Keyword search (4,163 papers available)

"Ge J" Authored Publications:

Title Authors PubMed ID
1 Strategies to Reduce Uncertainties from the Best Available Physicochemical Parameters Used for Modeling Novel Organophosphate Esters across Multimedia Environments Xing C; Ge J; Chen R; Li S; Wang C; Zhang X; Geng Y; Jones KC; Zhu Y; 40105294
CHEMBIOCHEM
2 Probability of major depression diagnostic classification using semi-structured versus fully structured diagnostic interviews Levis B; Benedetti A; Riehm KE; Saadat N; Levis AW; Azar M; Rice DB; Chiovitti MJ; Sanchez TA; Cuijpers P; Gilbody S; Ioannidis JPA; Kloda LA; McMillan D; Patten SB; Shrier I; Steele RJ; Ziegelstein RC; Akena DH; Arroll B; Ayalon L; Baradaran HR; Baron M; Beraldi A; Bombardier CH; Butterworth P; Carter G; Chagas MH; Chan JCN; Cholera R; Chowdhary N; Clover K; Conwell Y; de Man-van Ginkel JM; Delgadillo J; Fann JR; Fischer FH; Fischler B; Fung D; Gelaye B; Goodyear-Smith F; Greeno CG; Hall BJ; Hambridge J; Harrison PA; Hegerl U; Hides L; Hobfoll SE; Hudson M; Hyphantis T; Inagaki M; Ismail K; Jetté N; Khamseh ME; Kiely KM; Lamers F; Liu SI; Lotrakul M; Loureiro SR; Löwe B; Marsh L; McGuire A; Mohd Sidik S; Munhoz TN; Muramatsu K; Osório FL; Patel V; Pence BW; Persoons P; Picardi A; Rooney AG; Santos IS; Shaaban J; Sidebottom A; Simning A; Stafford L; Sung S; Tan PLL; Turner A; van der Feltz-Cornelis CM; van Weert HC; Vöhringer PA; White J; Whooley MA; Winkley K; Yamada M; Zhang Y; Thombs BD; 29717691
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3 Shortening self-report mental health symptom measures through optimal test assembly methods: Development and validation of the Patient Health Questionnaire-Depression-4 Ishihara M; Harel D; Levis B; Levis AW; Riehm KE; Saadat N; Azar M; Rice DB; Sanchez TA; Chiovitti MJ; Cuijpers P; Gilbody S; Ioannidis JPA; Kloda LA; McMillan D; Patten SB; Shrier I; Arroll B; Bombardier CH; Butterworth P; Carter G; Clover K; Conwell Y; Goodyear-Smith F; Greeno CG; Hambridge J; Harrison PA; Hudson M; Jetté N; Kiely KM; McGuire A; Pence BW; Rooney AG; Sidebottom A; Simning A; Turner A; White J; Whooley MA; Winkley K; Benedetti A; Thombs BD; 30238571
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Title:Strategies to Reduce Uncertainties from the Best Available Physicochemical Parameters Used for Modeling Novel Organophosphate Esters across Multimedia Environments
Authors:Xing CGe JChen RLi SWang CZhang XGeng YJones KCZhu Y
Link:https://pubmed.ncbi.nlm.nih.gov/40105294/
DOI:10.1021/acs.est.4c11028
Publication:Environmental science & technology
Keywords:environmental multimedia distributionnovel organophosphate estersoctanol-air partition coefficient (Koa)octanol-water partition coefficient (Kow)overall persistencephysicochemical propertiesvapor pressure (Vp)water solubility (Sw)
PMID:40105294 Category: Date Added:2025-03-19
Dept Affiliation: CHEMBIOCHEM
1 School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
2 The Key Laboratory of Environmental Health Impact Assessment for Emerging Pollutants, Ministry of Ecology and Environment of the People's Republic of China, Shanghai 200240, China.
3 Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China.
4 Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street West, Montreal, Quebec H4B 1R6, Canada.
5 Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom.

Description:

Organophosphate esters (OPEs) raise growing environmental and human health concerns globally. However, numerous novel OPEs lack data on physicochemical properties, which are essential for assessing environmental fate, exposure, and risks. This study predicted water solubility (Sw), vapor pressure (Vp), octanol-water partition coefficient (Kow), and octanol-air partition coefficient (Koa) at 25 °C for 46 novel OPEs by identifying optimal in silico tools and establishing prediction strategies based on molecular weights (MWs). Prediction discrepancies between in silico tools increased with MWs and structural complexity. Method evaluations for compounds with MWs > 450 g/mol suggest that COSMOtherm is advantageous in predicting Sw and Vp for alkyl-OPEs, while SPARC is better for predicting Vp for aryl- and halogenated-OPEs. For compounds with MWs > 500 g/mol, COSMOtherm and SPARC are recommended for Kow and Koa prediction, respectively. For smaller OPEs, average values from the top three of COSMOtherm, SPARC, EPI Suite, and OPERA, ranked by validation on traditional flame retardants, are recommended. Using improper software could cause deviations in multimedia distribution and overall persistence in the environment by up to 83 and 350%, respectively. The present data and prediction strategy are useful to enhance the reliability of environmental fate, exposure, and risk assessments of various OPEs and emerging contaminants.





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