evidence for highly branched alkyl chains and
aromatic carbon as the major structural components of seawater DOM.
Advanced characterization methods revealed that NOM is composed of
compounds with identifiable classes e.g., lignin, amino acids, sugars,
proteins, and nucleic acids; however, these compounds are not
representative of the complete composition of DOM. A significant fraction
of NOM is termed “uncharacterized”. The uncharacterized fraction of
NOM is the portion that is significantly degraded and does not retain
chemical properties associated with one particular class.
A variety of analytical methods are now implemented to describe
the chemical properties of marine and terrestrial DOM including, low
resolution mass spectrometry and size-exclusion chromatography for
molecular weight distributions, elemental analysis and Fourier-transform
ion cyclotron resonance mass spectrometry for elemental composition,
nuclear magnetic resonance spectroscopy, and UV/Vis absorbance and
excitation emission matrix fluorescence spectroscopy for information
about the core composition and functional groups of NOM. No single
analytical technique produces both bulk and detailed molecular
information on DOM. The three most promising analytical techniques for
comprehensive DOM characterization are nuclear magnetic resonance
11
(NMR) spectroscopy, excitation emission matrix fluorescence
spectroscopy (EEMS), and Fourier transform ion cyclotron resonance
mass spectrometry (FT-ICR MS).
NMR spectroscopy
NMR is a common analytical method applied for structural
characterization of NOM. One-dimensional 1H and 13C NMR was first
applied to DOM in 1976 and yielded a spectrum consisting of many
unresolved peaks.69 Increases in resolution due to larger magnets and
new techniques have advanced the capability of NMR to produce data
that are useful for NOM characterization. Solid-state cross polarization
magic-angle spinning (CP-MAS) NMR was first used to characterized
marine and terrestrial DOM in 1983.70 The advent of CP-MAS NMR
provided the foundation for comparative analysis of marine and
terrestrial DOM signatures as a function of source and depth.71-73
EEMS Composition, concentration, distribution, and dynamics of the
fraction of DOM that absorbs light, chromophoric DOM (CDOM), may be
inferred from Fluorescence spectroscopy.74 CDOM from marine and
terrestrial sources is composed of aromatic rings and unsaturated
aliphatic chains. Therefore, a fluorescent method such as excitation
emission matrix fluorescence spectroscopy (EEMS) is useful for
characterization of CDOM. In EEMS, repeated emission scans are
collected at numerous excitation wavelengths, resulting in excitation-
emission matrices which provide highly detailed information that may be
used to identify fluorescent compounds in a complex mixture.75 Three-
dimensional plots of excitation and emission wavelength as a function of
intensity enable direct visualization of fluorophores in a DOM sample.76-
79 EEMS was applied to infer chemical composition,76, 80-86 environmental
12
interactions,87 and degradation88-92 of DOM from both marine and
terrestrial systems.74 Coble and Parlanti et al. determined marine DOM
to have marine-humic like and tryptophan-like fluorophores with varying
terrestrial characteristics depending on near-shore proximity and
depth.76, 93 Meanwhile, freshwater DOM from natural terrestrial sources
is predominantly composed of humic-like and fulvic-like fluorophores
with distinct maxima compared to marine DOM.76, 93-94 DOM from both
sources are affected by autochthonous and allochthonous contributions
and fluctuations in human activity. EEMS provides a high throughput
means to continuously monitor DOM in dynamic environments.
FT-ICR MS
Fourier transform ion cyclotron resonance mass spectrometry (FT-
ICR MS) is currently the only analytical method capable of achieving the
resolution and mass accuracy required to assign unique, unambiguous
molecular formulas to each peak across an entire molecular weight
distribution (200 < m/z < 1500). The application electrospray ionization
coupled to FT-ICR MS for DOM characterization began in the last decade
to typically characterize the carbon, hydrogen, and oxygen component of
DOM.15-18, 95 Advancements in sensitivity, mass range, mass resolving
power, m/∆m 50% > 600,000 at m/z 500),96 and part-per-billion mass
accuracy (<200 ppb)97 have increased the information inferred from
ultra-high resolution mass spectra. In the past decade, a typical DOM
spectrum contained ~5,000 resolved peaks. Currently, typical DOM
spectra contain greater than 15,000 resolved peaks which may be
assigned unambiguous molecular formulas. Information about the
chemical composition, aromaticity, and structure (tandem MS) may be
obtained from the custom-built 9.4 Tesla FT-ICR mass spectrometer
located at the National High Magnetic Field Laboratory, Tallahassee,
Florida.
13
The application of “novel” ionization methods coupled to FT-ICR
MS for characterization of NOM is the theme of this dissertation.
Historically, only the C, H, and O component of DOM was characterized
by FT-ICR MS. Other components of DOM such as nitrogen, which are
equally important in biogeochemical processes, are neglected due to
relatively low concentration and disparities in ionization efficiency. Still
other fractions of NOM, such as black carbon are insoluble in common
solvents and defy molecular level characterization by FT-ICR MS. Here, I
address these problems and explore methods to efficiently ionize and
characterize fractions of NOM that were uncharacterized or under
characterized. Furthermore, I apply these methods to characterize NOM
to studies that are topics of interest in the field of biogeochemistry.
14
CHAPTER 2
CHARACTERIZATION OF DISSOLVED ORGANIC
MATTER BY FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY
Dissolved organic matter is a complex mixture of components with
a variety of chemical and physical properties. The majority of DOM
species are polar, although water soluble compounds with hydrophobic
functionalities are also present. Various analytical methods, including,
gas chromatography-mass spectrometry (GC-MS), liquid
chromatography-mass spectrometry (LC-MS), fluorescence spectroscopy,
and nuclear magnetic resonance spectroscopy (NMR) have been utilized
in an effort to characterize DOM.98-99 These methods only enable
characterization of a small fraction of the total DOM pool. Fourier-
transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at
high magnetic field (> 9 Tesla) is the only analytical method that enables
characterization of DOM at the molecular level.
A typical DOM mass spectrum has more than 15,000 individual
spectral peaks that range from 200 <m/z < 1500 (Figure 2.1). The
assignment of individual molecular formulas to each spectral peak
requires ultrahigh resolution and high mass accuracies due to the
spectral complexity and close peak proximity. The 9.4 Tesla FT-ICR mass
spectrometer located at the National High Magnetic Field Laboratory
routinely provides resolving power (m/∆m 50%) >600,000 at m/z 500 and
200 ppb mass accuracy.
15
Figure 2.1. Negative ion electrospray ionization FT-ICR broadband mass spectrum of Lake Bradford DOM. Here, ions are detected for 5.6 s providing the resolving power needed to resolve and assign exact molecular formulas to the more than 15,000 peaks in the spectrum.
Electrospray ionization (ESI) is the most common ionization
method utilized for DOM analysis. 3, 15-17, 100-104 DOM is well suited for
ESI because ESI occurs at atmospheric pressure, ionizes a wide range of
polar, hydrophilic molecules with acidic and basic functional groups, and
may generate positive or negative ions. Recently, atmospheric pressure
photoionization (APPI) was implemented to ionize the less polar fraction
of DOM. Like ESI, APPI is a relatively soft ionization method that ionizes
an analyte based on its ionization energy and gas-phase acidity rather
than the pH of an analyte in solution.
Ionization Techniques
16
Electrospray ionization
John Fenn earned the Nobel prize in 2001 for electrospray
ionization. The applicability of ESI expanded rapidly because it is able to
transform analyte species in solution to free ions in the gas phase
continuously. Furthermore, multiple charges enable detection of large
compounds that are outside the analytical window of some mass
analyzers. Moreover, fragile compounds may be ionized without
fragmentation. Electrospray quickly became the ionization technique of
choice for large polar molecules and is the most common ion source used
to couple a liquid chromatograph to a mass spectrometer.
Figure 2.2 is a schematic of an electrospray source. Solution-
phase anions or cations, depending on the sign of the applied potential,
create charged droplets by application of an electric field. Dilute sample
solution is pushed by a syringe pump through a needle where 2-4 kV
electric potential is applied. As the drops evaporate, they reach their
Rayleigh limit. Gas-phase ions are produced after a series of Columbic
explosions. A small portion of the ions enter the mass spectrometer at
atmospheric pressure through a capillary that is coupled to the first
pumping stage of the instrument that is at a few mTorr of pressure.
17
Figure 2.2. Schematic of electrospray ionization. 1-2 kV are applied to the tip of a capillary. Ions desolvate and undergo a series of columbic explosions to form intact gas-phase ions. (Figure from www.bris.ac.uk/nerclsmsf/techniques/hplcms.html.)
Fievre et al., first applied electrospray ionization to FT-ICR MS for
characterization of humic and fulvic acids in 1997.95 The ability of
electrospray to ionize the most polar species in a complex mixture poses
a problem for characterization of the component of DOM that contains
other heteroatom classes besides C, H, and O. Negative electrospray
ionizes the C, H, and O component very efficiently because these
compounds are the most acidic species in a DOM mixture. Less polar
and basic species such as the N-containing component of DOM are not
ionized as efficiently. The nitrogen-containing compounds that are
ionized have very low signal magnitude relative to the compounds with
18
only C, H, and O. Therefore, nitrogen compounds are typically ignored
and left uncharacterized. It is critical to determine an ionization method
for characterization of the nitrogen species in DOM. Atmospheric
pressure photoionization (APPI) ionizes polar and nonpolar analytes, and
ionization is independent of the chemistry of an analyte in solution which
makes APPI an ideal candidate to efficiently ionize nitrogen species in
DOM.
Atmospheric pressure photoionization One of the main limitations of electrospray is limited ability to
efficiently ionize less polar and nonpolar species. Positive ESI was used
previously in an attempt to characterize the nitrogen component of DOM.
However, little information was gained and abundant sodium adducts
were present throughout the spectra. Atmospheric pressure
photoionization (APPI) is tolerant of salts, forms both positive and
negative ions simultaneously, and ionization is independent of the
chemistry of an analyte in solution. For these reasons, APPI is perceived
to be the method of choice for characterization of the N-containing
fraction of DOM.
Figure 2.3 is a schematic of the APPI source used at NHMFL. A
custom-built adapter was used to interface the APPI source to the first
stage of pumping in the mass spectrometer. The sample solution is
dissolved in methanol to 250 μg mL-1 and toluene (10% v/v) is added as
a dopant to assist in ionization. The sample is supplied to a fused silica
capillary by a syringe pump at a rate of 50 μL min-1. The sample mixes
with a nebulization gas, typically N2, at approximately 50 psi inside a
heated chamber. The nebulization temperature is controlled by an
external heating supply which can be operated between 200-500 ˚C .
Once nebulized, the sample exits the chamber as a confined jet and
passes orthogonally to a vacuum gas UV-Krypton discharge lamp where
19
photoionization occurs at atmospheric pressure. The ions are then swept
into the mass spectrometer through a resistively heated capillary into the
mass spectrometer.
Figure 2.3. Two-dimensional schematic of the APPI ion source coupled to the 9.4 Tesla FT-ICR mass spectrometer at the NHMFL (Figure modified from Purcell et al. 2006).105 The krypton vacuum ultraviolet gas discharge lamp is drawn on the z-axis along with the heated metal
capillary. In practice, the three assemblies are mutually orthogonal.
Positive ion APPI. APPI relies on the absorption of a photon by an
analyte causing the ejection of an electron and the formation of a
molecular radical cation (Eq. 2.1). Direct photoionization occurs if the
photon energy is greater than the ionization potential (IP) of the molecule.
The probability of this occurring is very low, since photons collide with
20
gases and other molecules in the source before they reach the analyte. As
a result, an easily photoionizable reagent called a dopant is added in
excess to increase ionization efficiency. Equations 2.2-2.4 are the
pathways of positive ion formation by APPI. Ionization of the dopant (Eq.
2.2) followed by subsequent charge exchange or proton transfer
increases ionization efficiency of the analyte. If the proton affinity of the
analyte is greater than that of the dopant, the analyte is ionized through
proton transfer to form [M+H]+ (Eq. 2.3). If the ionization potential of the
analyte is much less than that of the dopant, a molecular radical cation
is formed through charge exchange (Eq. 2.4). Although, some species in a
complex mixture form both protonated and radical ions, other
preferentially form one or the other which may provide additional
information regarding the nature and structure of the analyte.
2.1) M + hν → M+• Direct Photoionization if 10 eV > IP (M)
2.2) D + hν → D+• Photoionization of Dopant if 10 eV > IP (D)
2.3) D+• + M → [D-H]• + [M + H]+ Proton Transfer if PA (M) > Pa (D+•)
2.4) D+• + A → D + M+• Charge Exchange if IP (M) << IP (D+•)
Negative ion APPI. Negative ions are formed by an analyte with
either high gas-phase acidity, positive electron affinity, or both. An
analyte with one or both of these properties may be ionized in negative
ion mode by proton transfer to form a deprotonated molecule, or by
electron capture or charge exchange to form a molecular radical anion.
The process of negative ion formation is the same as positive ion APPI.
Equations 2.5-2.9 are the main pathways for negative ion formation by
APPI. Dopant molecules are photoionized which release an abundant
amount of electrons in the source (Eq. 2.5). Oxygen, which has a
relatively high electron affinity (EA), scavenges the electrons to form
superoxide radicals (Eq. 2.6). Proton transfer from an analyte to the
21
superoxide radical is possible if the proton affinity of the analyte is less
than the superoxide radical (Eq. 2.7). Charge exchange between the
analyte and superoxide radical is possible if the EA of the analyte is
greater than that of oxygen (Eq. 2.8). Finally, the analyte may capture an
electron if the EA of the analyte is greater than 0 eV (Eq. 2.9).
2.5) D + hν → D+• Photoionization of Dopant if 10 eV > IP (D)
2.6) O2 + e- → O2-• Formation of Superoxide
2.7) M + O2-• → [M-H]- + HO2
• Proton Transfer if PA (M) < PA (O2-•)
2.8) M + O2-• → M-• + O2 Charge Exchange if EA (M) > EA (O2)
2.9) M + e- → M-• Electron Capture if EA (M) > 0 eV
Dopant-assisted APPI. One of the main limitations of APPI is
that ionization occurs at atmospheric pressure, where collisions between
photons and atmospheric gases can occur and limit analyte ionization
efficiency. Robb et al. developed a technique called dopant-assisted APPI
to help increase analyte ionization through the addition of an easily
ionizable reagent in excess relative to the analyte.106 Benzene and
toluene are two commonly used dopants because they are photoionizable
with a 10 eV lamp, and have relatively high PA and IE. Other solvents
including acetone, anisole, substituted anisole, substituted benzene, and
tetrahydrofuran, have been used as dopants.
FT-ICR Mass Spectrometry: Theory In 1973, Alan Marshall and Melvin Comisarow combined Fourier
transforms, ion cyclotron resonance and mass spectrometry to create FT-
ICR mass spectrometry. A fixed magnetic field and an rf pulse applied
excited trapped ions to cyclotron motion through electrodes parallel to
the magnetic field. Coherent ion packets were excited close enough to
another pair of detection electrodes to induce an “image” current that
22
was measured as a time-varying differential voltage. Sinusoidal signals
were subjected to Fourier transformation after conversion from analog to
digital. The first FT-ICR mass spectrum was collected on methane ions
in 1973 at the University of British Columbia.107
Ion cyclotron motion occurs when a charged particle enters a
static, uniform, magnetic field. As an ion enters the magnetic field, it
encounters a force which bends the ion’s path into a circle. This is the
Lorentz force (FL), and the applied force on the ion is always
perpendicular to the ion motion and is expressed mathematically by Eq
(2.10), in which q is ion charge, v is ion velocity and Bo is magnetic field
strength.
FL = mass x acceleration = q v x Bo (2.10)
The cross product indicates that the force is perpendicular to the
velocity and the magnetic field. The angular acceleration of uniform
circular motion is shown in Eq. (2.11) where v and r are velocity and
radius.
a = v 2 / r (2.11)
Substituting Eq. (2.12) into Eq. (2.10)
m v 2/ r = q v Bo (2.12)
Angular velocity (ω) is equal to
ω r = v (2.13)
Substitution of Eq. (2.13) into Eq. (2.12) and simplification
produces the conventional form of the cyclotron equation Eq. (2.14)
where ω is the cyclotron frequency.
