共 50 条
Multidimensional library for the improved identification of per- and polyfluoroalkyl substances (PFAS)
被引:0
|作者:
Kara M. Joseph
[1
]
Anna K. Boatman
[1
]
James N. Dodds
[1
]
Kaylie I. Kirkwood-Donelson
[2
]
Jack P. Ryan
[1
]
Jian Zhang
[3
]
Paul A. Thiessen
[3
]
Evan E. Bolton
[3
]
Alan Valdiviezo
[4
]
Yelena Sapozhnikova
[5
]
Ivan Rusyn
[6
]
Emma L. Schymanski
[4
]
Erin S. Baker
[5
]
机构:
[1] University of North Carolina at Chapel Hill,Department of Chemistry
[2] National Institute of Environmental Health Sciences,Immunity, Inflammation, and Disease Laboratory
[3] National Institutes of Health,National Center for Biotechnology Information, National Library of Medicine
[4] Texas A&M University,Interdisciplinary Faculty of Toxicology
[5] Texas A&M University,Department of Veterinary Physiology and Pharmacology
[6] U.S Department of Agriculture,Agricultural Research Service
[7] 6 Avenue du Swing,Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg
来源:
关键词:
D O I:
10.1038/s41597-024-04363-0
中图分类号:
学科分类号:
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.
引用
收藏
相关论文