Multidimensional library for the improved identification of per- and polyfluoroalkyl substances (PFAS)

被引:1
|
作者
Joseph, Kara M. [1 ]
Boatman, Anna K. [1 ]
Dodds, James N. [1 ]
Kirkwood-Donelson, Kaylie I. [2 ]
Ryan, Jack P. [1 ]
Zhang, Jian [3 ]
Thiessen, Paul A. [3 ]
Bolton, Evan E. [3 ]
Valdiviezo, Alan [4 ,5 ]
Sapozhnikova, Yelena [6 ]
Rusyn, Ivan [4 ,5 ]
Schymanski, Emma L. [7 ]
Baker, Erin S. [1 ,4 ]
机构
[1] Univ North Carolina Chapel Hill, Dept Chem, Chapel Hill, NC 27599 USA
[2] Natl Inst Environm Hlth Sci, Immun Inflammat & Dis Lab, Durham, NC 27709 USA
[3] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20894 USA
[4] Texas A&M Univ, Interdisciplinary Fac Toxicol, College Stn, TX 77843 USA
[5] Texas A&M Univ, Dept Vet Physiol & Pharmacol, College Stn, TX 77843 USA
[6] US Dept Agr, Agr Res Serv, Wyndmoor, PA 19038 USA
[7] Univ Luxembourg, Luxembourg Ctr Syst Biomed LCSB, 6 Ave Swing, L-4367 Belvaux, Luxembourg
基金
美国国家卫生研究院;
关键词
MASS-SPECTROMETRY; RAPID CHARACTERIZATION; FLUORINATED COMPOUNDS;
D O I
10.1038/s41597-024-04363-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
As the occurrence of human diseases and conditions increase, questions continue to arise about their linkages to chemical exposure, especially for per-and polyfluoroalkyl substances (PFAS). Currently, many chemicals of concern have limited experimental information available for their use in analytical assessments. Here, we aim to increase this knowledge by providing the scientific community with multidimensional characteristics for 175 PFAS and their resulting 281 ion types. Using a platform coupling reversed-phase liquid chromatography (RPLC), electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI), drift tube ion mobility spectrometry (IMS), and mass spectrometry (MS), the retention times, collision cross section (CCS) values, and m/z ratios were determined for all analytes and assembled into an openly available multidimensional dataset. This information will provide the scientific community with essential characteristics to expand analytical assessments of PFAS and augment machine learning training sets for discovering new PFAS.
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页数:11
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