ω = q Bo / m (2.14)
23
A more useful form of the cyclotron equation is given in Eq. (2.15)
where vc is the cyclotron frequency in Hertz, Bo is the magnetic field
strength in Tesla, m is the ion mass in Da and z is multiples of
elementary charge.
zm
B101.535611
2πω
ν 0
7
cc
×== (2.15)
Ion cyclotron motion is independent of ion velocity and is what
makes ion cyclotron resonance a valuable attribute for mass
spectrometry.
9.4 Tesla FT-ICR Mass Spectrometer at the NHMFL
Figure 2.4 is a schematic of the custom-built FT-ICR mass
spectrometer equipped with a passively-shielded 22 cm room
temperature bore 9.4 Tesla superconducting magnet (Oxford
Instruments, Abingdon, Oxfordshire OX13 5QX United Kingdom)
controlled by a modular ICR data station. Ions generated at atmospheric
pressure in the external ionization region (ESI or APPI) enter the skimmer
region operated at ~2 Torr through a heated metal capillary into the first
rf-only octopole. Ions then pass through a quadrupole to a second rf-only
octopole where they are accumulated for ~50-1000 ms before they are
collisional cooled with helium gas and transferred through another rf-
only octopole to an open cylindrical Penning ion trap. Octopole ion guides
are operated at 2.0 MHz and 240 Vp-p rf amplitude. Broadband frequency
chirp excitation at a sweep rate of 50 Hz µs-1 accelerate the ions to a
detectable cyclotron orbital radius by the differential current induced
between two opposed electrodes within the ICR cell. Multiple (50-200)
time-domain acquisitions are summed for each sample, Hanning-
24
apodized, and zero-filled once before fast Fourier transform and
magnitude calculation.
DOM Analysis by FT-ICR Mass Spectrometry
FT-ICR MS is the only analytical method available to separate and
identify formulas for the tens of thousands of individual compounds
typically observed in a single mass spectrum of DOM. Mass spectrometry
techniques provided little information for characterization of DOM prior
to the 1990’s; however, in the past 10 years the technique has been
successfully applied to characterize organic matter at the molecular level.
FT-ICR MS was first applied to the analysis of humic and fulvic acids in
1997.95 Since then, FT-ICR mass spectrometry has become a standard
for molecular characterization of NOM. The study of the natural cycles is
called “Geomics” by some. The primary focus of Geomics is to determine
the origin and fate of organic molecules in the environment, e.g.
geochemical cycles. However, the challenge is that the beginning and end
points of these cycles are not well defined. Often there are many small
cycles within larger cycles. For example, to understand the role of
dissolved organic nitrogen (DON) in an estuary the elemental
composition of DON must be determined as it enters the estuary, as it
moves through various geochemical cycles within the estuary, and then
as it exits to the open ocean. Different biogeochemical processes may be
inferred based on the composition at each point. Understanding the
bioavailability of thermogenic organic matter (e.g. black carbon, biochar)
and determinig which fractions are inert it is another topical scientific
question that can be addressed by FT-ICR MS.
25
Figure 2.4. Schematic of the 9.4 Tesla FT-ICR mass spectrometer located at the National High Magnetic Field Laboratory, Tallahassee, Florida. Six stages of differential pumping are used to reduce the base pressure in the ICR cell to 10-10
Torr to minimize collisions between ions during excitation/detection. Figure provided by the Marshall
Research group courtesy of John Paul Quinn.
Kendrick mass sorting
Figure 2.5 includes two expanded mass spectral segments for
Suwannee River fulvic acid. A 30 Da expanded mass spectral segment
shows the spacing of 2.0157 Da which is compounds differing in
elemental composition by two hydrogens, equivalent to a increase or
decrease in one double bond equivalent (DBE). DBE represents the
number of double bonds or non aromatic rings (Eq. 2.7). A 100 Da
expanded mass spectral segment depicts the 14.01565 Da spacing
26
representative of members of a homologous series, differing in CH2 units
with the same heteroatom content and DBE (bottom).
DBE = C – 0.5H + 0.5N + 1 (2.16)
Even with high mass accuracy, assignment of correct molecular
formulas above m/z 400 becomes difficult because the number of
possible elemental combinations increase with higher m/z. Kendrick
mass sorting may be used to assign formulas to ions of higher m/z by
extending the mass range of a homologous series from low m/z to span
the entire molecular weight distribution. The Kendrick mass scale is
derived through normalization of molecular weights by the integer value
of the molecular weight of a CH2 unit (14.00000 Da versus the IUPAC
weight of CH2, 14.01565 Da) Eq (2.17).
Kendrick Mass = IUPAC Mass X (14.0000/14.01565) (2.17)
Complex natural mixtures, such as organic matter and crude oil, benefit
by using the Kendrick scale because compounds that differ only in the
degree of alkylation make up homologous series and may be sorted by
their Kendrick mass defect Eq. (2.18).
Kendrick Mass Defect = (exact Kendrick Mass –
Nominal Kendrick Mass) (2.18)
Kendrick normalization and Kendrick mass sorting then identify
homologous series that span the entire molecular weight distribution of a
sample. Accurate mass alone can assign elemental formulas up to m/z
400 and extension of the series allows for identification of all the other
members of that series. Kendrick mass sorting extends elemental
formula assignment to formulas up to nearly m/z 1400.
27
Figure 2.5. Expanded mass spectral segments of Suwannee River fulvic acid produced by negative ESI FT-ICR MS of. 2.0157 Da spacings represent compounds that differ by two hydrogen atoms, indicative of compounds that differ by the addition of one non aromatic ring or
double bond (DBE values). 14.01565 Da spacings (bottom) represent members of a homologous series which differ only in alkylation (CH2).
Mass resolution
Ultrahigh resolution (m/∆m 50% > 350,000, where ∆m 50% is the
magnitude mode mass spectral peak width and half-maximum peak
height) is essential for separation of isobaric species complex mixtures. A
minimum resolving power must be achieved in order to separate signals
from ions with the same nominal mass but differing in Kendrick mass.
For example, the 3.4 mDa split between isobars which differ in elemental
composition by SH4 vs. C3, both having a nominal mass of 36 Da. To
28
accurately assign compositions in complex mixtures, these species must
be resolved, and separation requires a minimum resolving power. The
sulfur species in DOM cannot be correctly assigned if the 3.4 mDa split
is not resolved. In other complex mixtures such as crude oil ionized by
APPI, the overlap between SH313C and C4 (1.1 mDa split) occurs between
a protonated and radical cation, both with 48 Da nominal mass. Correct
elemental assignment requires sufficient resolving power to separate and
identify these isobaric species.
Figure 2.6. Theoretical resolving power for FT-ICR mass spectrometry
(Figure modified from Marshall et al. 1998).108 Because of the
complexity of DOM, a minimum resolving power much be achieved to
facilitate separation and correct identification of isobaric species. The
3.4 mDa split occurs between species with 36 Da nominal mass, but
differing by SH4 and C3. The overlap between SH313C and C4 occurs
between species weighing 48 Da.
29
Figure 2.6 is the theoretical resolving power in FT-ICR MS and the
minimum resolving power required to separate the 3.4 mDa split and the
1.1 mDa split. Separation of the 1.1 mDa and 3.4 mDa isobaric overlap
is required to correctly assign elemental formulas to mass spectral
peaks.
Figure 2.7 Broadband positive ion APPI FT-ICR MS at 9.4 Tesla. 26,359 mass spectral peaks above 6σ the signal-to-noise ratio baseline rms noise are observed from 400 < m/z < 1100 with m/∆m 50% = 900,000 at m/z 687, currently the world record for resolving power at 9.4 Tesla of a petroleum sample.
Figure 2.7 shows broadband APPI FT-ICR MS at 9.4 Tesla of a
crude oil. 26,359 mass spectral peaks from 350 < m/z < 1000 were
observed at 6 times the signal-to-noise ratio baseline rms noise, at an
30
average m/∆m50% = 900,000 at m/z 687. The mass spectrum represents
the highest resolving power at 9.4 Tesla for a broadband mass spectrum
of a complex mixture by FT-ICR MS.
Spectral complexity
Spectral complexity may hinder correct identification of elemental
compositions if sufficient resolution is not achieved. Routinely, FT-ICR
MS of DOM results in more than 15,000 spectral peaks in a single mass
spectrum. As DOM weathers and ages, spectral complexity increases.
Furthermore, APPI ionizes polar and non polar species, and forms
protonated/deprotonated and radical molecular ions which increase
spectral complexity. Approximately 50 peaks per single nominal mass are
common for APPI spectra of DOM. Figure 2.8 shows a broadband
negative ion APPI FT-ICR mass spectrum at 9.4 tesla for DOM isolated
from Lake Bradford (Tallahassee, FL). The mass spectrum contains more
than 25,000 peaks (each with signal magnitude higher than at least 6σ
baseline noise) between 200 and 1000 Da, at a mass resolving power
m/Δm50% (in which Δm50% denotes the full mass spectral peak width at
half-maximum peak height) greater than 600,000 at m/z 500. Mass
spacings as small as 1.8 mDa (C2N13C vs. H3O3) are observed in this
particular mass spectrum.
31
Figure 2.8. Broadband negative ion APPI 9.4 T FT-ICR mass spectrum of Lake Bradford DOM. More than 25,000 mass spectral peaks are resolved at 6σ baseline rms noise at an average resolving power, m/∆50% > 600,000. Inset: m/z ~ 0.1 that shows the spectral complexity of DOM.
Isotopic signatures
To ensure that elemental compositions are assigned correctly,
isotopic signatures are used in conjunction with mass accuracy. One
commonly used isotopic signature is 13C. Since NOM is composed of
compounds containing carbon and hydrogen, the 13C peak may be
detected and identified for nearly every compound. The exact mass
difference between 12C and 13C is 1.0033 Da at an abundance of 1%;
therefore, once a molecular formula is assigned it can be further
validated from its 13C isotope. DOM also contains compounds with high
oxygen content. The 18O signatures may also be used to confirm a
32
molecular formula assignment. Isotopomers, compounds with the same
elemental composition differing by an isotope, such as 16O and 18O differ
in mass by 2.0042 Da at O.2% abundance and are routinely used in FT-
ICR MS to confirm molecular formula assignment. Figure 2.9 shows the
isotopic signatures for a compound containing ten oxygens, its 12C, 13C1,
16O, and 18O1 isotopomers.
Figure 2.9. An m/z ~2 expanded mass spectral segment of negative ion APPI FT-ICR mass spectrum of Lake Bradford DOM at m/z 423 showing the monoisotopic peak for [C20H23O10-H]- with corresponding 13C1 and 18O1 isotopic signatures. The signatures of heavy isotopes are used to confirm the assignment of molecular formulas.
Mass accuracy
Inside of the ICR cell, the act of trapping ions inside an
electrostatic cell shifts their natural cyclotron frequency slightly. A
33
frequency-to-m/z calibration can be applied to correct the m/z
measurement across the molecular weight distribution. The most widely
used calibration equation is shown in Eq. (2.19).
m/z = A/f + B/f2 (2.19)
A and B are constants that are obtained by fitting at least two ICR
frequencies of ions of known m/z to the equation. Internal calibration
produces mass accuracies of less than 1 ppm because calibrant and
analyte ions experience the same electric field inside the ICR cell during
detection. Internal calibration in a DOM mass spectrum is based on
calibration on a homologous, highly abundant alkylation series of ions
differing in mass by 14.01565 Da, the mass of a CH2 unit, across the
entire molecular weight distribution of the sample.
Internal calibration with Eq (2.19) yields mass accuracies between
100-400 ppb for complex mixtures and allows for unambiguous
elemental formula assignments. However, recently it was shown that the
application of a two term Eq. (2.20) or three term Eq. (2.21) “walking
calibration” routinely produces sub 100 ppb mass accuracy.97
m/z = Ai/f2 + Bi/f2 (2.20)
m/z = Ai/f2 + Bi/f2 + Ci*I/f2 (2.21)
34
Figure 2.10. Internal calibration mass accuracy for more than 10,000
mass spectral peaks observed at 10 times the signal-to-noise ratio
baseline rms noise collected by APPI FT-ICR MS at 9.4 Tesla for
European crude. Calculation of the rms mass error for all observed
peaks across 350 < m/z <1025 was 260 ppb.
Dynamic range
Dynamic range is the concentration range of an analyte over which
an analyzer responds linearly. In mass spectrometry, it is the ratio
between the largest and smallest signals simultaneously present in a
mass spectrum and allows measurement of the smaller signal to a given
degree of uncertainty. FT-ICR mass spectrometry has a high dynamic
range therefore making it uniquely sorted for complex mixture analysis,
since less abundant ions are able to be resolved along with highly
abundant ions in the same spectrum. Other techniques with a lower
35
dynamic range have difficulty identifying the less abundant species in a
sample.
Figure 2.11. An m/z ~ 0.25 expanded mass spectral segment of Lake Bradford DOM by negative ESI FT-ICR (bottom). The dynamic range of
FT-ICR MS enables simultaneous detection of peaks with low and high signal-to-noise peaks (top).
Often, the species of interest in DOM are those in low
concentration relative to the overall composition of the sample or
compounds that do not ionize as efficiently, e.g., nitrogen. Figure 2.11
visually represents the advantage of dynamic range across a 250 mDa
window of a FT-ICR mass spectrum. The peak with the greatest
magnitude is at m/z 411.129671 with a signal-to-noise ratio of 222 and
a -40 ppb mass error in elemental composition assignment. A 12 mDa
36
window shows three peaks above six times the baseline rms signal-to-
noise level with much lower signal to noise. However, these peaks are
detected and exact molecular formulas may be assigned with high mass
accuracy due to high dynamic range.
Conclusion
FT-ICR MS is a powerful technique for characterization of complex
mixtures. The enormous complexity of NOM thus makes it well-suited for
the characterization by FT-ICR MS. High mass accuracy and dynamic
range are needed to assign exact molecular formulas to the tens of
thousands of peaks in a single mass spectrum of DOM. High mass
accuracy alone can assign elemental compositions below ~400 Da.
Kendrick mass sorting exploits patterns in DOM and extends the upper
mass limit based on homologous series.
37
CHAPTER 3 SELECTIVE IONIZATION OF DISSOLVED ORGANIC
NITROGEN BY POSITIVE ION ATMOSPHERIC PRESSURE PHOTOIONIZATION COUPLED WITH FT-
ICR MS
Summary
Dissolved organic nitrogen (DON) comprises a heterogeneous
family of organic compounds that includes both well known biomolecules
such as urea or amino acids as well as more complex, less characterized
compounds such as humic and fulvic acids. Typically, DON represents
only a small fraction of the total dissolved organic carbon pool and
therefore presents inherent problems for chemical analysis and
characterization. Here, we demonstrate that DON may be selectively
ionized by atmospheric pressure photionization (APPI) and characterized
at the molecular level by Fourier transform ion cyclotron resonance mass
spectrometry. Unlike electrospray ionization (ESI), APPI ionizes polar and
nonpolar compounds, and ionization efficiency is not determined by
polarity. APPI is tolerant to salts, due to the thermal treatment inherent
to nebulization, and thus avoids salt-adduct formation that can
complicate ESI mass spectra. Here, for dissolved organic matter from
various aquatic environments, we selectively ionize DON species that are
not efficiently ionized by other ionization techniques, and demonstrate
significant increase of signal to noise for APPI relative to ESI.
Introduction
38
Although dissolved organic nitrogen (DON) is an important
component of aquatic ecosystems, most studies focus on dissolved
inorganic nitrogen because that fraction is directly available for biological
uptake.109-110 In fact, DON accounts for a considerable portion of the
total N pool in most environments and represents a potential bioavailable
source of N for phytoplankton and bacteria.21, 111-114 Historically, DON
was believed to be composed predominantly of refractory compounds
resistant to biological degradation.21, 115 However, more recent studies
suggest that DON may have a wide temporal range in turnover, ranging
from hours to decades.21 The roles of DON in the global nitrogen cycle
are identified in fields such as water purification, soil chemistry,
wastewater treatment, and atmospheric chemistry.116-119 Therefore,
complete molecular level characterization of DON is essential to develop a
more complete understanding of the global nitrogen cycle of DON as a
mixture of labile and refractory compounds with significantly different
roles.120-121
Previous molecular characterization of DON has been based on
negative electrospray ionization (ESI) or negative/positive-ESI coupled to
ultrahigh resolution mass spectrometry.122-128 However, ion formation in
ESI is based on the relative acidity (negative ESI) or basicity (positive ESI)
of the analyte in solution. Because DOM is composed of molecular
species with predominately highly polar, carboxylic functionalities,
oxygen-containing compounds are most efficiently ionized by negative
ESI and thus suppress ionization of less acidic species, e.g., nitrogen-
containing compounds.129 Basic nitrogen species may be selectively
ionized by positive ESI; however, less polar nitrogen species are not
efficiently ionized and Na+ adducts often complicate positive ESI mass
spectra.
