Yazar "Olasunkanmi, Lukman O." seçeneğine göre listele

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    Development of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors
    (Elsevier, 2022) Quadri, Taiwo W.; Olasunkanmi, Lukman O.; Akpan, Ekemini D.; Fayemi, Omolola E.; Lee, Han-Seung; Lgaz, Hassane; Verma, Chandrabhan
    Twenty pyridazine derivatives with previously reported experimental data were utilized to develop predictive models for the anticorrosion abilities of pyridazine-based compounds. The models were developed by using quantitative structure-activity relationship (QSAR) as a tool to relate essential molecular descriptors of the pyridazines with their experimental inhibition efficiencies. Chemical descriptors associated with frontier molecular orbitals (FMOs) were obtained using density functional theory (DFT) calculations, while others were obtained from additional calculations effected on Dragon 7 software. Five descriptors together with concentrations of the pyridazine inhibitors were used to develop the multiple linear regression (MLR) and artificial neural network (ANN) models. The optimal ANN model yielded the best results with 111.5910, 10.5637 and 10.2362 for MSE, RMSE and MAPE respectively. The results revealed that ANN gave better results than MLR model. The proposed models suggested that the adsorption of pyridazine derivatives is dependent on the five descriptors.Five pyridazine compounds were theoretically designed.
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    Molecular modelling of compounds used for corrosion inhibition studies: a review
    (Royal Soc Chemistry, 2021) Ebenso, Eno E.; Verma, Chandrabhan; Olasunkanmi, Lukman O.; Akpan, Ekemini D.; Verma, Dakeshwar Kumar; Lgaz, Hassane; Guo, Lei
    Molecular modelling of organic compounds using computational software has emerged as a powerful approach for theoretical determination of the corrosion inhibition potential of organic compounds. Some of the common techniques involved in the theoretical studies of corrosion inhibition potential and mechanisms include density functional theory (DFT), molecular dynamics (MD) and Monte Carlo (MC) simulations, and artificial neural network (ANN) and quantitative structure-activity relationship (QSAR) modeling. Using computational modelling, the chemical reactivity and corrosion inhibition activities of organic compounds can be explained. The modelling can be regarded as a time-saving and eco-friendly approach for screening organic compounds for corrosion inhibition potential before their wet laboratory synthesis would be carried out. Another advantage of computational modelling is that molecular sites responsible for interactions with metallic surfaces (active sites or adsorption sites) and the orientation of organic compounds can be easily predicted. Using different theoretical descriptors/parameters, the inhibition effectiveness and nature of the metal-inhibitor interactions can also be predicted. The present review article is a collection of major advancements in the field of computational modelling for the design and testing of the corrosion inhibition effectiveness of organic corrosion inhibitors.
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    Multilayer perceptron neural network-based QSAR models for the assessment and prediction of corrosion inhibition performances of ionic liquids
    (Elsevier, 2022) Quadri, Taiwo W.; Olasunkanmi, Lukman O.; Fayemi, Omolola E.; Akpan, Ekemini D.; Lee, Han Seung; Lgaz, Hassane; Verma, Chandrabhan
    The present study reports the quantum chemical studies and quantitative structure activity relationship (QSAR) modeling of thirty ionic liquids utilized as chemical additives to repress mild steel degradation in 1.0 M HCl. Five molecular descriptors obtained from standardization of calculated descriptors together with the inhibitor con-centration were employed in model building. Multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN) modeling were utilized in model construction. The optimal MLPNN model was developed using a network architecture of 6-3-5-1 with Levenberg-Marquardt as the learning algorithm. The model yielded an MSE of 29.9242, RMSE of 5.4703, MAD of 4.9628, MAPE of 5.7809, rMBE of 0.1202 and CoV of 0.0052. The MLPNN model displayed better predictive performance than the MLR model. Furthermore, developed models were applied to forecast the inhibition efficiencies of five novel ionic liquids. The theoretical inhibitors were found to be effective inhibitors of steel corrosion, showing over 80% inhibition efficiency.