Structural feature based computational approach of toxicity prediction of ionic liquids: Cationic and anionic effects on ionic liquids toxicity

Salam, M.A. and Abdullah, B. and Ramli, A. and Mujtaba, I.M. (2016) Structural feature based computational approach of toxicity prediction of ionic liquids: Cationic and anionic effects on ionic liquids toxicity. Journal of Molecular Liquids, 224. pp. 393-400. ISSN 01677322

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

The density functional theory (DFT) based a unique model has been developed to predict the toxicity of ionic liquids using structural-feature based quantum chemical reactivity descriptors. Electrophilic indices (�), the energy of highest occupied (EHOMO) and lowest unoccupied molecular orbital, (ELUMO) and energy gap (� E) were selected as the best toxicity descriptors of ILs via Pearson correlation and multiple linear regression analyses. The principle components analysis (PCA) demonstrated the distribution and inter-relation of descriptors of the model. A multiple linear regression (MLR) analysis on selected descriptors derived the model equation for toxicity prediction of ionic liquids. The model predicted toxicity values and mechanism are very consistent with observed toxicity. Cationic and side chains length effect are pronounced to the toxicity of ILs. The model will provide an economic screening method to predict the toxicity of a wide range of ionic liquids and their toxicity mechanism. © 2016 Elsevier B.V.

Item Type: Article
Additional Information: cited By 33
Uncontrolled Keywords: Computation theory; Correlation methods; Density functional theory; Dyes; Forecasting; Ionic liquids; Linear regression; Liquids; Molecular orbitals; Negative ions; Positive ions; Principal component analysis; Quantum chemistry; Regression analysis; Toxicity, Computational approach; Electrophilicity index; Lowest unoccupied molecular orbital; Multiple linear regression analysis; Multiple linear regressions; Pearson correlation; Principle components analysis; Toxicity predictions, Density of liquids
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 16:18
Last Modified: 09 Nov 2023 16:18
URI: https://khub.utp.edu.my/scholars/id/eprint/6563

Actions (login required)

View Item
View Item