Predicting the rate constants of semivolatile organic compounds with hydroxyl radicals and ozone in indoor air

Wei, Wenjuan; Sivanantham, S.; Malingre, Laeticia; Ramalho, Olivier; Mandin, Corinne

doi:10.1016/j.envpol.2020.115050

Cited by 5 publications

(3 citation statements)

References 70 publications

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“…The gas-phase reactions initiated by OH and NO 3 radicals primarily occur during the day and at night, respectively. [13,14] Transition state theory (TST) has been employed to investigate the mechanisms and corresponding possible products of gasphase reactions. [15][16][17] Studies on the mechanism of the NO 3radical-initiated oxidation of PAHs 15 have revealed that NO 3 initially serves as an electrophilic reagent, which attacks the position showing the highest electron density.…”

Section: Introductionmentioning

confidence: 99%

“…In the atmosphere, the gas‐phase reactions of PAHs initiated by NO 3 and OH radicals are their primary oxidation reactions. The gas‐phase reactions initiated by OH and NO 3 radicals primarily occur during the day and at night, respectively [13,14] . Transition state theory (TST) has been employed to investigate the mechanisms and corresponding possible products of gas‐phase reactions [15–17] .…”

Section: Introductionmentioning

confidence: 99%

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Computational Studies on the Reactivity of Polycyclic Aromatic Hydrocarbons

Ren

Zhang

et al. 2022

ChemPhysChem

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Polycyclic aromatic hydrocarbons (PAHs) are widely present in the environment as toxic pollutants. In this study, quantum chemistry methods are used to study reactions of PAHs in both particle and gas phases. Seven theoretical methods are exploited to predict the reactive sites of 15 PAHs in the particle phase. Among these methods, the performance of the condensed Fukui function (CFF) is optimum. The gas-phase reactions of eight PAHs are also investigated. Except for fluorene, CFF predicts correctly the gas-phase mono-nitro products for seven systems. The products of fluorene predicted by CFF are 1-nitrofluorene and 3-nitrofluorene, which is however inconsistent with the experimental results. Transition state theory is then used to investigate the reaction mechanism of fluorene. Calculated rate constants for 3-nitrofluorene and 2nitrofluorene formation are much bigger than that for 1nitrofluorene formation, which is in agreement with the experimental results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Studies on the Reactivity of Polycyclic Aromatic Hydrocarbons

Ren

Zhang

et al. 2022

ChemPhysChem

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“…Well-trained college students can rationally elucidate the qualitative properties of specific molecules from their structures with domain knowledge, which encourages us to build computer-based numerical models to learn the quantitative properties of structures in the form that machines recognized. − Machine learning (ML) techniques are widely used for such a purpose and other applications. − They represent versatile and highly transferable methods to investigate the quantitative structure-property relationship (QSPR) of molecules. ,, Typically, chemical descriptors are calculated from input structures to feed ML models. For example, topological descriptors and fingerprints in the form of numerical arrays can be generated from the Simplified Molecular Input Line Entry System (SMILES), which is the string representation of unique chemical structures. , With such vector representations of molecules, ML models can be trained to learn the hidden relationship between structures and given properties.…”

Section: Introductionmentioning

confidence: 99%

Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models

Liu

2023

J. Chem. Inf. Model.

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Ionization energy (IE) is an important property of molecules. It is highly desirable to predict IE efficiently based on, for example, machine learning (ML)-powered quantitative structure-property relationships (QSPR). In this study, we systematically compare the performance of different machine learning models in predicting the IE of molecules with distinct functional groups obtained from the NIST webbook. Mordred and PaDEL are used to generate informative and computationally inexpensive descriptors for conventional ML models. Using a descriptor to indicate if the molecule is a radical can significantly improve the performance of these ML models. Support vector regression (SVR) is the best conventional ML model for IE prediction. In graph-based models, the AttentiveFP gives an even better performance compared to SVR. The difference between these two types of models mainly comes from their predictions for radical molecules, where the local environment around an unpaired electron is better described by graph-based models. These results provide not only highperformance models for IE prediction but also useful information in choosing models to obtain reliable QSPR.

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Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

Kelleci Çelik,

Karaduman

2024

Chemosphere

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Predicting the rate constants of semivolatile organic compounds with hydroxyl radicals and ozone in indoor air

Cited by 5 publications

References 70 publications

Computational Studies on the Reactivity of Polycyclic Aromatic Hydrocarbons

Computational Studies on the Reactivity of Polycyclic Aromatic Hydrocarbons

Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models

Computational modeling of air pollutants for aquatic risk: Prediction of ecological toxicity and exploring structural characteristics

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