4.7 Article

A quantitative structure activity relationship (QSAR) model for predicting the rate constant of the reaction between VOCs and NO3 radicals

Journal

CHEMICAL ENGINEERING JOURNAL
Volume 448, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2022.136413

Keywords

VOCs; NO3 radical; Reaction rate constant; Quantitative descriptor; QSAR

Funding

  1. National Key Research and Devel-opment Program of China [2019YFC1805201]
  2. NSCF Joint Program [U21A20320]
  3. Medicine & Engineering Collabo-rative Research Fund of Shanghai Jiao Tong University [YG2019ZDA29]

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A QSAR model was established to predict the rate constant (kNO3) for the chemical degradation of VOCs using the kNO3 of 189 VOCs and quantum chemical parameters. The model revealed that EHOMO and f(-)x were the key intrinsic factors determining kNO3, while EGAP, f(0)n, and BOx also significantly affected kNO3.
NO3 radical is one of the important oxidants used for the chemical degradation of Volatile organic compounds (VOCs). Thus, the derivation of rate constant (kNO3) is critical to understand the removal kinetics of VOCs. To better estimate the kNO3, a quantitative structure activity relationship (QSAR) model was established using the kNO3 of 189 VOCs and quantum chemical parameters. The optimal QSAR model can predict the kNO3 with its R2, q2, and Qext2 being 0.880, 0.872, and 0.861, respectively. The QSAR analysis results indicate that the energy of the highest occupied molecular orbital (EHOMO) and the maximum value of electrophilic Fukui index (f(- )x) are two intrinsic factors determining the kNO3. Additionally, the analysis of the correlation between kNO3 values and quantum chemical parameters shows that the gap energy between LUMO and HOMO (EGAP), the minimum value of free radical Fukui index (f (0)n), and the maximum value of bond order (BOx) also significantly affect kNO3. The validations of the optimal QSAR model confirm the high realizability, stability and accuracy, and the wide applicability of the derived model for predicting k(NO3).

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