IMPACT‐4CCS: Integrated Modeling and Prediction Using Ab Initio and Trained Potentials for Collision Cross Sections DOI Creative Commons
Carson Farmer, Hector Medina

Journal of Computational Chemistry, Journal Year: 2025, Volume and Issue: 46(11)

Published: April 18, 2025

ABSTRACT Collision cross section (CCS) values can enhance the identification and classification of molecular contaminants such as per‐ polyfluororoalkyl substances (PFAS). However, computational burden required for large molecules, combined with increasing number potential PFAS candidates, render existing methods incapable providing sufficiently accurate results in a timely manner. Furthermore, machine learning struggle to generalize when (de)protonated structure undergoes structural changes that are not common training dataset. In this study, we introduce IMPACT4‐CCS (Integrated Modeling Prediction using Ab initio Trained potentials Cross Section), novel workflow ensemble comprises ab tasks accelerate prediction CCS molecules. IMPACT‐4CCS achieves comparable accuracy current approaches, validated test set 100 exhibits better implemented on some specific emerging subclasses, n H‐perfluoroalkyl carboxylic acids ( H‐PFCA) family, which other overestimate their values. As far authors know, is only method capable capturing dynamics (i.e., hydrogen bridging) present flexible Our work demonstrates careful use traditional likely be more than relying purely graphs. Future (or recommended) includes assessing usefulness extending nontarget analysis larger datasets OECD (Organization Economic Co‐operation Development) list PubChem, could greater 7 million molecules diverse chemistry.

Language: Английский

Per- and polyfluorinated substances in reservoir water from a metropolitan city in the Guangdong-Hong Kong-Macao Greater Bay Area, China, and their ecological risks DOI Creative Commons

Yiming Ge,

Yi Huang,

Linshen Xie

et al.

Environmental Chemistry and Ecotoxicology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Per- and Poly-Fluoroalkyl Substances, and Organophosphate Flame Retardants in the Upper Yangtze River: Occurrence, Spatiotemporal Distribution, and Risk Assessment DOI Creative Commons
Wen Sun,

Zhiyou Fu,

Yueyue Liu

et al.

Toxics, Journal Year: 2025, Volume and Issue: 13(2), P. 116 - 116

Published: Feb. 1, 2025

Contaminants of Emerging Concern (CECs), including per- and polyfluoroalkyl substances (PFASs) organophosphate flame retardants (OPFRs), have raised global concerns due to their persistence, bioaccumulation potential, toxicity. This study presents a comprehensive investigation the occurrence, spatiotemporal distribution, potential sources, ecological human health risks associated with 18 PFASs 9 OPFRs in surface waters upper Yangtze River, China. The water samples were collected from main stream five major tributaries (Min, Jinsha, Tuo, Jialing, Wu Rivers) 2022 2023. total concentration ranged 16.07 927.19 ng/L, 17.36 190.42 respectively, consistently higher observed compared tributaries. Ultra-short-chain (e.g., TFMS) halogenated TCPP) predominant compounds, likely originating industrial discharges, wastewater effluents, other anthropogenic sources. Ecological risk assessments indicated low-to-moderate at most sampling sites, near discharge points. Human evaluations suggested negligible non-carcinogenic but identified carcinogenic OPFR exposure for adults specific locations, particularly Leshan city. highlights importance understanding fate impacts provides valuable insights developing targeted pollution control strategies management measures.

Language: Английский

Citations

0
IMPACT‐4CCS: Integrated Modeling and Prediction Using Ab Initio and Trained Potentials for Collision Cross Sections DOI Creative Commons
Carson Farmer, Hector Medina

Journal of Computational Chemistry, Journal Year: 2025, Volume and Issue: 46(11)

Published: April 18, 2025

ABSTRACT Collision cross section (CCS) values can enhance the identification and classification of molecular contaminants such as per‐ polyfluororoalkyl substances (PFAS). However, computational burden required for large molecules, combined with increasing number potential PFAS candidates, render existing methods incapable providing sufficiently accurate results in a timely manner. Furthermore, machine learning struggle to generalize when (de)protonated structure undergoes structural changes that are not common training dataset. In this study, we introduce IMPACT4‐CCS (Integrated Modeling Prediction using Ab initio Trained potentials Cross Section), novel workflow ensemble comprises ab tasks accelerate prediction CCS molecules. IMPACT‐4CCS achieves comparable accuracy current approaches, validated test set 100 exhibits better implemented on some specific emerging subclasses, n H‐perfluoroalkyl carboxylic acids ( H‐PFCA) family, which other overestimate their values. As far authors know, is only method capable capturing dynamics (i.e., hydrogen bridging) present flexible Our work demonstrates careful use traditional likely be more than relying purely graphs. Future (or recommended) includes assessing usefulness extending nontarget analysis larger datasets OECD (Organization Economic Co‐operation Development) list PubChem, could greater 7 million molecules diverse chemistry.

Language: Английский

Citations

0