Atmospheric pressure photoionization
39
Atmospheric pressure photoionization (APPI) is described in detail
elsewhere.130 Briefly, ionization relies on photon absorbtion, which
excites an electron and forms a molecular radical cation (direct
photoionization-see Equation 3.1).106 This process is referred to as direct
photoionization Eq. (3.1). The energy of the photon, hυ, must be greater
than the ionization energy of the analyte. However, in practice, the
radiation output of conventional krypton UV-lamps used for APPI
analysis is too low for efficient direct photoionization of analytes. As a
result, Bruins et al. developed dopant-assisted APPI,106 in which an
excess of photoionizable reagent, D, provides D+• ions (Equation 3.2) that
can react with analyte, M, by charge exchange or proton transfer to
generate M+• or [M+H]+ ions (Equations 3.3 and 3.4). Dopant-assisted
APPI can increase analyte ionization by 2-3 orders of magnitude.131
M + hν → M+˙ + e- (3.1)
D + hν → D+˙ + e- (3.2)
D+˙ + M → [M+H]+ + [D-H]˙ (3.3)
D+˙ + M → M+˙+ D (3.4)
Unlike ESI, in which the sign of the applied potential determine the
charge of the ion, APPI forms both positive and negative ions
simultaneously. As noted above, positive APPI ions are of two ion types,
M+• (if analyte ionization energy is far below that of the dopant) and
[M+H]+ (if the analyte proton affinity is higher than that of the
dopant.106,130 Here, we present data from a broad range of DOM pools to
demonstrate the advantages of positive ion APPI for selective ionization of
nitrogen-containing species, coupled with ultrahigh resolution Fourier
transform ion cyclotron resonance mass spectrometry (FT-ICR MS) for
40
determination of elemental compositions (CcHhNnOo) of thousands of
DON components.
Experimental Methods
Samples
A Suwannee River fulvic acid (SRFA) standard, obtained from the
International Humic Substance Society (IHSS), was diluted to a final
concentration of 500 μg mL-1 with HPLC grade methanol (Sigma-Aldrich,
St. Louis, Missouri). A deep-sea marine DOM sample was collected from
the sea floor of the Gulf of Mexico. Approximately 15 g of Tripsacum
floridanum (Gamma grass without stems or seeds) was dried thoroughly,
cut into 1x1x5 cm pieces and combusted in an oven at a heating rate of
10-12 ° C min-1 and held at a peak temperature of 250 °C for 3 h. Non-
rinsed biochar (1.5 g) was added to 35 mL of deionized water and shaken
for 4 days to obtain the water-soluble leachate. Another sample was
collected from the mouth of the Ochlockonee River, Florida, prior to its
terminus in the Ochlockonee Bay. Finally, a fifth sample was collected
from Lake Bradford, Florida. All samples except the SRFA were filtered
through 0.45 µm Whatman Polycap 150TC filter, acidified to pH 2, and
concentrated by solid-phase extraction as previously reported.132 Each
sample was further diluted with methanol to a final concentration of 500
µg mL-1 in 100% MeOH for mass spectral analysis. Toluene (Sigma-
Aldrich, St. Louis, Missouri) was added as a dopant directly to samples
(10% v/v) prior to APPI analysis.133
Mass spectrometry FT-ICR mass spectra were acquired with a custom-built FT-ICR
mass spectrometer with a passively shielded 9.4 tesla superconducting
magnet (Oxford Instruments, Abingdon, Oxfordshire OX13 5QX United
41
Kingdom) located at the National High Magnetic Field Laboratory,
Tallahassee, Florida.134 A modular ICR data acquisition station was used
for data acquisition, collection, and processing.135 Negative ions were
produced at atmospheric pressure by an external electrospray source
and positive ions were produced with an external atmospheric pressure
photoionization source.105, 136 Electrosprayed negative ions were
accumulated in the first of two radio frequency (rf)-only octopoles for
300-1000 ms. Positive ions produced by APPI were accumulated directly
into the second rf-only octopole (250-500 ms) prior to collisional cooling
with helium gas before transfer to an open cylindrical Penning ion trap.
Broadband frequency sweep ("chirp") excitation (~90-700 kHz at a sweep
rate of 50 Hz µs-1 and 400 V peak-to-peak amplitude at m/z 600)
accelerated the ions to a detectable cyclotron orbital radius. Multiple
(150-200) time-domain acquisitions were summed for each sample,
Hanning-apodized, and zero-filled once prior to fast Fourier transform
and magnitude calculation137 and detected frequencies converted to m/z
by the quadrupolar electric trapping potential approximation.138-139 Mass
spectra were internally calibrated from extended (20-30 peaks)
homologous alkylation series (compounds that differ in elemental
composition by integer multiples of CH2) of high relative abundance. An
average mass resolving power, m/∆m50% > 600,000 at m/z 500 with 100-
400 ppb mass error was achieved for all samples.
Results and Discussion Negative electrospray ionization (ESI) is routinely applied to
characterize DOM by FT-ICR MS.3, 16-17, 124, 140-142 The presence of
carboxylic acid moieties renders negative ESI especially efficient for
ionizing CcHhOo and CcHhOoSs compounds.133, 143 However, many
CcHhOoNn compounds are not ionized efficiently by negative ion ESI
42
(Figure 3.1). Although nitrogen-containing DOM compounds often have
carboxylic acid groups readily available for deprotonation by negative
ESI, the nitrogen may exist as a basic moiety or a relatively less polar
compound.144 Furthermore, nitrogen-containing compounds are always
present at lower concentration than CHO compounds in DOM.
Therefore, comprehensive characterization of DON by (-) ESI is not
feasible without prior separation. Here, we present data to demonstrate
the advantages of positive ion APPI for selective ionization of nitrogen-
containing species, coupled with ultrahigh resolution Fourier transform
ion cyclotron resonance mass spectrometry (FT-ICR MS) for
determination of elemental compositions (CcHhNnOo) of thousands of
DON components without prior separation of the nitrogen-containing
component.
Lake Bradford DOM We compared samples of marine- and terrestrial-derived DOM by
negative ion ESI and positive ion APPI to determine the selectivity of both
ionization types for nitrogen-containing species. Figure 3.1 is a
broadband FT-ICR mass spectrum and a 0.3 Da (all species reported
here are singly charged) mass-scale expanded region (insert) of a
terrestrial DOM sample ionized by negative ion ESI. The sample was
obtained from a fresh water lake (Lake Bradford, FL) for which the DOM
is primarily allochthonous detritus from a surrounding hardwood forest.
The most abundant ions are [CcHhOo - H]- of odd nominal mass. The
most abundant species of even nominal mass are [13C12Cc-1HhOo - H]-.
According to the "nitrogen rule", an even-electron ion (e.g., [M-H]-, as for
negative ESI) containing an odd (even) number of nitrogens will have
even (odd) mass.145
43
Figure 3.1. Broadband negative electrospray 9.4 T FT-ICR mass spectrum of Lake Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 412.0.
In contrast, the positive ion APPI FT-ICR mass spectrum of Lake
Bradford DOM exhibits a marked increase in the number of nitrogen
species (Figure 3.2). Although [CcHhOo + H]+ are the most abundant ions
throughout the mass spectrum, [CcHhOoN1 + H]+ are the most abundant
ions at even nominal mass.145 The four most abundant nitrogen-
containing species are labeled in each expanded segment of Figures 3.1
and 3.2. Five-nine fold higher S/N are observed by APPI for the two
parent compounds common to both ionization modes, C18H23N1O10 and
C19H27N1O9. Similarly higher APPI S/N of the nitrogen-containing ions
are observed across the entire mass spectrum.
44
Figure 3.2. Broadband positive ion atmospheric pressure photoionization 9.4 T FT-ICR mass spectrum of Lake Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 414.0.
Deep-sea marine DOM
A more dramatic increase in the S/N of DON ions from (+) APPI
relative to (-) ESI is observed for deep-sea marine DOM (Figure 3.3). In
fact, across an eight Da mass window, the most abundant ions formed
by (+) APPI are N1 species at even nominal mass, e.g., [CcHhN1Oo + H]+
(Figure 3.3 (top)). However, the same DOM sample analyzed by negative
ESI yields the most abundant ions at odd nominal mass, e.g., [CcHhOo -
H]- (Figure 3.3 (bottom)), with little to no signal from [CcHhN1Oo - H]-. It is
important to note that ions formed by positive ion APPI may be an even-
(e.g., [M+H]+) or odd-electron (e.g., M+•). However, one can in fact
45
distinguish even- from odd-electron nitrogen-containing ions based on
the calculated half-integer or integer DBE for the ion.146
Figure 3.3. FT-ICR MS m/z expanded mass spectral segments for deep-sea marine DOM produced by positive ion APPI (top) and negative ESI (bottom). For APPI, the most abundant have even nominal mass, e.g., [CcHhN1Oo + H]+. For ESI, the most abundant ions have odd nominal mass, e.g., [CcHhOo - H]-.
Next, we randomly chose two deep-sea DOM neutral compounds
ionized by APPI and ESI, to compare ionization efficiency (Figure 3.4).
C20H29N1O5 has a 13-fold higher S/N (Figure 3.4 (top)) and C16H21N1O5
has nearly a 6-fold increase in S/N with (+) APPI compared with (-) ESI
(Figure 3.4 (bottom)). The increase in S/N is particularly important for
isolation of ions of a single m/z for subsequent dissociation (MS/MS) to
46
provide structural information: e.g., as previously reported for (-) ESI FT-
ICR MS/MS of CHO compounds.147
Figure 3.4. Two m/z ~ 0.01 expanded mass spectral segments for deep-sea marine DOM produced by APPI (left) and ESI (right). Compounds with a common neutral formula were selected. Note that S/N ratio is
more than 10-fold (top) or (5-fold (bottom) higher for APPI than ESI for the same neutral compound.
Various DOM Samples
Deep sea marine DOM, Lake Bradford DOM, a DOM leachate from
combusted biomass (e.g. biochar, or black carbon), DOM from the
Ochlockonee River, and Suwannee River fulvic acid (SRFA) were
compared by positive APPI and negative ESI. Those samples represent a
wide variety of unique DOM pools, and all exhibit markedly higher
47
relative abundance for all nitrogen-containing classes by (+) APPI relative
to (-) ESI, e.g., CcHhN1-3Oo and CcHhN1OoS1 (Figure 3.5). The exception is
Suwannee River fulvic acid standard (SRFA). It should be noted that the
fulvic acid extract does not necessarily represent a comprehensive
molecular fraction of the DOM in the Suwannee River.
Figure 3.5. Histogram depicting the percent relative abundances for all nitrogen-containing species representative of five distinct DOM sources for positive ion APPI and negative ion ESI.
Na+ adduct formation by (+) ESI. Positive ion ESI has previously
characterized DON in algae and rainwater.128, 148 Although positive ion
ESI can access the more basic nitrogen species, Na+ adducts complicate
the spectrum without providing addition information, as illustrated by
the Na+ adduct species in the (+) ESI FT-ICR mass spectrum of Figure
48
3.6. Moreover, such an adduct ion frequently occurs at a mass
separation of only 2.4 mDa from an ion of identical composition except
for substitution of NaH for C2. We observe that (+) APPI of DOM yields [M
+ H]+ but not [M + Na]+. Moreover, less polar and non-polar nitrogen
species are not ionized by (+) ESI. Therefore, (+) APPI is recommended for
DON characterization because it does not complicate spectra with
unwanted adducts and ionizes nitrogen in all forms, i.e., basic, less-
polar, non-polar.
Figure 3.6. An m/z ~1 expanded segment of the positive ESI 9.4 T FT-ICR mass spectrum of SRFA. Each sodium adduct is separated by 2.4 mDa from the compound of the same nominal mass, but differing in composition by substitution of NaH for C2. The elemental compositions that contain Na are highlighted with an (*).
Conclusion
49
Changes in DON composition are paramount in understanding
DON cycling and environmental interactions. Although DON has an
important role in biogeochemical processes, DON remains largely
uncharacterized by FT-ICR MS due to inefficient ionization relative to
DOC. Here, for the first time, we show that positive ion APPI ionizes DON
more efficiently that negative ESI. Positive ion APPI coupled to FT-ICR
MS opens new doors for the molecular characterization of DON. DON
may be characterized without separation. Finally, High signal to noise
and relative abundance of DON peaks enable possible MS/MS
characterization of DON ions at a single m/z unlocking structural
information about the incorporation of nitrogen in DOM.
50
CHAPTER 4
APPI FT-ICR MS CHARACTERIZATION OF
WASTEWATER-DERIVED DON AFTER ADVANCED OXIDATION TREATMENT AND ALGAL
BIOREMEDIATION
Summary
Wastewater treatment facilities (WWTF) are equipped with
nitrification and denitrification systems that may decrease the
concentration of dissolved inorganic nitrogen (DIN) by more than 95%.
However, the removal of dissolved organic nitrogen (DON) is significantly
less efficient. DON accounts for approximately 65% of the total dissolved
nitrogen (TDN) in conventional WWTF effluent and may compose up to
80% of the TDN in effluent from WWTF with efficient nitrification-
denitrification systems. Previous studies suggest that anywhere from 2 to
70% of DON in surface water is bioavailable. The variability in DON
bioavailability in natural waters is most likely related to differences in
DON composition. Here, the elemental composition of DON in wastewater
before and after treatment by advanced oxidation processes (AOP) and
algal remediation was determined by positive ion atmospheric pressure
photoionization coupled to Fourier transform ion cyclotron resonance
mass spectrometry. The data show that AOP degrades DON compounds.
DON is mostly unavailable to algae prior to treatment by AOP. After AOP
degraded compounds are more labile and are available for uptake by the
algae.
Introduction
51
Wastewater-derived nitrogen accounts for approximately 12 to 33%
of the nitrogen pollution in rivers worldwide, while agriculture and
fertilizer runoff account for the remainder of the anthropogenic nitrogen
release to rivers.149 Wastewater treatment facilities (WWTF) are equipped
with nitrification and denitrification systems that may decrease the
concentration of dissolved inorganic nitrogen (DIN) by more than 95%;
however, the removal of dissolved organic nitrogen (DON) is significantly
less efficient. DON accounts for approximately 65% of the dissolved
nitrogen in conventional WWTF effluent and may compose up to 80% of
the total dissolved nitrogen (TDN) in effluent from WWTF with efficient
nitrification-denitrification systems.150
The relatively high contribution of DON to the TDN content of
treated wastewater effluent is significant for watershed protection
because most total daily load permits use TDN as the nitrogen parameter
and do not consider the possibility that DON and DIN may have different
potentials to cause cultural eutrophication. Both nitrate and ammonium
are known to stimulate primary production; however, the bioavailability
of DON is uncertain.42 Previous studies suggest that from 2 to 70% of
DON in surface water is bioavailable.25, 151-155 The variability of DON
bioavailability in natural waters is most likely related to differences in
DON composition. Free amino acids, urea, and nucleic acids are readily
available for uptake by heterotrophic bacteria and algae. DON in other
forms, such as humic substances, are not as available to support algal
growth in N-limited systems.114, 153, 156 However, photochemical reactions
in natural waters may convert DON that is not readily available to more
labile compounds such as primary amines152 or ammonia,157-158 although
photochemical reactions may also adversely affect the bioavailability of
DON.158
Although wastewater-derived DON is a significant contributor to
anthropogenic nitrogen input in receiving waters, there is a lack of
52
information about its bioavailability. Previously, the bioavailability of
wastewater-derived DON was determined to range from 0 to 60% by
measuring the uptake of nitrogen by activated sludge bacteria over 60
days.159 In another study, DON that was exposed to bacteria for 42 days
prior to algal uptake experiments, did not support algal growth.160 Two
factors may have resulted in underestimation of the bioavailability of
DON. First, gravimetric methods used for determination of algal growth
may not have been sensitive enough to detect small changes in algal
biomass. Second, bacterial uptake is needed to reincorporate dissolved
organic matter from primary production161 and evidence is provided that
bacteria play role in the availability of DON to algae in natural waters.153,
162-163 The absence of bacteria in the algal cultures used by Parkin and
McCarty may have underestimated the availability of wastewater-derived
DON to algae. The importance of bacteria in the wastewater-derived DON
cycle was also indicated by algal bioavailability experiments conducted in
the absence and presence of bacteria.164 Approximately 10% of the
wastewater-derived DON was available to algae in the absence of
bacterial compared with 60% in the presence of bacteria.
