Quantitative structure-activity relationship models for predicting apparent rate constants of organic compounds with ferrate (VI)

Xiaochen Qi; Na Liu; Zhongen Tang; Wenjuan Ou; Chuanqi Jian; Yutao Lei

doi:10.1016/j.scitotenv.2023.162043

Quantitative structure-activity relationship models for predicting apparent rate constants of organic compounds with ferrate (VI)

Sci Total Environ. 2023 May 1:871:162043. doi: 10.1016/j.scitotenv.2023.162043. Epub 2023 Feb 6.

Authors

Xiaochen Qi¹, Na Liu², Zhongen Tang³, Wenjuan Ou², Chuanqi Jian², Yutao Lei⁴

Affiliations

¹ Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, Guangdong, China.
² Department of Ecology, College of Life Science and Technology, Jinan University, Guangzhou 510632, Guangdong, China.
³ Anew Global Consulting Limited, Guangzhou 510075, Guangdong, China.
⁴ South China Institute of Environmental Sciences, Guangzhou 510655, Guangdong, China. Electronic address: leiyutao@scies.org.

PMID: 36754322
DOI: 10.1016/j.scitotenv.2023.162043

Abstract

Ferrate (VI) (Fe (VI)) is a promising, environmentally friendly multifunctional oxidant widely applied in organic compound degradation. Oxidative kinetics of the apparent second-order rate constants (k_app) of Fe (VI) with organic compounds are critical for modeling oxidation processes. Herein, a quantitative structure-activity relationship (QSAR) model was developed using particle swarm optimization and an extreme learning machine to better understand the laws of the k_app values of organic compounds, including 33 aliphatic and aromatic hydrocarbon derivatives, during degradation by Fe (VI). Seven components-electronic hardness (H), electronic softness (S), ratio of oxygen to carbon atoms (O_n/C_n), energy of the highest occupied molecular orbital (E_HOMO), vertical ionization potential (VIP), maximum nucleophilic reaction index (f(+)_x), and minimum relative electrophilicity index (RE_n) constitute the critical molecular parameters. The developed QSAR model was verified on the basis of the coefficient of determination (R²) and the root mean square error (RMSE): for the training set, R² = 0.924 and RMSE = 1.186, whereas for the test set, R² = 0.996, and RMSE = 0.352. The applicability, reliability, and predictability of the model were verified by estimating the applicability domain (AD) of the model. Furthermore, QSAR models constructed using different methods were compared, and the main impact descriptors and conclusions obtained from previous studies were theoretically analyzed. Results indicate that constructing the QSAR model facilitates k_app prediction for Fe (VI) in the degradation of various organic compounds, improves the understanding of the degradation mechanism, and reduces the pressure on human and material resources caused by experiments.

Keywords: Apparent second-order rate constants; Degradation mechanism; Ferrate (VI); Quantitative structure–activity relationship.