Woods Hole Oceanographic Institution

Elizabeth B Kujawinski Behn

»Using stable isotope probing to characterize differences between free-living and sediment-associated microorganisms in the subsurface.
»DOM in Lake Superior
»Deepwater Horizon hydrocarbons in the marine environment
»Microbes and marine DOM, Ann. Rev. Mar. Sci. 2011
»Greenland ice sheet outlet glacier: Insights from a new isotope-mixing model
»Groundwater DOM, GCA 2011
»Dispersants & DWH, ES&T 2011
»FT-MS variability in DOM, Org Geochem 2010
»Predatory Flavobacteria, FEMS Microb Ecol 2010
»Greenland Ice Sheet DOM, GCA 2010
»Protozoa and bacteria in aquifers, FEMS Microb Ecol, 2009
»Source markers in DOM, GCA 2009
»Automated data analysis, Anal. Chem. 2006
»Marine DOM and ESI FT-ICR MS; Marine Chem 2004
»DOM extraction by C18; Org. Geochem. 2003
»Black carbon by ESI FT-ICR MS; ES&T 2004
»ESI FT-ICR MS review; Env. Forensics 2002
»Marine protozoan surfactants; Marine Chem. 2002
»ESI MS and NOM; Org. Geochem. 2002
»ESI FT-ICR MS & humic acids; Anal. Chem. 2002
»Protozoan DOM & PCBs; ES&T 2001
»Protozoa & Fe, Th, C; Aquat. Microb. Ecol. 2001
»PCB uptake by protozoa; AEM 2000

Elizabeth B. Kujawinski and Mark D. Behn, Automated analysis of electrospray ionization Fourier-transform ion cyclotron resonance mass spectra of natural organic matter, Anal. Chem. 78:4363-4373, 2006

The advent of ultrahigh resolution mass spectrometry has revolutionized the ability of aquatic biogeochemists to examine the components of complex mixtures of organic matter on a molecular level. A critical component of these studies has been the interpretation of mass spectra and the ability of each set of investigators to accurately assess the chemical composition and/or elemental formulas of detected compounds. Here we build on previous studies that use functional group relationships between compounds to extend elemental formulas of low-molecular weight (LMW) compounds to those of higher molecular weight. We propose an automated approach to the analysis of ultrahigh resolution mass spectra of natural organic matter (NOM) acquired by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. This approach is bench-marked with synthetic data sets of compounds cited in the literature. Sources of error are examined and the Compound Identification Algorithm is applied to two previously-published data sets.


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