Wastewater-derived DOM may also serve as precursors to
disinfection by-products when wastewater is treated with chlorine.
Although disinfection of water with chlorine offers protection against
waterborne diseases, chlorination forms disinfection by-products (DBP)
which are linked to other illnesses such as cancer.165 Organic
compounds in water, including humic substances, amino acids, and
proteins, are known to form trihalomethanes (THM) and dihaloacetic
acids (DHAA) when treated with chlorine.166-168 In addition to acting as a
precursor for THM and DHAA, wastewater-derived DON may form a
variety of DBP with a nitrogen functional group such as haloacetonitriles,
cyanogen halides, and N-nitrosodimethylamine. Chlorinated wastewater
effluent is toxic to aquatic organisms and must undergo dechlorination
prior to discharge. DON affects the efficiency the chlorination and
53
dechlorination process because DON reacts with chlorine to form organic
chloramines.
DON is typically characterized by bulk property measurements.
These measurements do not provide information about the individual
nitrogen components in a complex DOM mixture. Recently, positive ion
atmospheric pressure photoionization (APPI) showed selective ionization
of the nitrogen-containing component of DOM.169-170 Here, untreated
wastewater and wastewater treated by advanced oxidation processes
(AOP), i.e., a combination of ultra-violet (UV) radiation and ozone are
characterized APPI coupled to a Fourier transform ion cyclotron
resonance (FT-ICR) mass spectrometer. AOP provide an alternative
method for disinfection of wastewater without formation of chlorinated
DBP. Furthermore, AOP may break down refractory organic compounds
such as humic substances to more labile compounds. To better
understand if wastewater DON is more bioavailable after AOP we added
algae to treated and untreated water and characterized the changes in
elemental composition by positive ion APPI FT-ICR MS.
Experimental Methods
Samples
A wastewater sample was collected from the secondary clarifier at
the Tallahassee Municipal Wastewater Treatment Facility, Tallahassee,
Florida in a 5 gallon carboy. Fats, oils, and grease are skimmed from the
surface in the secondary clarifier while solids settle to the bottom. The
secondary clarifier is prior to disinfection in the treatment process. The
sample was immediately filtered through a 0.7 μm Whatman GF/F filter.
An untreated and treated wastewater sample was inoculated with algae.
After 14 days, each sample (including treated and untreated wastewater
without algae) was filtered through a 0.2 μm Whatman Polycap TC150
54
filter and acidified to pH 2. Varian Bond-Elute PPL cartridges were used
for solid-phase extraction of DOM.132
Advance oxidation treatment
Approximately 1 L of filtered wastewater was treated for 90 min. by
UV radiation and ozone pumping chamber made from 4 inch diameter
PVC pipe. A Maxi-Jet 1200 submersible pump and power head is located
within the chamber for continuous circulation of the sample. Ozone was
pumped into the reaction chamber by an air-cooled corona discharge
ozone generator at a rate of ~0.33 g ozone min-1. A Mazzei injector
entrained ozone into the water column. The wastewater sample was
simultaneously irradiated with an 8-Watt, UV-C germicidal lamp inserted
down the center line of the reaction chamber. Aliquots were removed for
analyses at 15, 30, 60, 90, and 120 min. intervals to determine when the
sample was fully treated. Samples were considered fully treated when
absorption, measured by a Cary Varian 100 dual beam UV/VIS
spectrometer, no longer changed as a function of exposure time. It was
found that absorption did not change after 90 min. of exposure for any of
the samples.
Mass spectrometry
FT-ICR mass spectra were acquired with a custom-built FT-ICR
mass spectrometer with a passively shielded 9.4 tesla superconducting
magnet (Oxford Instruments, Abingdon, Oxfordshire OX13 5QX United
Kingdom) located at the National High Magnetic Field Laboratory,
Tallahassee, Florida.96 A modular ICR data acquisition station was used
for data acquisition, collection, and processing.135 Positive ions were
produced with an external atmospheric pressure photoionization
source.105, 136 Ions were accumulated directly into the second rf-only
octopole (250-500 ms) prior to collisional cooling with helium gas before
55
transfer to an open cylindrical Penning ion trap.136 Broadband frequency
sweep (“chirp”) excitation (~90-700 kHz at a sweep rate of 50 Hz µs-1 and
a 400 V peak-to-peak amplitude at m/z 600) accelerated the ions to a
detectable cyclotron orbital radius. Multiple (150-200) time-domain
acquisitions were summed for each sample, Hanning-apodized, and zero-
filled once prior to fast Fourier transform and magnitude calculation137
and detected frequencies converted to m/z by the quadrupolar electric
trapping potential approximation.138-139 Mass spectra were internally
calibrated from extended (20-30 peaks) homologous alkylation series
(compounds that differ in elemental composition by integer multiples of
CH2) of high relative abundance. An average mass resolving power,
m/∆m50% > 600,000 at m/z 500 with 100-400 ppb mass accuracy was
obtained for all samples.
Results and Discussion
Fourier transform ion cyclotron resonance mass spectrometry (FT-
ICR MS) is the only analytical method with the resolving power and mass
accuracy required to assign exact molecular formulas to each peak in a
mass spectrum of a complex mixture such as dissolved organic matter
(DOM). The elemental composition of the DOM extracted from untreated
wastewater, wastewater treated by advanced oxidation processes (AOP),
and untreated and treated wastewater after 14 days of algal growth is
determined by FT-ICR MS. As mentioned previously, reports show that
the bioavailability of DON is dependent on composition. Therefore, the
data presented specifically focuses on the changes in DON as a function
of treatment and algal growth.
Untreated vs. treated DON Molecular formulas for DON in untreated and treated wastewater
are shown in Figure 4.1. Prior to treatment, wastewater is composed of a
56
relative high amount of compounds with aromatic (yellow) and
condensed aromatic (red) character (top). The formulas reside in regions
of the diagram associated with lignin-, aminosugar-, protein-, and lipid-
like compounds.100 Although the bulk of the DON composition remains
the same, the aromatic compounds are removed after treatment by AOP
(bottom). The removal of aromatic compounds after AOP are consistent
with previous reports of DOM degradation by UV radiation124 and
ozone.171
Formulas for DON in treated and untreated wastewater are plotted
as a function of double bond equivalents (DBE) and carbon number
(Figure 4.2). Prior to treatment, wastewater has a DBE range from ~1
to18 and a carbon number distribution from ~11 to 30. After treatment
the maximum DBE decreases to ~13 and more formulas are formed at
lower DBE. Furthermore, there is a slight shift to lower carbon number;
however, compounds with low DBE, but high carbon number are formed.
A possible explanation for the formation of compounds with high carbon
number and low DBE may be that the compounds are products of
reactions between multiple partially oxidized DON compounds.
Although FT-ICR MS is only semi-quantitative, formula class
graphs are useful for visualizing differences in mass spectra. The
dominant DON heteroatom classes in untreated and treated wastewater
are shown in Figure 4.3. Wastewater-derived DON is predominately
composed of N1O4-9 and N2O4-7 classes. The most abundant class is the
treated and untreated sample is N1O5. The relative abundance of the
N1O5 class increases following AOP. Furthermore, an abundance of N1O1
species are formed. There is an overall decrease in relative abundance of
N1 and N2 classes after treatment.
57
Figure 4.1. van Krevelen diagram of wasterwater-derived DON compounds before (top) and after (bottom) treatment by AOP. AOP degrades the aromatic (yellow) and condensed aromatic (red) DON compounds.
58
Figure 4.2. DBE vs. carbon number plots of wastewater-derived DON before (top) and after (bottom) AOP. A shift to lower DBE is observed after AOP caused by degradation of aromatic compounds. The increase of compounds at low DBE and high carbon number may be the product of reactions between partially oxidized compounds.
Before AOP
After AOP
59
Figure 4.3. Class graph of the most abundant DON species in wastewater before and after treatment. A shift to lower heteroatom number is indicative of degradation of large compounds.
The decrease in relative abundance of N2 classes in Figure 4.3 is
believed to be indicative of degradation of larger DON compounds to
smaller more labile compounds. Figure 4.4 is a series of van Krevelen
diagrams for individual N1, N2, and N3 DON classes before (top) and after
(bottom) AOP. The van Krevelen diagrams for the N2 and N3 classes show
a significant loss of compounds after AOP. The majority of the elemental
composition of the N1 class remains relatively unchanged although there
is a loss of aromatic compounds (relatively low H:C and O:C) and a minor
increase of compounds with high H:C and low O:C. The relatively
consistency in N1 composition after AOP may be explained by the
degradation of N2 and N3 compounds to N1 compounds.
60
Figure 4.4. van Krevelen diagrams of individual nitrogen classes before (top) and after (bottom) AOP. N2 and N3 compounds are removed after AOP. The removal of N2 and N3 compounds are consistent with degradation of large compounds by AOP. The N1 class remains mostly
unchanged although there is a slight addition of compounds with high H:C and low O:C. Although N1 compounds are most likely degraded after AOP it is likely that degraded N2 and N3 compounds have the same composition as the original N1 compounds before AOP. Therefore, the N1 class has similar compositional coverage before and after AOP.
The data from untreated and treated wastewater show that large
and aromatic DON compounds are degraded by AOP. Presumably, the
compounds are degraded to labile compounds that may be available for
uptake by algae. The second phase of this work is to characterize
changes in DON composition of untreated and treated wastewater by
algal remediation.
61
Untreated and treated wastewater remediated by algae The elemental compositions of DON in untreated and treated
wastewater before and after 14 days of bioremediation by algae is
summarized in the van Krevelen Diagrams of Figure 4.5. The elemental
composition of untreated wastewater-derived DON remediated by algae
does not significantly change relative to the original untreated sample
(top). However, after AOP treatment and bioremediation, compounds with
relatively high H:C and low O:C are removed and those with relatively low
H:C and high O:C are formed. Figure 4.6 is a van Krevelen Diagram of
the DON formulas that are only present in the treated sample without
algae (top) and the DON formulas only present in the treated sample
after algal bioremediation (bottom). The algae uptake nitrogen
compounds with high H:C and low O:C and release DON compounds
with relatively low H:C and high O:C.
The relative abundance of DON compounds with relatively high
oxygen increase after remediation while DON with relatively low oxygen
decrease (Figure 4.7). The relative abundance of N1O1 and N1O5 classes
are significantly decreased after bioremediation. The formulas for the
N1O5 class are plotted in Figure 4.8. The trend of the N1O5 class is
different than the trend observed for all DON compounds. The N1O5
compounds shift to lower O:C and remain at a relatively constant H:C
after algal remediation. The shift to lower O:C is not easily explained;
however, the compounds that are removed after remediation (top) are not
aromatic in nature and therefore may be available to algae.
62
Figure 4.5. van Krevelen diagrams of wastewater-derived DON untreated (black), untreated after algal remediation (green), after treatment (blue), and after treatment and algal remediation (red). No change is observed for untreated wastewater before and after algal remediation. After treatment, formulas with relatively high H:C and low O:C are
lost and formulas with low H:C and high O:C are added.
63
Figure 4.6. van Krevelen diagrams of the formulas that appear only in treated wastewater before algal remediation (top) and formulas only detected in the sample after algal remediation (bottom). Algae remove compounds with relatively high H:C and low O:C and release compounds with low H:C and high O:C.
64
Figure 4.7. Class graph of the most abundant nitrogen species in treated wastewater before and after algal remediation. After remediation, a shift to higher oxygen class is observed. Furthermore, there is a significant decrease in the abundant N1O1 and N1O5 classes observed in the treated sample before algae.
Although the work here is devoted to characterization of DON, it is
important to note the changes in dissolved organic carbon. The relative
abundance of O5-O12 classes drastically increase after remediation
compared with the treated sample before remediation (Figure 4.9). The
increase implies that a significant amount of highly oxygenated DOC
compounds are released by the algae. Figure 4.10 shows that the oxygen
compounds removed (top) and added (bottom) follow a similar trend to
the DON compounds. The data show that algae are not only significant
participants in the organic nitrogen cycle, but also participate in the
carbon cycle as well.
65
Figure 4.8. FT-ICR MS enables characterization of individual heteroatom classes. A shift to lower O:C of the N1O5 class is observed in the treated sample after algal remediation (bottom). The trend to lower
O:C in the N1O5 class differs from the overall trend observed for all N classes; however, the compounds removed after remediation (top) are not aromatic in nature and may be bioavailable to algae.
66
Figure 4.9. Class graph of the oxygen species in treated wastewater before and after algal remediation. There is an increase in relative abundance of O5-O12 classes indicative of a release of highly oxygenated DOC compounds by algae.
67
Figure 4.10. van Krevelen diagram of DOC formulas unique to the treated sample before (top) and after (bottom) algal remediation. The trend of removal of compounds with relatively high H:C and low O:C, and input of compounds with low H:C and high O:C see in the plots for DOC are similar to those observed for DON.
Conclusion
Here, we show that positive ion APPI coupled to FT-ICR MS enables
characterization of wastewater-derived DON before and after AOP and
algal remediation. AOP degrades large compounds that are refractory in
the environment into labile compounds. Further evidence is provided
that AOP beaks apart N2 and N3 compounds into smaller N1
components. Some of these degraded compounds are then bioavailable to
algae for uptake. Undoubtedly, algae continuously remineralize DON in
68
untreated aquatic systems. However, it is difficult to discern which
compounds are removed and released by algae in untreated water. After
treatment, it became clear which compounds were used and released by
the algae. Furthermore, we show that algae not only use labile DON, but
also labile DOC, further emphasizing the key role of algae in the organic
nitrogen and organic carbon cycles. As suggested by others,
bioavailability of DON to algae is dependent on DON composition. AOP
degrades DON that is not available to algae to more labile compounds
that are bioavailable.
69
CHAPTER 5
CHARACTERIZATION OF REACTIVE AND
REFRACTORY DISSOLVED ORGANIC NITROGEN IN A STORMWATER TREATMENT AREA BY APPI FT-
ICR MS
Summary
Dissolved organic nitrogen (DON) represents a significant fraction
of the total dissolved nitrogen pool in most surface waters. While
traditionally phytoplankton and bacteria were thought to prefer inorganic
nitrogen as a substrate, recent work indicates that DON also serves as
an important nitrogen source. We have coupled atmospheric pressure
photoionization (APPI) with ultrahigh resolution Fourier transform-ion
cyclotron resonance mass spectrometry (FT-ICR MS) to examine DON
from the Caloosahatchee Estuary located in southwest Florida as part of
a larger bioassay study to determine DON bioavailability. FT-ICR mass
spectra were obtained on samples before and after a 5-day bioassay with
natural microbial communities. Positive ion APPI FT-ICR MS yielded
more nitrogen-containing DOM ions than negative ion APPI, an
indication that positive APPI selectively ionizes DON. Less than 5% of
DON was removed after 5 days of microbial exposure. Mass spectral data
confirmed that the majority of formulas found in the original sample were
also present in the degraded sample, and most of these compounds,
which we define as refractory, had molecular compositions representative
of lignin-like molecules. In contrast, lipid-like and protein-like molecules
were the primary compounds removed from the original sample during
70
the bioassay, suggesting that they may be the small reactive component
of the DON pool.
Introduction
Transport of nitrogen to coastal waters leads to coastal
eutrophication, often resulting in harmful algal blooms, reductions in
submerged aquatic vegetation, hypoxia, and anoxia.172 The
Caloosahatchee River, in southwest Florida, USA, transports water,
nutrients, and suspended solids from Lake Okeechobee and the Northern
Everglades Watershed to the Caloosahatchee River Estuary and then into
the Gulf of Mexico. This region of the Gulf is plagued with annual blooms
of the red tide forming dinoflagellate, Karenia brevis.173 Karenia brevis,
like many harmful algal bloom species, uptake dissolved organic nitrogen
(DON).174 Riverine input of nitrogen to the Gulf of Mexico may support
near shore blooms and some suggest that the reduction of terrestrial
derived nitrogen sources may aid in reducing red tides.
