Quantum chemical calculation of the vapor pressure of volatile and semi volatile organic compounds

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Quantum chemical calculation of the vapor pressure of volatile and semi volatile organic compounds. / Stahn, Marcel; Grimme, Stefan; Salthammer, Tunga et al.

In: Environmental Sciences: Processes & Impacts, Vol. 24, No. 11, 03.10.2022, p. 2153-2166.

Research output: Journal contributionsJournal articlesResearchpeer-review

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Stahn M, Grimme S, Salthammer T, Hohm U, Palm W-U. Quantum chemical calculation of the vapor pressure of volatile and semi volatile organic compounds. Environmental Sciences: Processes & Impacts. 2022 Oct 3;24(11):2153-2166. Epub 2022 Oct 3. doi: 10.1039/d2em00271j

Bibtex

@article{4de425e581d34c8596e1a60d1bba5ac7,
title = "Quantum chemical calculation of the vapor pressure of volatile and semi volatile organic compounds",
abstract = "The vapor pressure is a specific and temperature-dependent parameter that describes the volatility of a substance and thus its driving force for evaporation or sublimation into the gas phase. Depending on the magnitude of the vapor pressure, there are different methods for experimental determination. However, these are usually associated with a corresponding amount of effort and become less accurate as the vapor pressure decreases. For purposes of vapor pressure prediction, algorithms were developed that are usually based on quantitative structure–activity relationships (QSAR). The quantum mechanical (QM) approach followed here applies an alternative, much less empirical strategy, where the change in Gibbs free energy for the transition from the condensed to the gas phase is obtained from conformer ensembles computed for each phase separately. The results of this automatic, so-called CRENSO workflow are compared with experimentally determined vapor pressures for a large set of environmentally relevant compounds. In addition, comparisons are made with the single structure-based COSMO-RS QM approach, linear-free-energy relationships (LFER) as well as results from the SPARC program. We show that our CRENSO workflow is superior to conventional prediction models and provides reliable vapor pressures for liquids and sub-cooled liquids over a wide pressure range.",
keywords = "Chemistry",
author = "Marcel Stahn and Stefan Grimme and Tunga Salthammer and Uwe Hohm and Wolf-Ulrich Palm",
note = "Publisher Copyright: {\textcopyright} 2022 The Royal Society of Chemistry.",
year = "2022",
month = oct,
day = "3",
doi = "10.1039/d2em00271j",
language = "English",
volume = "24",
pages = "2153--2166",
journal = "Environmental Sciences: Processes & Impacts",
issn = "2050-7887",
publisher = "Royal Society of Chemistry",
number = "11",

}

RIS

TY - JOUR

T1 - Quantum chemical calculation of the vapor pressure of volatile and semi volatile organic compounds

AU - Stahn, Marcel

AU - Grimme, Stefan

AU - Salthammer, Tunga

AU - Hohm, Uwe

AU - Palm, Wolf-Ulrich

N1 - Publisher Copyright: © 2022 The Royal Society of Chemistry.

PY - 2022/10/3

Y1 - 2022/10/3

N2 - The vapor pressure is a specific and temperature-dependent parameter that describes the volatility of a substance and thus its driving force for evaporation or sublimation into the gas phase. Depending on the magnitude of the vapor pressure, there are different methods for experimental determination. However, these are usually associated with a corresponding amount of effort and become less accurate as the vapor pressure decreases. For purposes of vapor pressure prediction, algorithms were developed that are usually based on quantitative structure–activity relationships (QSAR). The quantum mechanical (QM) approach followed here applies an alternative, much less empirical strategy, where the change in Gibbs free energy for the transition from the condensed to the gas phase is obtained from conformer ensembles computed for each phase separately. The results of this automatic, so-called CRENSO workflow are compared with experimentally determined vapor pressures for a large set of environmentally relevant compounds. In addition, comparisons are made with the single structure-based COSMO-RS QM approach, linear-free-energy relationships (LFER) as well as results from the SPARC program. We show that our CRENSO workflow is superior to conventional prediction models and provides reliable vapor pressures for liquids and sub-cooled liquids over a wide pressure range.

AB - The vapor pressure is a specific and temperature-dependent parameter that describes the volatility of a substance and thus its driving force for evaporation or sublimation into the gas phase. Depending on the magnitude of the vapor pressure, there are different methods for experimental determination. However, these are usually associated with a corresponding amount of effort and become less accurate as the vapor pressure decreases. For purposes of vapor pressure prediction, algorithms were developed that are usually based on quantitative structure–activity relationships (QSAR). The quantum mechanical (QM) approach followed here applies an alternative, much less empirical strategy, where the change in Gibbs free energy for the transition from the condensed to the gas phase is obtained from conformer ensembles computed for each phase separately. The results of this automatic, so-called CRENSO workflow are compared with experimentally determined vapor pressures for a large set of environmentally relevant compounds. In addition, comparisons are made with the single structure-based COSMO-RS QM approach, linear-free-energy relationships (LFER) as well as results from the SPARC program. We show that our CRENSO workflow is superior to conventional prediction models and provides reliable vapor pressures for liquids and sub-cooled liquids over a wide pressure range.

KW - Chemistry

UR - http://www.scopus.com/inward/record.url?scp=85141267644&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e686c9f6-4a66-3710-be20-bb22867027bc/

U2 - 10.1039/d2em00271j

DO - 10.1039/d2em00271j

M3 - Journal articles

C2 - 36222641

VL - 24

SP - 2153

EP - 2166

JO - Environmental Sciences: Processes & Impacts

JF - Environmental Sciences: Processes & Impacts

SN - 2050-7887

IS - 11

ER -

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