期刊
JOURNAL OF HAZARDOUS MATERIALS
卷 397, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jhazmat.2020.122761
关键词
Ionic liquids; Toxicity; Vibrio fischeri; ELM model; Electrostatic potential
资金
- project IGA of the Faculty of Environmental Sciences, Czech University of Life Sciences Prague [2020B0032]
Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (S-EP) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R-2), the average absolute error (AAE%) and the rootmean-square error (RMSE) of the developed model are 0.9272, 0.2101 and 0.3262 for the entire set, respectively. The proposed approach based on the high R-2 and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri.
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