The South Florida Water Management District is developing a
water quality treatment area (WQTA) to demonstrate a wetland-based
technology for reducing dissolved nitrogen in the Caloosahatchee River,
of which DON comprises approximately 90% of the total dissolved
nitrogen (TDN). Unlike treatment of dissolved inorganic nitrogen (DIN),
where successful treatment results in dramatic reduction of the
concentration of DIN, DON reduction is feasible only to some background
refractory concentration. The design of nitrogen WQTA’s must thus focus
on; 1) removal of DON by sequestering it into particles that will
eventually sink and be removed from the system or 2) transformation of
DON into recalcitrant forms that will not be further degraded while
within the river or coastal zone.
Although DON plays an important role in microbial productivity,
over 75% of the total marine DON pool remains uncharacterized at the
71
molecular level.175 Characterization of DON at the molecular level
enables identification of DON compounds based on molecular formula,
and insight into the types of DON compounds that are bioavailable or
refractory. Therefore, designing a WQTA requires molecular-level insight
into DON bioavailability and the development of cost-effective methods to
determine the specific transformations of DON that occur here.
Molecular-level characterization of DON for the work presented
was accomplished by atmospheric pressure photoionization (APPI)
Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR
MS). The DON component of natural organic matter (NOM) is extremely
complex and has resisted molecular-level characterization because of a
lack of suitable analytical methods.175 FT-ICR MS is a powerful method
for advanced characterization of dissolved organic matter (DOM).3, 15, 104,
176 Recent reports suggest that FT-ICR MS has significant potential for
the molecular-level characterization of DON in a variety of ecosystems.133,
177-179
Unlike electrospray ionization (ESI), APPI may ionize non-polar and
polar analytes by proton transfer or charge exchange between a
photoionized dopant (e.g., toluene) and an analyte.131 Furthermore, APPI
is more resistant to chemical noise from solvents and salts (particularly
beneficial for DOM characterization), and exhibits less ion suppression
from matrix effects.131, 180 In a recent study of DOM, we noted that APPI
generated nitrogen-containing ions with significantly higher S/N relative
to negative ESI, providing evidence that positive ion APPI may be more
appropriate for studies of DON bioavailability than negative ESI.169
Here, we conduct a detailed study of DON composition and
bioavailability in waters sampled from the Caloosahatchee River and
adjacent stormwater treatment areas. Bulk DON bioavailability was
assessed through bioassays with inoculum from downstream, mid-
salinity (salinity of 15) sites in the Caloosahatchee River. Sampling was
carried out at the end of the dry (April/May) and during the wet (end of
72
June) seasons to determine if “fresh” DON washed into the river during
the rainy season was more bioavailable than “older” DON that had
accumulated during the dry season.
Experimental Methods
Samples
Water samples were collected by Professor Deborah Bronk from the
Virginia Institute of Marine Sciences. Waters from the Caloosahatchee
River and adjacent stormwater treatment areas in southwest Florida
were sampled during the dry season on April 29, 30 and May 1, 2009,
and the wet season on June 23, 24, and 25, 2009. In this report we will
focus on data from the northern Caloosahatchee River C-43 site, located
at 26.79199° N, 81.29789 W. Samples obtained during both seasons
were handled in a similar fashion. The water was collected in acid
washed (10% HCl) 9 L polycarbonate carboys and stored in the dark and
kept cool to limit biological activity during transportation. The water was
filtered within through a Pall Life Sciences 142 mm A/D binder-free glass
fiber filter (GFF), which was pre-combusted for 2 hours at 450°C. A
similarly pre-combusted Whatman 142 mm GF/F was used as a second
filtration and the final filtration step was with a pre-cleaned Pall Gelman
Acropak 500 0.2 µm capsule filter. The resulting filtrate was stored
refrigerated (~4oC) in the dark until use in bioassay experiments.
Bioassays Bioassays were carried out at the Virginia Institute of Marine
Sciences under the supervision of Professor Bronk. The water from the
sampling site was fresh. However, the interest of this study was to
determine the bioavailability of the DON downstream of the sampling
site. Therefore, the inoculum was collected from a site on the river with a
73
salinity of 15, and was subsequently passed through 150 µm mesh to
remove grazers. The salinity of the fresh water sampling sites was
increased to 15 with pre-combusted sodium chloride, magnesium
sulfate, and sodium bicarbonate. Once the salinities were properly
adjusted, incubations were set up using 400 mL of site water and 125
mL of inoculum. The initial samples (designated T0) were filtered with two
Whatman 25 mm GF/F, which were pre-combusted for 2 hours at 450°C,
immediately after the inoculum was added, and the filtrate was stored in
the dark at 4°C. Samples were incubated on a 12:12 light:dark cycle at
ambient temperature, which was 24°C in the dry season and 26°C in the
wet season. After 120 hours, the samples (designated T5) were removed
from the incubators and filtered as described for the T0 samples. Some of
the filtrate was collected in polypropylene tubes for later analysis of total
dissolved nitrogen (TDN), ammonium (NH4+), nitrite (NO2
-), and nitrate
(NO3-) concentrations. Additional filtrate was stored in high-density
polyethylene (HDPE) bottles, which was washed with 7% HCl and de-
ionized water, before the extractions for APPI FT-ICR MS analyses.
Extraction DON in water samples was extracted for FT-ICR MS analysis with
100 g, 1 mL Varian Bond Elut PPL solid phase extraction (SPE)
cartridges that were first rinsed with HPLC grade methanol.132
Optimization experiments were carried out to determine the effect of
acidity on DON extraction efficiency. Each sample was extracted after; (1)
addition of concentrated HCl to a final pH ≤ 2.5, (2) addition of NH4OH to
a final pH of > 10, and (3) no pH adjustment. Analysis of subsequent FT-
ICR mass spectra indicated that the neutral extraction was slightly
superior, although the differences between methods were within the
experimental variability (~4%) of the overall method. However, the acid
extraction is surprisingly efficient at isolating organic-N, with ~ 22% of
74
the assignable compounds containing nitrogen. Because the acid
extraction greatly increases the total number of compounds we observe
(i.e. those that do and do not contain N) we chose the acid extraction
method.
Prior to extraction each acidified sample was pre-filtered through a
sequence of 3.0 and 0.2 μm Nuclepore filter cartridges. One L of water
was pumped through the SPE PPL cartridge at a flow rate of <50
mL/min. The cartridges were rinsed with 100 mL of ultrapure water at
pH 2 to remove any remaining salts, dried with a stream of N2 and eluted
with 30 mL of HPLC grade methanol at a flow rate of <10 mL/min. DON
extractions were stored in the dark at -18ºC. The quantitative reliability
of this method for extracting DOM is estimated to be ~60% for DOM in
fresh water,132 but there is no data available on DON extraction
efficiency. Toluene (10 v/v) was added as the APPI dopant to the SPE
extracts prior to FT-ICR MS analysis.
Mass Spectrometry FT-ICR mass spectra were acquired with a custom-built FT-ICR
mass spectrometer with a passively shielded 9.4 tesla superconducting
magnet (Oxford Instruments, Abingdon, Oxfordshire OX13 5QX United
Kingdom) located at the National High Magnetic Field Laboratory,
Tallahassee, Florida.96 A modular ICR data acquisition station was used
for data acquisition, collection, and processing.135 Ions were produced
with an external atmospheric pressure photoionization source105 and
accumulated directly into the second rf-only octopole (250-500 ms) prior
to collisional cooling with helium gas before transfer to an open
cylindrical Penning ion trap. Broadband frequency sweep ("chirp")
excitation (~90-700 kHz at a sweep rate of 50 Hz µs-1 and a 0.75 V peak-
to-peak amplitude) accelerated the ions to a detectable cyclotron orbital
radius. Multiple (150-200) time-domain acquisitions were summed for
75
each sample, Hanning-apodized, and zero-filled once prior to fast Fourier
transform and magnitude calculation137 and detected frequencies
converted to m/z by the quadrupolar electric trapping potential
approximation.138-139 Mass spectra were internally calibrated from
extended (20-30 peaks) homologous alkylation series (compounds that
differ in elemental composition by integer multiples of CH2) of high
relative abundance. An average mass resolving power, m/∆m 50% >
600,000 at m/z 500 with 100-400 ppb mass error was achieved for all
samples.
Results and Discussion
Bioassay results For the site chosen for this study, DON uptake during the
bioassays was negligible (1.2% for the dry season and 4.1% for the wet
season). Similar results were obtained from bioassays on samples from
all sites sampled. The low rates of DON removal may be attributed to a
variety of factors including availability of alternative nitrogen sources,
failure of the inoculum to grow, or the refractory nature of the DON at
the sampling site.181 Phytoplankton generally favor inorganic nitrogen as
a growth substrate, and NH4+ is preferred by both phytoplankton and
bacteria.23, 182 Approximately 96% of the TDN was determined to be DON,
and therefore, only a relatively small fraction of the nitrogen available
existed as inorganic nitrogen. Bioassays are completely dependent on the
ability of the inoculum to thrive, not only so that the microbes can
remove DON, but also so that dying cells do not contribute to the
accumulation of DON. Although the changes were minimal, the
decreasing DON values do indicate that as least some portion of the DON
was reactive. However, it is likely that the labile fraction is very small
relative to the refractory fraction of the DON pool. Therefore, the majority
76
of the DON compounds in the C-43 water are not readily available for
uptake by the microbes during the time-scale of this study, which in turn
resulted in a relatively small change in the bulk DON concentrations.
Characterization of DON by APPI FT-ICR MS
All assigned DON ions contained carbon, hydrogen, and oxygen,
and thus we define classes based on the number of nitrogen atoms
contained in each molecular formula. Compounds in the N1 class are the
most frequently observed in both the negative and positive ion mass
spectra, with a steady decline in the number of molecules in the N2 and
N3 classes (Figure 5.1). No ions could be confirmed with more than three
nitrogen atoms.
It is clear from these data that positive ion APPI is significantly
more efficient at ionizing DON molecules than negative ion APPI for all
three nitrogen classes. Approximately 62% of the formulas identified in
the positive ion mass spectrum contained at least one nitrogen atom;
38%, 18% and 6% for the N1, N2 and N3 classes, respectively.
Furthermore, approximately 31% of the identified formulas in the
negative ion mass spectrum contained at least one nitrogen; 22%, 8%
and <1% for the N1, N2 and N3 classes, respectively. It should also be
noted that the data in Figure 1 represent relative numbers of identifiable
molecules observed. The total number of formulas identified containing
nitrogen was almost twice as high for the positive ion mass spectrum
(9403 for positive ion APPI to 4990 for negative ion APPI). Moreover,
positive ion APPI is also selective for nitrogen. We found that the N1, N2
and N3 classes comprised 35.22 ± 2.40%, 16.21 ± 2.19%, and 4.74 ±
2.32% of the identified formulas, respectively. With positive mode APPI,
over 50% of the molecules identified contained nitrogen. Thus, the
remainder of the ICR data presented are from positive ion APPI mass
spectra.
77
Figure 5.1. Percentage of total assigned formulas verse nitrogen class comparison of positive and negative ion atmospheric pressure
photoionization in generating organic nitrogen ions.
The mass distribution of the positive ion spectra of the dry season
C-43 stormwater treatment area extract ranges from m/z 200 to 800,
and contain 12722 indentified molecular formulas (Figure 5.2a). The
apex of the mass distribution is approximately m/z 353, with signal-to-
noise 339.
78
Figure 5.2. a) Broadband positive ion APPI FT-ICR mass spectrum of Caloosahatchee River DOM and; b) An m/z = 0.3 expanded mass spectral segment at m/z 432 with formulas containing N1 (●) and N3 (■) labeled.
Figure 5.2b is a 0.3 Da (all ions are singly charged) expanded
mass spectral zoom inset from the broadband spectrum in Figure 5.2a.
This small inset exhibits the spectral complexity of the sample and the
need for ultrahigh resolution, as it contains 38 peaks above a
conservative noise threshold of 6σ greater than the RMS baseline noise
Of the 38 peaks, 18 are formulas that contain at least one nitrogen atom.
In this m/z = 0.3 even nominal mass expanded zoom inset, both N1 (solid
circle) and N3 (solid square) compound classes are present; N2 ions are
located at odd nominal mass.145 Even in this very narrow zoom expanded
mass spectral segment, three distinct homologous series (two N1 and one
N3) may be identified. In each case, these series are defined by the
79
substitution of CH4 for O in the molecular formula, which results in the
36.4 mDa mass difference that is commonly observed in DOM (Table
5.1).
Table 5.1. Three nitrogen-containing homologous series identified from m/z 432.00-432.30. All three series exhibit the substitution of CH4 for
O and the corresponding 36.4 mDa mass difference.
Series Measured Mass (m/z)
Molecular Formula
1st N1 432.09264 C20H17N1O10 1st N1 432.12901 C21H21N1O9
1st N1 432.16540 C22H25N1O8 1st N1 432.20179 C23H29N1O7 1st N1 432.23817 C24H33N1O6 1st N1 432.27456 C25H37N1O5 2nd N1 432.11379 C17H21N1O12 2nd N1 432.15014 C18H25N1O11 2nd N1 432.18653 C19H29N1O10 2nd N1 432.22291 C20H33N1O9
2nd N1 432.25930 C21H37N1O8 N3 432.14021 C20H21N3O8 N3 432.17663 C21H25N3O7 N3 432.21306 C22H29N3O6
Kendrick analysis The power of ultrahigh resolution mass spectrometry is that
sample comparisons are possible on a molecular level. However, the large
quantities of data produced makes molecule-by-molecule analysis
unreasonable. Therefore, graphical representations are used to condense
and visualize the data. One of the most useful of these data reduction
strategies is the Kendrick Mass Analysis.183 This method rescales the
measured masses from their IUPAC masses based on the IUPAC CH2
mass of 14.01565 Da to Kendrick masses based on CH2=14.00000 Da
(Eq. 5.1).
80
Kendrick Mass = IUPAC mass x (14/14.01565) (5.1)
Members of the same homologous series (e.g. molecules that are similar
except for the addition of –CH2) have Kendrick masses differing by
exactly 14 Da and have the same Kendrick Mass Defect (KMD). The
KMD is the calculated difference between the Kendrick mass and the
IUPAC nominal mass (Eq. 5.2).
KMD = (Nominal Mass – Kendrick Exact mass) x 1000 (5.2)
Kendrick Plots of FT-ICR MS data are formulas reduced to their KMD
plotted against nominal mass. This allows visualization of a large data
sets, with an emphasis on the characteristics of formulas at each
nominal mass.
The elemental composition for the dry season samples are
remarkably similar, with over 95% of the formulas identified appearing in
both samples (data not shown). This molecular-based qualitative data
supports the conclusions from quantitative measurements that suggest
the dry-season DON in the river and STAs is essentially refractory.
Significant differences are exhibited in the mass spectra for the wet
season C-43 STA DON sample. We highlighted both the similarities and
differences in DON composition with the Kendrick plots in Figure 5.3.
Figure 5.3a includes all 5367 of the nitrogen-containing formulas that
were present in both the original C-43 STA wet season sample (T0) and
the sample that was incubated for 5 days (T5). The fact that these
formulas are found in both of these samples suggests that these
molecules are refractory. The vast majority of these refractory
components are located within a broad, elliptical distribution with
nominal masses between approximately 200 and 800 Da and KMD
values ranging from around 100 to 600. This is the same general area of
the Kendrick plot where the largely refractory dry season DON molecules
81
were located. There are a small number of formulas that fall outside of
that broad distribution, with KMD values <100.
Figure 5.3. a) Kendrick plot of the nitrogen-containing formulas assigned in both the original (T0) and incubated sample (T5) from the Caloosahatchee River during the wet season; and b) Kendrick plot of
assigned formulas only in T0 (♦) and those only in T5 (■). Figure 5.3b is a Kendrick plot that includes DON molecules
identified in either the original or in the sample after the five day
bioassay. The 1976 formulas identified only in the T0 sample are
represented as red diamonds and are referred to as removed formulas
because they were present in the T0 but not in T5. We consider the
removed formulas the labile fraction of the DON pool in the wet season
C-43 STA sample. The 945 formulas that appear in the T5 sample, but
not the T0 sample, are referred to as formed formulas. The most notable
82
characteristic of the distribution of removed formulas is that presence of
an elliptical pattern between about 400 and 820 Da that occurs at lower
KMD values than the patterns for refractory DON (Figure 5.3b). There are
two ways to view this distribution relative to the refractory distribution.
