用于改进全氟烷基和多氟烷基物质(PFAS)鉴定的多维文库。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Kara M Joseph, Anna K Boatman, James N Dodds, Kaylie I Kirkwood-Donelson, Jack P Ryan, Jian Zhang, Paul A Thiessen, Evan E Bolton, Alan Valdiviezo, Yelena Sapozhnikova, Ivan Rusyn, Emma L Schymanski, Erin S Baker
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引用次数: 0

摘要

随着人类疾病和病症的发生增加,它们与化学品接触的联系,特别是与全氟烷基和多氟烷基物质的接触,继续出现问题。目前,许多令人关注的化学品在分析评估中可用的实验资料有限。在这里,我们的目标是通过为科学界提供175种PFAS及其产生的281种离子类型的多维特征来增加这方面的知识。使用耦合反相液相色谱(RPLC)、电喷雾电离(ESI)或大气压化学电离(APCI)、漂移管离子迁移谱(IMS)和质谱(MS)的平台,确定了所有分析物的保留时间、碰撞横截面(CCS)值和m/z比,并将其组装成一个公开可用的多维数据集。这些信息将为科学界提供基本特征,以扩展PFAS的分析评估,并增加机器学习训练集,以发现新的PFAS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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