Determination of corrosion inhibition effects of amino acids: Quantum chemical and molecular dynamic simulation study

• Published in: Journal of the Taiwan Institute of Chemical Engineers Vol. 58; pp. 528 - 535
• Main Authors: Kaya, Savaş, Tüzün, Burak, Kaya, Cemal, Obot, Ime Bassey
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2016
• Subjects: Amino acids; Corrosion; DFT; Molecular Dynamic simulations; Monte Carlo; Amino acids; DFT; Corrosion; Molecular Dynamic simulations; Monte Carlo
• ISSN: 1876-1070; 1876-1089
• DOI: 10.1016/j.jtice.2015.06.009

•Arginine is a good corrosion inhibitor compared to other amino acids studied.•The agreement between theoretical and experimental results was obtained.•Basic amino acids such as arginine acts as powerful electron donator. Amino acids are biologically important organic compounds in the human body which contain two important functional groups namely: –NH2 (amine) and –COOH (carboxylic acid) in their structures. In the present work, corrosion inhibitive performance amino acids such alanine (Ala), methionine (Met), aspartate (Asp), asparagine (Asn), lysine (Lys), arginine (Arg) and histidine (His) were investigated. All quantum chemical calculations related to these amino acids at the B3LYP/6-31G++(d, p) HF/6-31G++(d,p) methods were performed. Corrosion inhibition effects of the subject amino acids were discussed not only in the gas phase but also in the water phase, acetic acid and formic acid. Furthermore, molecular dynamic simulations employing Monte Carlo sampling approach were applied to search for the most stable configuration and adsorption energies for the interaction of the amino acid corrosion inhibitors on Cu (111)/50 H2O interface. A good correlation between theoretical data and experimental data has been obtained. Moreover, arginine that is a basic amino acid the best corrosion inhibitor among amino acids, considered in this study. [Display omitted] Typical energy profile plots for arginine/Cu (111)/50 H2O system obtained during 5 cycles of the Monte Carlo simulation process.