Publications by authors named "Ikenna Benedict Onyeachu"
Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives.
Christopher Ikechukwu Ekeocha Ikechukwu Nelson Uzochukwu Ikenna Benedict Onyeachu Ini-Ibehe Nabuk Etim Emeka Emmanuel Oguzie
J Phys Chem A
November 2024
Article Synopsis
- This study investigated how effective certain antipyrine compounds are at preventing corrosion in hydrochloric acid (HCl) using advanced modeling techniques.
- Researchers used density functional theory (DFT) and machine learning methods like K-nearest neighbor (KNN) and artificial neural networks (ANN) to create predictive models, with the ANN model outperforming KNN significantly.
- The efficiencies of these antipyrine compounds in inhibiting corrosion varied widely, with the best-performing compound (A1) achieving nearly 99.79% effectiveness and demonstrating strong adsorption characteristics.
Metal pollution of water and soil ecosystems has been linked to stress and/or toxicity in plants, thus affecting the quality and productivity of food crops. This condition has further aggravated the essential food demand caused by the increase in the human population. Reports from previous studies have shown that correcting the noxiousness due to metal stress tolerance, requires several modes of action in the systemic, tissue, cellular, physiological, biochemical, and molecular levels in food crops which might be apparent in terms of enhanced productivity.
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