First is that the labile fraction occurs at higher nominal masses along a
common KMD line, an indication that molecules within a given
homologous series (i.e. longer or more abundant alkyl chains) are more
readily available for use by the microbes. The second way to examine the
labile distribution relative to the refractory molecules is by looking at a
common nominal mass. From this perspective, the labile components of
the DON pool have lower KMD values than their refractory counterparts.
There are two features of organic molecules that result in the decrease of
KMD at a given nominal mass; either a decrease in the number of oxygen
atoms or an increase in the number of hydrogen atoms. The exact mass
of oxygen is 15.994915 Da, and the removal of oxygen decreases KMD.
Conversely, hydrogen has an atomic mass of 1.007825. Therefore, an
increase of hydrogen content (i.e., increasing saturation) results in a
decrease in KMD at a given nominal mass. Unfortunately, it is not
possible to determine through the Kendrick analysis which of these
factors contributes to the decreased KMD of reactive of DON molecules.
There is another group of molecules present in the labile region of
the plot. These components have nominal masses from approximately
200 to 700 Da, with KMD values greater than 150, and they lie parallel
to the horizontal axis. Hatcher et al. identified molecules in this portion
of the Kendrick plot as fatty acids and have used them as internal
standards to calibrate FT-ICR spectra.184 Fatty acids are composed of
long alkyl chains and a relatively low number of oxygen molecules. These
characteristics are consistent with our earlier assessment of the first
distribution of reactive DON components.
Finally, there are removed formulas scattered throughout the
region that we considered mostly refractory molecules. However, toward
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the low nominal mass, low KMD edge of that distribution there appears
to be a more concentrated grouping of these types of molecules.
Therefore, it is possible that low molecular weight versions of typically
refractory molecules may be consumed by the microbes during
incubation.
The formed formulas identified in Figure 5.3b occupy the same
region of the Kendrick plot that is linked to refractory components
(Figure 5.3a). These formed molecules span nearly the same mass range,
but they are more concentrated in the mid to high nominal mass region
(~400-800 Da). While it might appear from Figure 5.3a that virtually all
possible KMDs occurs in the refractory DON pool, these newly formed
formulas clearly prove that is not the case. Moreover, it is noteworthy
that the newly formed formulas generally have KMD values greater than
the labile DON KMDs, suggesting that the removed formulas were
transformed by microbes into these newly formed formulas through
either addition of oxygen or loss of hydrogen (unsaturation) of the
original compounds.
van Krevelen analysis While Kendrick plots are useful for identifying trends in mass
effects in ultrahigh resolution MS data, comparisons of actual formulas
are best visualized with van Krevelen diagrams that reduce molecular
formulas to elemental ratios. A typical van Krevelen diagram includes
O/C ratios (x-axis) plotted against H/C ratios (y-axis). Different classes of
biomolecules, such as lipids, proteins, lignin, and carbohydrates, tend to
aggregate in distinct regions of the van Krevelen diagram.100 Therefore, it
is possible to examine changes to specific classes of DON compounds
with these plots.
As noted previously, 5367 formulas containing nitrogen were
present in both the T0 and T5 wet season C-43 samples, and Figure 5.4a
84
depicts the O/C and H/C of these formulas. The overwhelming majority
of these formulas fall within the region of the diagram associated with
lignin- and tannin-like compounds, the most refractory types of DOM
molecules. These formulas are the same group that forms the large
elliptical distribution in Figure 5.3a. A small group of formulas is also
apparent in the upper left corner of Figure 5.4a, an area which is
associated with lipids. This group represents the small number of
formulas in Figure 5.3a with KMD values less than 100.
Figure 5.4. a) A van Krevelen diagram of nitrogen-containing formulas identified in both the original (T0) and incubated sample (T5) from the
Caloosahatchee River. B) A van Krevelen diagram of nitrogen containing formulas identified only in T0 (♦) and those only in T5 (■).
Similar to the Kendrick plot in Figure 5.3b, the formulas identified
in the van Krevelen diagram in Figure 5.4b are distinguished as removed
85
or formed formulas. The most striking aspect of these data are the large
number of labile formulas with O/C ranging from approximately 0.1 to
0.3 and H/C of approximately 1.5 to 1.9, an area of the diagram
representative of protein-like compounds. Molecules in the upper left-
hand corner of this distribution have O/C < 0.1 and an H/C ~ 2.0,
typically associated with lipid- and fatty acid-like compounds. These
molecules can therefore be correlated with molecules with low KMD
values arranged horizontally in Figure 5.3b.
The van Krevelen plot analysis indicates that nearly all the newly
formed formulas are located in one characteristic region of the graph
associated with lignin- and tannin-like molecules. The position of these
formed formulas relative to the labile group is particularly noteworthy.
The labile formulas generally have lower O/C and higher H/C than their
formed counterparts, an indication of less oxygenated, more saturated
molecules that are more bioavailable. These data also suggest that the
microbes are converting these lipid- and protein-like compounds into
more oxygenated and less hydrogenated (unsaturated) structures.
Conclusion
The role DON plays in estuarine systems is relatively unknown,
which can largely be attributed to the lack of qualitative measurements
available. Physical separation of DON components from their complex
sample matrices is currently not possible, and therefore, mass spectral
analysis is the most suitable technique for characterization of DON
species on a molecular level. However, the inherent heterogeneity of both
the sample as a whole and, specifically, the DON pool requires ultrahigh
resolution and mass accuracy that is only achievable by FT-ICR MS. The
results of this study suggest that positive ion APPI is a highly efficient
method for the ionization of DON molecules. Bulk DON measurements
show that a limited amount of DON was removed during the course of a
86
5-day bioassay. Kendrick plots and van Krevelen diagrams yielded
molecular level information regarding the DON pool and were used to
confirm that the majority of the DON exists as a refractory component of
the DOM pool. However, it appears that molecules with a high degree of
saturation and low oxygen content are available for uptake by microbes
and converted to refractory type compounds.
87
CHAPTER 6
CHARACTERIZATION OF PYROGENIC BLACK
CARBON BY DESORPTION ATMOSPHERIC PRESSURE PHOTOIONIZATION FOURIER
TRANSFORM ION CYCLOTRON MASS SPECTROMETRY
Summary We present a new method for molecular characterization of intact
biochar directly, without sample preparation or pretreatment, based on
desorption atmospheric pressure photoionization (DAPPI) coupled to
Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry.
Conventional ionization methods (e.g., electrospray or atmospheric
pressure photoionization) for characterization of natural organic matter
have limited utility for the characterization of chars due to incomplete
solubility in common solvents. Therefore, direct ionization techniques
that do not require sample dissolution prior to analysis are ideal. Here,
we apply DAPPI FT-ICR mass spectrometry to enable the first molecular
characterization of uncharred parent oak biomass, and after combustion
(250 °C), or pyrolysis (400 °C). Parent oak is primarily composed of
cellulose-, lignin-, and protein-like compounds. Oak combusted at 250
°C contains condensed aromatic compounds with low H:C and O:C ratios
while retaining compounds with high H:C and O:C ratios. The bimodal
distribution of aromatic and aliphatic compounds observed in the
combusted oak sample is attributed to incomplete thermal degradation of
lignin and hemicellulose. Pyrolyzed oak constituents exhibit lower H:C
and O:C ratios: approximately three-quarters of the identified species are
aromatic. DAPPI FT-ICR MS results agree with bulk elemental
88
composition as well as functional group distribution determined by
elemental analysis and solid state 13C NMR spectroscopy. With the
detailed molecular fingerprint and molecular transformation that occur
in biomass after combustion and pyrolysis, the relationship between
biomass composition and thermal degradation processes may be better
understood. Complete molecular characterization of biomass upon
thermal transformation may also provide insight into the biogeochemical
cycles of biochar and future renewable energy sources, particularly for
samples currently limited by solubility, separation, and sample
preparation.
Introduction
Combustion products from thermally degraded vegetation (i.e.,
“black carbon” or BC) range from slightly charred biomass (biochar), to
charcoal, soot, and graphite according to the degree of thermal
degradation. Terrestrial soil and sediments contain BC, and groundwater
runoff transports BC to marine sediments.185 Although BC is a
heterogeneous mixture with a wide range of chemical and physical
properties, most BC remains in the environment and is resistant to
biological or chemical degradation.186-188 Characterization and
quantitation of BC in the global carbon budget is of recent interest
because BC may act as a significant carbon sink, moving carbon from
the relatively rapid bio-atmospheric cycle to the slower sedimentary
cycle.44, 48, 189
Contrary to previous reports that all BC is inert, Baldock and
Smerick reported on the presence of reactive BC, and concluded that the
degree of subsequent degradation depends on the extent of thermal
alteration of certain organic components in the post-fire residue.190
Degradation of BC occurs through two main processes: microbial and
photochemical.43, 190-191 Shneour found that 2% of artificial graphite is
89
oxidized in non-sterile soils.192 Scott et al. and Hofrichter et al. identified
several fungi able to decompose low-grade coals.193-194 They provided
evidence that BC undergoes some degradation in the environment, but
on a time scale ranging from a few to thousands of years.50, 191, 195-196
Previous characterization of char has been based on nuclear
magnetic resonance (NMR) spectroscopy,197-199 which provides structural
and bulk property measurements, but does not identify molecular
rearrangements that occur upon release into the environment. FT-ICR
mass spectrometry has been routinely applied to characterize complex
organic mixtures, due to high mass accuracy (< 200 ppb) and ultrahigh
resolution (m/Δm50% = 400,000 at m/z 400), required for accurate
elemental composition assignment.105, 200-202 Previous molecular
characterization of BC by FT-ICR MS examined and identified the
changes in elemental composition of the water soluble fraction after it
enters and is transported through an aquatic environment.126, 203-204
However, they were unable to characterize the starting BC material due
to low solubility. Here, we present the direct ionization and molecular
characterization of solid biochar and and its compositional changes of
oak before and after combustion or pyrolysis at 400 °C.
Direct ionization techniques enable the rapid analysis of solid
samples with little or no sample pretreatment, by placing them in an
atmospheric pressure chamber interface to the mass spectrometer inlet.
The most common direct ionization methods are desorption electrospray
ionization (DESI)205 and direct analysis in real time (DART) based on a
corona discharge.206 DESI and ESI mainly ionize polar analytes. Here we
present the first coupling of DAPPI to FT-ICR MS, thereby taking
advantage of all of the inherent benefits of APPI to enable analysis of
compounds spanning a wide range of polarity.131 DAPPI was first
introduced in 2007, and detailed mechanistic characterization has been
reported.106, 207 In DAPPI, a heated nebulizer produces a plume of hot
gas/solvent that is directed to the surface of a sample to desorb neutral
90
analyte molecules into the gas phase through a combination of thermal
and chemical processes.207 The desorbed analytes are then ionized by the
same mechanism as for APPI.106 APPI can ionize an analyte molecule
directly, resulting in loss of an electron to generate a molecular radical
cation, M+•. The energy of the photon must be greater than the ionization
energy of the analyte. However, the radiation output of a conventional
krypton UV lamp used for APPI is too low for efficient direct
photoionization.106 As a result, Bruins et al. developed dopant-assisted
APPI106 in which an excess of photoionizable reagent (dopant) provides
D+• ions that can easily react with analyte, M, by proton transfer or
charge exchange to generate M+• or [M+H]+ ions. Dopant-assisted APPI
can increase ionization efficiency by 2-3 orders of magnitude.131 The
choice of dopant for DAPPI determines the type/distribution of analyte
ion generated: [M+H]+ vs. M+• or [M-H]- vs. M-•, and should be
appropriately selected to increase ionization efficiency for compounds of
interest.180 DAPPI has been applied to the analysis of pharmaceutical
tablets,207 illicit drugs,208 polycyclic aromatic hydrocarbons in soil,209
pesticides on produce,209 and MS imaging of biological tissue.210
Here, for the first time we present molecular changes in solid
biochar for parent, combustion (at 250 °C), and pyrolysis (at 400 °C)
products of oak (henceforth denoted as parent oak, oak 250, and oak
400) by DAPPI FT-ICR MS without sample preparation or preseparation.
Our method provides an experimental framework for enhanced molecular
level characterization of biochar and its reactivity in the environment.
Experimental Methods
Samples
Quercus laurifolia (laurel oak) without bark was dried thoroughly
and cut into 1x1x5 cm pieces. Portions of approximately 1.5 g were
91
loosely wrapped in foil and baked in a 0.04 m3 combustion oven or
heated in a pyrolyzer (5.5 cm diameter x 50 cm length pipe). In the
pyrolysis experiment, N2 gas flowed over the sample at a rate of
approximately 2.3 oven volumes min-1. The heating rate was 10-12 °C
min-1 and peak temperature hold time was 3 h for combustion and
pyrolysis.
DAPPI source
A ThermoFisher IonMaxx™ ion source equipped with a krypton UV
lamp was used for all linear ion trap MS experiments, and a modified
ThermoFisher LCQ APPI (ThermoFisher Corp., Bremen, Germany) source
was used for all 9.4 Tesla FT-ICR MS experiments (Figure 6.1).105
Parent and charred biomass were held ~1 mm from the exit of the heated
ceramic nebulization tube by use of tweezers. The sample was 10 mm
from the MS inlet. Gas-phase neutrals were produced through thermal
and chemical desorption. Nitrogen was used as nebulizer gas at 100 psi
with toluene as dopant at a flow rate of 50 uL min-1. The temperature of
the heated nebulizer gas/solvent plume ranged from 100-500 °C
depending upon the sample. The temperature of the MS inlet capillary
was 200 °C and the tube lens voltage was set at -35 V.
92
Figure 6.1. Schematic of Thermo LCT source converted for DAPPI Experiments (Figure from modified from Purcell et al. 2006).105 The sample is placed directly the path of the heated solvent spray for thermal and chemical desorption. Desorbed neutrals undergo either direct photoionization, proton transfer, or charge exchange and enter
the mass spectrometer through a heated metal capillary.
Mass spectrometry Molecular weight distributions were determined with a
ThermoFisher linear ion trap (LTQ ThermoFisher Corp., Bremen,
Germany) mass spectrometer. LTQ mass spectra were obtained for
molecular weight determination and for desorption temperature
optimization based on signal intensity measurements across a
temperature range (100-500 °C). Negative ion mass spectra were
acquired with automatic gain control.211 Data were acquired with
Xcalibur version 2.0 software at a maximum injection period of 2000 ms
93
and a scan speed of 3 scans/spectrum. 50 scans were acquired for each
sample. For parent oak, 100 °C produced the highest signal intensity,
oak 250 at 200 °C, and oak 400 at 350 °C (Figure 6.2).
Figure 6.2. Broadband (-) DAPPI LTQ mass spectra of the parent oak,
oak combusted at 250 °C, and oak pyrolyzed at 400 °C. The optimum
solvent plume temperature was determined for each sample. Top: The mass spectrum is typical of fresh, labile organic biomass. Middle and Bottom: The mass spectra exhibits a broad pseudo-Gaussian distribution as the biomass is thermally degraded.
FT-ICR mass spectra were acquired with a passively shielded 9.4
tesla superconducting magnet (Oxford Instruments, Abingdon,
Oxfordshire OX13 5QX United Kingdom) located at the National High
Magnetic Field Laboratory in Tallahassee, Florida.212 Time-domain
transient signals were collected and processed by a modular ICR data
acquisition system.213 Negative ions were accumulated (50-500 ms)
94
externally214 in the second rf-only octopole and collisionally cooled with
helium prior to transfer through an rf-only octopole to a seven segment
open cylindrical cell with capacitively coupled excitation electrodes
similar to the configuration of Tolmachev et al.215 Chirp excitation (~700-
90 kHz at a sweep rate of 50 Hz µs-1 and 360 Vp-p amplitude) accelerated
the ions to a detectable cyclotron radius. Approximately 10-20 time-
domain acquisitions were co-added, Hanning-apodized, and zero-filled
once prior to fast Fourier transform and magnitude calculation.
