Literature DB >> 33370097

Online Counter Gradient LC-FT-ICR-MS Enables Detection of Highly Polar Natural Organic Matter Fractions.

Limei Han1, Jan Kaesler1, Chang Peng2, Thorsten Reemtsma1,2, Oliver J Lechtenfeld1,3.   

Abstract

Natural organic matter (NOM) is a highly complex mixture of natural organic molecules. The recent developments in NOM molecular characterization methods have shown that ESI-FT-ICR hyphenated with liquid chromatography (LC) is a promising approach to also obtain chemical information (such as polarity and molecular size) about NOM molecules. However, due to changing solvent composition during gradient elution in LC-FT-ICR-MS, ionization conditions also change throughout the chromatographic separation process. In this study, we applied a post-LC column counter gradient (CG) to ensure stable solvent conditions for transient ESI-MS signals. Suwanee River Fulvic Acid (SRFA) standard and a peat pore water were used as representative dissolved NOM samples for method development and validation. Our results show that in polar NOM fractions (which elute with <50% methanol) the TIC intensity and number of assigned molecular formulas were increased by 48% and 20%, as compared to the standard gradient (SG) method. Further application of a Q-isolation and selective ion accumulation for low abundance fractions revealed over 3 times more molecular formulas (especially for CHNO, CHOS, CHNOS formula classes) than in full scan mode. The number of detected highly polar NOM compounds (with elemental ratios H/C < 1, O/C > 0.6) were more than 20 times larger for CG-LC mode as compared to direct infusion (DI) (5715 vs 266 MF). We conclude that the application of a postcolumn counter gradient in LC-FT-ICR-MS analyses of NOM offers novel insight into the most polar fractions of NOM which are inaccessible in conventional DI measurements.

Entities:  

Year:  2020        PMID: 33370097     DOI: 10.1021/acs.analchem.0c04426

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  1 in total

1.  Identification of Unknown Substances in Ambient Air (PM10), Profiles and Differences between Rural, Urban and Industrial Areas.

Authors:  Antonio López; Esther Fuentes; Vicent Yusà; María Ibáñez; Clara Coscollà
Journal:  Toxics       Date:  2022-04-27
  1 in total

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