Мақола

Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies

Taiwo W. QuadriDepartment of Chemistry, School of Chemical and Physical Sciences and Material Science Innovation & Modelling (MaSIM) Research Focus Area, Faculty of Natural and Agricultural Sciences, North-West University, Private Bag X2046, Mmabatho 2735, South AfricaLukman O. OlasunkanmiDepartment of Chemical Sciences, University of Johannesburg, P.O. Box 17011, Doornfontein Campus, Johannesburg 2028, South AfricaOmolola E. FayemiDepartment of Chemistry, School of Chemical and Physical Sciences and Material Science Innovation & Modelling (MaSIM) Research Focus Area, Faculty of Natural and Agricultural Sciences, North-West University, Private Bag X2046, Mmabatho 2735, South AfricaHassane LgazInnovative Durable Building and Infrastructure Research Center, Center for Creative Convergence Education, Hanyang University-ERICA, 55 Hanyangdaehak-ro, Sangrok-gu, Ansan-si, Gyeonggi-do, 15588, KoreaOmar DagdagInstitute for Nanotechnology and Water Sustainability, College of Science, Engineering and Technology, University of South Africa, Johannesburg 1710, South AfricaEl‐Sayed M. SherifDepartment of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Al-Riyadh 11421, Saudi ArabiaAwad A. AlrashdiChemistry Department, Umm Al-Qura University, Al-Qunfudah University College, Saudi ArabiaEkemini D. AkpanInstitute for Nanotechnology and Water Sustainability, College of Science, Engineering and Technology, University of South Africa, Johannesburg 1710, South AfricaHan‐Seung LeeDepartment of Architectural Engineering, Hanyang University-ERICA, 1271 Sa 3-dong, Sangrok-gu, Ansan 426791, KoreaEno E. EbensoInstitute for Nanotechnology and Water Sustainability, College of Science, Engineering and Technology, University of South Africa, Johannesburg 1710, South Africa

2022en

ABI

Аннотация

The inhibition of mild steel deterioration via organic substances has become popular nowadays. Among the myriads of organic substances applied as potential inhibitors, quinoxalines stand out as toxic-free, cheap and effective compounds in different electrolytes. This report investigates the computational aspects of selected quinoxaline compounds tested as suppressors of mild steel deterioration in HCl medium using quantum chemical method (Density Functional Theory, DFT) and quantitative structure property relationship (QSPR). Feature selection tool was utilized to choose five top molecular descriptors (constitutional indices) that were used to characterize the quinoxaline molecules. Linear (ordinary least squares regression) and nonlinear (artificial neural network) modelling were adopted to correlate the selected constitutional indices of the studied quinoxalines with their experimental inhibition performances. The nonlinear model showed better performance as shown by the obtained results; RMSE of 5.4160, MSE of 29.3336, MAD of 2.3816 and MAPE of 5.0389. The developed models were utilized to determine the inhibition performances of ten new quinoxaline-based corrosion inhibitors which showed excellent inhibition performances of 87.88 to 95.73%.

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DOI: 10.1016/j.arabjc.2022.103870

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