Frequency was converted to m/z by the quadrupolar electric trapping
potential approximation.138, 216 Spectra were internally calibrated from
extended homologous alkylation series (compounds that differ in
elemental composition by integer multiples of CH2) of high relative
abundance.
Data analysis
Each peak in the mass spectrum was assigned a unique molecular
formula. In a van Krevelen diagram; namely, a plot of H:C vs. O:C ratio
(Figure 6.3),100, 196, 217 compounds with similar chemical properties tend
to locate in specific regions. The double bond equivalents (DBE = number
of rings plus double bonds to carbon) measures hydrogen deficiency,
calculated from the elemental composition, CcHhNnOoSs (DBE = C – h/2 +
n/2 + 1), determined by FT-ICR mass spectrometry.201, 218 DBE and
oxygen class are plotted as a function of percent relative abundance to
illustrate compositional changes in aromaticity and oxygen number
under different thermal conditions. Each elemental formula was assigned
an aromaticity index (AI) based on the system proposed by Koch and
Dittmar.219 Here, assignments are separated into non-aromatic (AI < 0.5),
aromatic (AI > 0.5) and condensed aromatics (AI ≥ 0.67) (Figure 2). AI
along with van Krevelen diagrams are efficient tools to visualize changes
95
in the elemental composition of organic materials resulting from thermal
degradation.
Figure 6.3. van Krevelen diagram of elemental H:C vs. O:C ratios. Molecular formulas with similar chemical characteristics tend to aggregate in specific regions. van Krevelen plots of different samples may be compared to determine changes in chemical composition. Formulas with Aromaticity Index (AI) values > 0.5 are considered aromatic, and those with AI > 0.67 condensed aromatic.
Nuclear magnetic resonance spectroscopy Solid-state 13C NMR spectra were obtained with a widebore Varian
Inova 500 MHz spectrometer operated at 125 MHz. Each sample (~100
mg) was packed in a 4 mm O.D. zirconium rotor and sealed with KEL-F
caps, followed by Ramped-Cross Polarization (CP) and Magic Angle
96
Spinning (MAS) at 14 kHz. Ramped CP overcomes the inefficiency of
cross polarization between 1H and 13C in high-speed MAS and decrease
in sensitivity from magnetic field inhomogeneity.220 A 3 s pulse delay five
times longer than the longest 1H spin lattice relaxation times minimizes
saturation effects. 20,000 free induction decays were summed for each
sample, zero-filled once, and processed with 50 Hz Lorentzian line
broadening. Functional group distributions were determined by
integrating over defined chemical shift regions; 0-50 ppm (alkyl C), 50-60
ppm ((N-alkyl and methoxy), 60-110 ppm (O-alkyl C including
carbohydrates), 110-160 ppm (aromatic C), 160-220 ppm (carbonyl C in
carboxylic acids, esters, amides, ketones, and aldehydes).
Elemental analysis Bulk elemental analyses were performed with a ThermoFinnigan
(Milan, Italy) Elemental Analyzer (Flash EA 1112) for total C, H, N, S, and
O content. Each sample (~1-2 mg) was weighed into a silver container for
oxygen determination (CE Elantech, Inc., Lakewood, NJ) or a tin
container (ThermoFinnigan Italia, Milan, Italy) for CHNS analysis,
crushed into a sphere, placed in an autosampler, and analyzed in
quadruplicate. Calibration is based on elemental analysis of
sulfanilamide (Thermo Electron S.p.A., Milan, Italy, CAS# 63-74-1, C:
±0.210, H: ±0.030, O: ±0.0834) and all quadruplicate runs included a
separate, independent standard not used in the initial calibration.
Results and Discussion
Parent oak
Figure 6.4 (top) shows the van Krevelen diagram for molecular
formulas detected for parent oak determined by DAPPI FT-ICR MS. Most
of assigned molecular formulas have high H:C and O:C with AI < 0.5,
97
indicative of aromatic structures based on the Koch/Dittmar method.219
Few formulas exhibit AI > 0.5, indicating that aromatic compounds
constitute a minor fraction of the overall composition of the parent oak,
as expected for untreated biomass. Most molecular formulas lie in
regions associated with cellulose-, aminosugar-, lignin-, and protein-like
compounds, abundant in cell walls and wood. Wood is composed of
~50% cellulose and ~30% lignin; therefore, a large contribution of
formulas with cellulose- and lignin-like molecular formulas is
expected.221 Results are in good agreement with bulk elemental analysis:
O:C = 0.51 +/- 0.003 and H:C = 1.51 +/- 0.010.
To determine if DAPPI ionization is representative of the native
sample, molecular compositions obtained by FT-ICR MS were compared
to 13C NMR results. Figure 6.4 (bottom) shows the RAMP CP 13C NMR
spectrum for the parent oak. The predominant peaks in the NMR
spectrum of parent oak are between 60-110 ppm, the region associated
with O-alkyl functionality. O-alkyl compounds have high O:C ratio, in
accord with DAPPI FT-ICR MS and bulk elemental analysis. Only a minor
contribution is observed in the aromatic region of the NMR spectrum
(110-160 ppm), consistent with MS results. The average O:C and H:C
ratios of parent oak are 0.51 ± 0.003 and 1.51 ± 0.010, which fall directly
in the center of the van Krevelen diagram distribution and further
validate that the DAPPI FT-ICR MS represent the parent material. High
H:C, and O:C of the parent material are consistent with prior 13C NMR
spectra of higher-plant biomass which are dominated by O-alkyl species,
a reflection of the dominance of cellulose.190, 217, 222
98
Figure 6.4. (Top) van Krevelen diagram for the elemental compositions assigned to parent oak by DAPPI FT-ICR MS. The molecular formulas aggregate in regions of the diagram typical of wood, i.e., lignin, protein, and cellulose. A few formulas are associated with aromatic compounds, i.e., A.I. > 0.5. (Bottom) NMR spectrum for parent oak. The spectrum is dominated by the O-alkyl peak, 60-110 ppm, with only minor contribution from the aromatic peak, 110-160 ppm. (* bulk O:C and H:C ratios determined by elemental analysis)
Oak 250 Combusted at 250 °C
A shift to formulas of lower H:C is evident in the van Krevelen
diagrams of oak 250 (Figure 6.5 (top)). The region associated with
protein-like compounds is lost and a significant number of formulas with
AI > 0.5 are formed. The appearance of aromatic and condensed aromatic
structures after the combustion of biomass is expected because of
degradation followed by dehydration of cellulose from 200-300 °C has
99
been reported to be responsible for the accumulation of new aryl
compounds.61 Furthermore, molecular formulas with AI ≥ 0.67, i.e.,
condensed aromatics, are observed for oak 250. Although elemental
formulas associated with protein-like compounds disappear after
combustion at 250 °C relative to the parent oak, formulas with H:C and
O:C associated with cellulose-like compounds remain. Elemental
compositions with high H:C and O:C in oak 250 may indicate multiple
processes. First, the elemental compositions that remain in that region
may show that a fraction of cellulose resists degradation by combustion
at 250 °C and remains mostly intact. Second, those compounds may be
by-products of incomplete combustion.
The DAPPI FT-ICR MS data for oak 250 again with RAMP CP 13C
NMR data (Figure 6.5 (bottom)), showing a relative decrease in the O-
alkyl signal and a relative increase in the aromatic region of the NMR
spectrum of oak 250 relative to the parent oak. From the van Krevelen
diagram, it is obvious that some of the compounds with high H:C and
O:C ratio are removed after combustion. The formation of aromatic
compounds after combustion is confirmed by the relative increase in the
aromatic peak in the NMR spectrum. Finally, the average O:C and H:C
ratios for oak 250 obtained by bulk elemental analysis are 0.34 ± 0.003
and 0.87 ± 0.017, suggesting that oak 250 consists primarily of
aromatic-like compounds. However, DAPPI FTMS enables the
simultaneous identification of the aromatic compounds and the high H:C
and O:C compounds that are not well represented by bulk elemental
analysis.
100
Figure 6.5. (Top) van Krevelen diagram for the elemental compositions
assigned to oak combusted at 250 °C by DAPPI FT-ICR MS. Molecular formulas characteristic of aromatic and condensed aromatics, i.e., AI > 0.5 and AI ≥ 0.67, are formed relative to the parent oak. Although
the elemental compositions associated with proteins disappear relative to parent oak, compounds with high O:C and H:C associated with cellulose remain in oak 250. (Bottom) NMR spectrum of oak 250, showing a decrease in the O-alkyl peak and increase in the aromatic peak relative to the parent oak. (* bulk O:C and H:C ratios determined by elemental analysis)
Oak 400 pyrolyzed at 400 °C A van Krevelen diagram from FT-ICR MS data for oak pyrolyzed at
400 °C shows a marked increase in the number of formulas with AI
values > 0.5, consistent with aromatic and condensed aromatic
compounds (Figure 6.6 (top)). Molecular formulas exhibit lower O:C and
H:C relative to the parent oak and oak 250. A complete loss of elemental
101
compositions associated with cellulose-like compounds is observed in the
oak 400 char. Significant depolymerization of cellulose occurs from 300-
350 °C, so a loss of most of the cellulose-like compounds observed in oak
400 is consistent with prior reports.223
Figure 6.6. (Top) van Krevelen diagram of the molecular formulas
assigned to oak pyrolyzed at 400 °C. Molecular formulas exhibit lower
O:C and H:C ratios relative to parent oak and oak 250, due to
depolymeriztion of cellulose and dehydration and deactylation of
lignin and cellulose. Approximately half of elemental compositions
assigned for to oak 400 have an AI > 0.55. (Bottom) NMR spectrum of
oak 400. The spectrum is dominated by the aromatic peak. There
almost no O-alkyl contribution relative to the parent oak and oak 250.
(* denotes bulk O:C and H:C ratios determined by elemental analysis)
102
The DAPPI FT-ICR MS data for oak 400 again agree with RAMP CP
13C NMR data (Figure 6.6 (bottom)). Here, the predominant NMR peak is
in the aromatic region. The peak in the O-alkyl region of the pyrolyzed
oak NMR spectrum is almost completely gone relative to the parent oak
and oak 250. Only a few of the compounds with high H:C and O:C ratios
remain. Moreover, the van Krevelen diagram for oak 400 identifies more
than 75% of the assigned formulas as aromatic or condensed aromatic.
Finally, the average O:C and H:C ratios for oak 400 from bulk elemental
analysis are 0.20 ± 0.002 and 0.78 ± 0.001. The bulk elemental analysis
reflects the predominance of aromatic compounds in oak 400 and
generally agrees with the formulas plotted in the van Krevelen diagram.
DBE distribution Figure 6.7 shows DBE distributions for all assigned elemental
compositions for negative DAPPI ions from parent oak, oak 250, and oak
400. Parent oak has relatively lower DBE than oak 250 and 400. Its
DBE classes range from DBE = 2 to 14 with the most abundant classes
from DBE = 3 to 6. The high relative abundance of classes with low DBE
is expected for parent material dominated by O-alkyl functionality and
high O:C and H:C.
The DBE distribution for oak 250 is bimodal, ranging from DBE =
1 to 21, with maxima at DBE = 7 and 13. The first distribution is slightly
higher in DBE than that for the parent oak, and the second distribution
is slightly lower than that for oak 400. The DBE distributions are
consistent with the van Krevelen plots. Elemental compositions with
relatively high H:C and O:C correspond to the low DBE maximum and
are associated with compounds that are either partially oxidized or not
thermally degraded. The higher DBE maximum is associated with the
species of AI > 0.5 that are formed after combustion.
103
The DBE distribution for oak 400 is dramatically higher than that
for the parent oak. High DBE values likely result from cleavage of
aliphatic chains from aromatic rings, with an onset at approximately 300
°C. Furthermore, high DBE in addition to lower O:C and H:C for oak 400
could result from loss of acetic acid (e.g., the deactylation of
hemicellulose at 200-300 °C). Although the contribution from thermally
unmodified lignin- and cellulose-like compounds make it unclear,
deactylation of hemicellulose may also explain the shift to lower DBE of
oak 250.
Figure 6.7. Double bond equivalents (DBE) relative abundance distribution for parent oak, oak 250, and oak 400. The parent oak has a relatively low DBE range, and oak 250 exhibits a bimodal distribution. Oak 400 is characterized by elemental compositions with relatively high DBE, the result of further thermal degradation of lignin
and cellulose.
104
Oxygen class distribution
Figure 6.8 shows On class relative abundances for parent oak, oak
250, and oak 400. Oxygen classes for the parent material range from O4
to O16. The two dominant oxygen classes are O9 and O10, with significant
O8 and O11 contributions. The narrow diversity in On classes is typical of
fresh, labile biomass.
As for the DBE distribution, the On class distribution for oak 250
is bimodal, centered at ~O8 and ~O13. The bimodal distribution is
consistent with the van Krevelen diagram for oak 250, in which
compounds with distributions centered at relatively low and high high
O:C and H:C. The relatively lower oxygen components may be attributed
to dehydration of hemicellulose and lignin, beginning at ~200 °C.223 The
higher oxygen species (~O12-O20) may arise from partial oxidation of
lignin due to incomplete combustion or thermally undegraded cellulose.
Additional evidence for deactylation of hemicellulose is evidenced
from the oxygen class graph for oak 400. The oxygen classes for oak 400
range from O2-O12, centered at O5. The lower oxygen numbers suggest
that the lower O:C ratios observed in Figure 3 are likely due to a
combination of deactylation and dehydration. The net change in O:C
ratio from deactylation (loss of H2CO) is negative (provided that, as in
this case, there are more carbons than oxygens in the molecule), and the
net change in O:C ratio is always negative for dehydration because one
oxygen is lost, but no carbons.
105
Figure 6.8. Percent relative abundance for various oxygen classes. Oak 250 has a bimodal distribution with the first distribution, and formulas in the O4-O11 classes are most likely thermally degraded lignin and cellulose compounds. Formulas in the O12-O20 classes represent residual cellulose that is not thermally degraded and
partially oxidized lignin and cellulose. Oak 400 formulas in the lower oxygen classes are the result of deactylation caused by thermal degradation.
Conclusion
We present data here that demonstrates for the first time that
molecular formula information for solid biochar may be determined by
DAPPI coupled to a FT-ICR MS. The results obtained by DAPPI FT-ICR
MS for parent oak, oak combusted in the presence of O2 at 250 °C, and
oak pyrolyzed at 400 °C in the absence of O2 are in agreement with bulk
106
data obtained by NMR spectroscopy and elemental analysis.
Furthermore, the DAPPI FT-ICR mass spectra are in agreement with
previous studies of parent and charred biomass reported in the
literature. Thes results provide the framework for molecular-level
characterization of a wide range of chars and complex mixtures where
solubility, separation, and sample preparation are limiting factors.
Finally, the data presented in this paper speaks for the versatility of
DAPPI as an ionization method for complex mixture analysis.
107
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209. L. Luosujarvi, S. Kanerva, V. Saarela, S. Franssila, R. Kostiainen, T. Kotiaho, T. J. Kauppila, Environmental and food analysis by desorption atmospheric pressure photoionization-mass spectrometry. Rapid Commun Mass Sp 2010, 24. 1343-1350, DOI: Doi 10.1002/Rcm.4524.
210. J. Pol, V. Vidova, G. Kruppa, V. Kobliha, P. Novak, K. Lemr, T. Kotiaho, R. Kostiainen, V. Havlicek, M. Volny, Automated Ambient Desorption-Ionization Platform for Surface Imaging Integrated with a Commercial Fourier Transform Ion Cyclotron Resonance Mass Spectrometer. Anal Chem 2009, 81. 8479-8487,
DOI: Doi 10.1021/Ac901368q.
211. T. J. Kauppila, T. Kotiaho, R. Kostiainen, A. P. Bruins, Negative ion-atmospheric pressure photoionization-mass spectrometry. J Am Soc Mass Spectr 2004, 15. 203-211, DOI: DOI 10.1016/j.jasms.2003.10.012.
212. N. K. Kaiser, G. E. Skulason, C. R. Weisbrod, J. E. Bruce, A Novel Fourier Transform Ion Cyclotron Resonance Mass Spectrometer with Improved Ion Trapping and Detection Capabilities. J. Am. Soc. Mass Spectrom. 2009, 20. 755-762, DOI: 10.1016/j.jasms.2008.12.022.
213. G. T. Blakney, C. L. Hendrickson, A. G. Marshall, Predator data station: A fast data acquisition system for advanced FT-ICR MS experiments. In Press, Corrected Proof. DOI: 10.1016/j.ijms.2011.03.009.
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214. M. W. Senko, C. L. Hendrickson, M. R. Emmett, S. D.-H. Shi, A. G. Marshall, External Accumulation of Ions for Enhanced Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. 1997, 8. 970-976.
215. A. V. Tolmachev, E. W. Robinson, S. Wu, H. Kang, N. M. Lourette, L. Pasa-Tolic, R. D. Smith, Trapped-ion cell with improved DC potential harmonicity for FT-ICR MS. J Am Soc Mass Spectr 2008, 19. 586-597, DOI: DOI 10.1016/j.jasms.2008.01.006.
216. E. B. Ledford, Jr., D. L. Rempel, M. L. Gross, Space Charge Effects in Fourier Transform Mass Spectrometry. Mass Calibration. 1984, 56. 2744-2748.
217. K. Hammes, R. J. Smernik, J. O. Skjemstad, A. Herzog, U. F. Vogt, M. W.
I. Schmidt, Synthesis and characterisation of laboratory-charred grass straw (Oryza saliva) and chestnut wood (Castanea sativa) as reference materials for black carbon quantification. Org Geochem 2006, 37. 1629-1633, DOI: DOI 10.1016/j.orggeochem.2006.07.003.
218. A. C. Stenson, A. G. Marshall, W. T. Cooper, Exact Masses and Chemical
Formulas of Individual Suwannee River Fulvic Acids from Ultrahigh Resolution Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectra. 2003, 75. 1275-1284.
219. B. P. Koch, T. Dittmar, From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Commun Mass Sp
2006, 20. 926-932, DOI: Doi 10.1002/Rcm.2386.
220. B. Glaser, J. Lehmann, W. Zech, Ameliorating physical and chemical properties of highly weathered soils in the tropics with charcoal - a review. Biol Fert Soils 2002, 35. 219-230, DOI: DOI 10.1007/s00374-002-0466-4.
221. E. Sjostrom, Wood Cemistry. Fundamentals and Applications. 2 ed.; Academic Press: San Diego, 1993.
222. C. M. Preston, J. A. Trofymow, J. Niu, C. A. Fyfe, (13)CPMAS-NMR
spectroscopy and chemical analysis of coarse woody debris in coastal forests of Vancouver Island. Forest Ecol Manag 1998, 111. 51-68.
223. R. H. White, M. A. Dietenberger, in Encyclopedia of materials : science and technology, ed. K. H. J. Buschow, R. W. Cahn, M. C. Flemings, B. Ilschner,
E. J. Kramer, S. Mahajan, P. Veyssiere. Elsevier, 2001, pp 9712-9716.
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BIOGRAPHICAL SKETCH
Current Address: Department of Chemistry and Biochemistry, Florida State
University, Tallahassee, Florida 32306-4390.
Education
Gardner-Webb University: B.S. in Chemistry, 2007
Florida State University: Graduate Student, Department of Chemistry &
Biochemistry, Analytical Chemistry Program, June, 2007 – present.
Graduate Advisor: W.T. Cooper, Department of Chemistry & Biochemistry,
Florida State University
Previous International Experience
Moscow and St. Petersburg Russia, September 14-19, 2008: Presented poster
and lecture at the 14th Meeting of the International Humic Substances
Society.
Current Research Activities
Amy M. McKenna, Ryan P. Rodgers, David C. Podgorski, Christopher Reddy, Robert Nelson, Mmilli Mapolelo; Molecular Level Characterization and
Archive for the 2010 BP Oil Spill. National Science Foundation Rapid Response Grant No. CHE-1049753.
Christopher Reddy, Karin Lemkau, Amy M. McKenna, Ryan P. Rodgers, David Valentine, David C. Podgorski; Molecular Characterization of the Cosco Busan oil spill in the San Francisco Bay 2007. National Science Foundation Grant No. OCE-0960841
William T. Cooper, David C. Podgorski, Ralph Mead, Robert Kieber;
Characterization of the Reactivity of CDOM in Rainwater from Ethanol, Gasoline and Diesel Emissions. National Science Foundation Grant No. AGS-1003078
William T. Cooper, David C. Podgorski, Rasha Hamdan, Andrew R. Zimmerman; Effects of Biomass Type and Combustion Conditions on the Molecular Properties of Biochar-derived Dissolved Organic Matter as Determined by Ultrahigh Resolution Mass Spectrometry. National Science Foundation Grant No. EAR-0819811.
Honors and Awards
2008. International Humic Substances Society Travel Bursary
2007. Hoffman Merit Award, Department of Chemistry & Biochemistry, Florida
State University
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2007. M.A. Moseley Chemistry Award, Florida State University
2006. Gardner-Webb University Community Service Award
2005-06. Gardner-Webb University Academic Scholarship
Publications
Podgorski, D. C., McKenna, A. M., Nyadong, L., Rodgers, R. P., Marshall, A. G., Cooper, W. T.; Characterization of pyrogenic black carbon by desorption atmospheric pressure photoionization Fourier transform ion cyclotron
resonance mass spectrometry. Anal. Chem., in preparation. Podgorski, D. C., McKenna, A. M, Osborne, D. M., Hendrickson, C. L.,
Marshall, A. G., Cooper, W. T.; Detection and unique molecular formula assignment of doubly charged negative dissolved organic matter ions extracted from natural sources by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Anal. Bioanal. Chem., in preparation.
Osborne, D. M., Podgorski, D. C., Roberts, Q., Bronk, D. A., Austin, D., Spiler, R., Bays, J.; Characterization of reactive and refractory dissolved organic nitrogen compounds in a stormwater treatment area by atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry. Environ. Sci. Technol., in preparation.
Podgorski D. C., McKenna, A. M., Rodgers, R. P., Marshall, A. G., Cooper, W. T.; Selective ionization and molecular characterization of dissolved
organic nitrogen by positive ion atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry. Submitted to Rapid Commun. Mass Spectrom., 2011.
Tfaily, M. M., Podgorski, D. C., Corbett, J. E., Chanton, J. P., Cooper, W. T.; Influence of acidification on the optical properties and molecular composition of dissolved organic matter. Submitted to Anal. Chim. Acta, 2011.
Gonsior, M., Peake, B. M., Cooper W. T., Podgorski D. C., D’Andrilli, J.,
Dittmar, T., Cooper, W.J.; Characterization of dissolved organic matter across the Subtropical Convergence off the South Island, New Zealand. Mar. Chem, 2011, 123, 99-110.
Chipman, L., Podgorski, D. C., Green, S., Kostka, J., Cooper, W.T., Huettel, M.; Decomposition of plankton-derived dissolved organic matter in permeable coastal sediments. Limnol. Oceanogr., 2010, 55, 857-871.
Gonsior, M., Cooper, W. J., Cooper, W. T., Podgorski D. C., D’Andrilli, J.,
Peake, B. M.; Photochemically-induced Changes in Dissolved Organic Matter Identified by Ultrahigh Resolution Fourier Transform - Ion Cyclotron Resonance - Mass Spectrometry. Environ. Sci. Technol., 2009, 43 (3), pp 698–703.
Oral Conference Presentations
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Podgorski, D. C., Osborne, D. M., McKenna, A. M., Rodgers, R. P., Marshall, A. G., Cooper, W. T.; “The molecular characterization of dissolved organic nitrogen by atmospheric pressure photoionization Fourier-transform ion cyclotron resonance mass spectrometry”. Goldschmidt 2010: Earth,
Energy, and the Environment, Knoxville, TN, June 13-18, 2010. Podgorski, D. C., Osborne, D. M., Cooper, W. T.; “The Detection and Exact
Molecular Formula Assignment of Multiply-Charged Ions in Complex Mixtures by ESI FT-ICR MS.” Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy, Orlando, FL, February 28-March 5, 2010.
Podgorski, D. C., Zimmerman, A. R., Cooper, W. T.; “Molecular Composition of
Dissolved Pyrogenic Carbon by Ultrahigh Resolution Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry and its Relationship to Bioavailability”, 238th American Chemical Society National Meeting, Washington, D.C., August 16-20, 2009.
Podgorski, D. C., Zimmerman, A. R., Cooper, W. T.; “Molecular Characterization of Dissolved Pyrogenic Carbon by Ultrahigh Resolution Mass Spectrometry.” North American Biochar Conference, Boulder, CO,
August 9-12, 2009. Podgorski, D. C., Zimmerman, A. R., Cooper, W. T.; “Effects of Biomass Type
and Combustion Conditions on the Molecular Properties of Biochar-Derived Dissolved Organic Matter as Determined by Ultrahigh Resolution Mass Spectrometry”, 85th Florida Annual Meeting and Exposition, Orlando, FL, May 14-16, 2009.
Podgorski, D. C., Huettel, M., Chipman, L., Magen, C., Cooper, W. T.; “Characterization of Microbiological Effects on the Composition and
Photochemical Properties of DOM in Coastal Sands Using Ultrahigh Resolution Mass Spectrometry”, 14th Meeting of the International Humic Substances Society, Moscow, Russia, September 14-19, 2008
Conference Abstracts Cooper, W. T., Osborne, D. M., Podgorski, D. C.; Ultrahigh Resolution Mass
Spectrometry of Dissolved Organic Nitrogen in Water Quality Treatment
Areas. 2011 IWA Specialty Conference on Natural Organic Matter: From Source to Tap and Beyond, Costa Mesa, CA, July 26-29, 2011.
McKenna, A. M., Rodgers, R. P., Nelson, R., Reddy, C., Podgorski, D. C., Savory, J. T., Kaiser, N. K., Hendrickson, C. L., Marshall, A. G.; Catastrophe in the Gulf of Mexico: The Deepwater Horizon Mississippi Canyon Mocondo Well 252 Oil Spill Characterized by FT-ICR Mass Spectrometry and Comprehensive Two-Dimensional GC x GC. Petrophase 2011, London, U.K., July 10-14, 2011.
Nikhil, J., Lim, F., Juyal, P., McKenna, A. M., Podgorski, D. C., Ho, V., Yen, A. T., Rodgers, R. P., Allenson, S. J., Marshall, A. G.; Characterization of Crude Oil and Asphaltenes from an Elevated GOR Production Well in the Gulf of Mexico. Petrophase 2011, London, U.K., July 10-14, 2011.
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McKenna, A. M., Rodgers, R. P., Nelson, R., Reddy, C., Podgorski, D. C., Savory, J. T., Kaiser, N. K., Hendrickson, C. L., Marshall, A. G.; Catastrophe in the Gulf of Mexico: Molecular Characterization of the Deepwater Horizon Oil Spill by FT-ICR MS and Comprehensive GC x GC.
59th ASMS Conference on Mass Spectrometry and Allied Topics, Denver, CO, June 5-9, 2011.
Ruddy, B. M., McKenna, A. M., Podgorski, D. C., Rodgers, R. P., Huettel, M., Marshall, A. G.; Compositional Analysis of BP Deepwater Horizon Oil Contaminated Pensacola Beach Sand by Ultrahigh Resolution FT-ICR MS. 59th ASMS Conference on Mass Spectrometry and Allied Topics, Denver, CO, June 5-9, 2011.
McKenna, A. M., Podgorski, D. C., Corilo, Y. E., Ruddy, B. M., Kaiser, N. K.,
Savory, J. T., Rodgers, R. P., Hendrickson, C. L., Marshall, A. G.; Advanced Characterization of Environmental Samples by FT-ICR MS: Dissolved Organic Matter to Petroleum. 8th North American FT MS Conference, Key West, FL, May 1-5, 2011.
Cooper, W. T., Witowski, C., Podgorski, D. C., Wetz, M., Kostka, J.; Optimization and Lipid Profiling of Algae Biofuel Feedstocks Grown in Wastewater. 2010 Florida Energy Systems Consortium, Orlando, FL,
September 28-29, 2010. Huettel, M., Chipman, L., Podgorski, D. C., Green, S. J., Magen, C.,
Niggemann, J., Ziervogel, K., Arnosti, C., Berg, P., Cooper, W. T., Dittmar, T., Kostka, J. E., Hallas, K.; Organic Matter Degradation and Nutrient Remobilization in Permeable Costal Sands. Goldschmidt 2010: Earth, Energy, and the Environment, Knoxville, TN, June 13-18, 2010.
Osborne, D. M., Cooper, W. T., Podgorski, D. C.; Dissolved Organic Nitrogen:
Not Just a Number Anymore? 86th Florida Annual Meeting and Exposition, Innisbrook, FL, May 13-16, 2010.
Witowski, C., Podgorski, D. C., Cooper, W. T.; Growth Optimization and Lipid Profiling of Algae Biofuel Feedstocks. 86th Florida Annual Meeting and Exposition, Innisbrook, FL, May 13-16, 2010.
Huettel, M., Chipman, L., Laschet, M., Podgorski, D. C., Green, S. J., Kostka, J. E., Cooper, W. T.; Dissolved Organic Carbon Degradation in
Sublittoral Sands. ASLO/TOS/AGU 2010 Ocean Sciences Meeting, Portland, OR, February 22-26, 2010.
Magen, C., Huettel, M., Podgorski, D. C., Cooper, W. T.; Advection of Labile Dissolved Organic Carbon (DOC) Through Permeable Sands Induces the Release of Sorbed DOC to the Overlying Water. ASLO/TOS/AGU 2010 Ocean Sciences Meeting, Portland, OR, February 22-26, 2010.
Osborne, D. M., Podgorski D. C., Cooper, W. T.; The Characterization of
Dissolved Organic Nitrogen by Ultrahigh Resolution Mass Spectrometry. 2009 Fall Meeting of the American Geophysical Union, San Francisco, CA, December 14-18, 2009.
Cooper, W. T., Tfaily, M. M., Podgorski, D. C., Osborne, D. M., Paul, A. L., Corbett, J. E., Chanton, J. P.; Relationship between Molecular
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Composition and Optical Properties of Dissolved Organic Matter. 2009 Fall Meeting of the American Geophysical Union, San Francisco, CA, December 14-18, 2009.
Chipman, L., Podgorski, D. C., Green, S., Kostka, J., Cooper, W. T., Huettel,
M.; DOM Decomposition in Permeable Coastal Sediments. Chemical Oceanography in a Changing World Symposium, Savannah, GA, February 22-24, 2009.
D’Andrilli, J., Cooper, W. T., Podgorski, D. C., Magen, C., Huettel, M. and Kostka, J.;“Characterization of the Effects of Microbial Processing in Gulf of Mexico Coastal Sands on the Composition of Dissolved Organic Matter Using Ultrahigh Resolution Mass Spectrometry”, 2008 Fall Meeting of
the American Geophysical Union, San Francisco, CA, December 14-19, 2008.
Kloecking, R., Helbig, H., Kinne, M., Kleiner, C., Gorecki, T., Poerschmann, J., Podgorski, D. C. and Cooper, W.T.; “Characterization of Synthetic (Core) Humic Substances Made from Dihydroxylated Phenylpropanoids” 14th Meeting of the International Humic Substances Society, Moscow, Russia, September 14-19, 2008.
Cooper, W. T., D’Andrilli, J., Podgorski, D. C., Dittmar, T., Huettel, M., Kostka, J., Chipman, L. and Gonsior, M.; “Ultrahigh Resolution Mass Spectrometry of Dissolved Organic Matter in Estuaries”, National Science Foundation Workshop, St. Petersburg, FL, May 6-8, 2008.
Cooper, W. T., D’Andrilli, J., Podgorski, D. C., Dittmar, T., Huettel, M. and Chipman, L.; “Ultrahigh Resolution Mass Spectrometry of Dissolved Organic Matter: The Path to Geomics”, American Society of Limnology and
Oceanography (ASLO) 2008 Ocean Sciences Meeting, Orlando, FL, March 2-7, 2008.
Field Experience April 2008: Research assistant on R/V Bellows. Participated in water sample
collection and preparation for FT-ICR MS analysis. Chief Scientist: Nicolas Wienders
June 2010: Research assistant on multiple research trips to sample rivers and
estuaries over a period of 10 days in Brazil (Sepetiba Bay and Paraiba do Sul River). Collected and prepared water samples for FT-ICR MS analysis. Chief Scientist: William T. Cooper
Professional Societies
• American Chemical Society
• American Geophysical Union
• International Humic Substances Society
• American Society of Mass Spectrometry
• American Association for the Advancement of Science