Corrosion inhibition properties of small peptides: DFT and Monte Carlo simulation studies

Artificial intelligence driven molecule adsorption prediction (AIMAP) applied to chirality recognition of amino acid adsorption on metals

Predicting the adsorption structure of molecules has long been a challenging topic given the coupled complexity of surface binding sites and molecule flexibility. Here, we develop AIMAP, an Artificial Intelligence Driven Molecule Adsorption Prediction tool, to achieve the general-purpose end-to-end prediction of molecule adsorption structures. AIMAP features efficient exploration of the global potential energy surface of the adsorption system based on global neural network (G-NN) potential, by rapidly screening qualified adsorption patterns and fine searching using stochastic surface walking (SSW) global optimization. We demonstrate the AIMAP efficiency in constructing the Cu-HCNO6 adsorption database, encompassing 1 182 351 distinct adsorption configurations of 9592 molecules on three copper surfaces. AIMAP is then utilized to identify the best adsorption structure for 18 amino acids (AAs) on achiral Cu surfaces and the chiral Cu(3,1,17) S surface. We find that AAs chemisorb on copper surfaces in their highest deprotonated state, through both the carboxylate-amino skeleton and side groups. The chiral recognition is identified for the d-preference of Asp, Glu, and Tyr, and l-preference for His. The physical origin for the enantiospecific adsorption is thus rationalized, pointing to the critical role of the competitive adsorption between functional side groups and the carboxylate-amino skeleton at surface low-coordination sites.

New Inhibitor Based on Hydrolyzed Keratin Peptides for Stainless Steel Corrosion in Physiological Serum: An Electrochemical and Thermodynamic Study

Reducing the impact of some biological fluids on bioimplants involves the control of surface characteristics by modeling the interface architecture and assembling ecofriendly thin films to retard corrosion. Therefore, a mixture of hydrolyzed keratin peptides (HKER) was investigated as a corrosion inhibitor for 304L stainless steel (SS) in physiological serum (PS), using electrochemical measurements associated with optical microscopy and atomic force microscopy (AFM). The tests, performed for various concentrations of the inhibitor at different temperatures, showed that the inhibition efficiency (IE) decreased with a rise in temperature and proportionally increased with the HKER concentration, reaching its maximum level, around 88%, at 25 °C, with a concentration of 40 g L-1 HKER in physiological serum. The experimental data best fitted the El-Awady adsorption model. The activation parameters (Ea, ∆Ha and ∆Sa) and the adsorption ones (∆Gads0, ∆Hads, ∆Sads) have highlighted a mixed action mechanism of HKER, revealing that physisorption prevails over chemisorption. AFM parameters, such as the average roughness (Ra), root-mean-square roughness (Rq) and maximum peak-to-valley height (Rp-v), confirmed HKER adsorption, indicating that a smoother surface of the 304L stainless steel was obtained when immersed in a PS-containing inhibitor, compared to the surface designed in blank solution, due to the development of a protective layer on the alloy surface.

Adaptive Peptide Molecule as the Promising Highly-Efficient Gas-Sensor Material: In Silico Study

Gas sensors are currently employed in various applications in fields such as medicine, ecology, and food processing, and serve as monitoring tools for the protection of human health, safety, and quality of life. Herein, we discuss a promising direction in the research and development of gas sensors based on peptides-biomolecules with high selectivity and sensitivity to various gases. Thanks to the technique developed in this work, which uses a framework based on the density-functional tight-binding theory (DFTB), the most probable adsorption centers were identified and used to describe the interaction of some analyte molecules with peptides. The DFTB method revealed that the physical adsorption of acetone, ammonium, benzene, ethanol, hexane, methanol, toluene, and trinitrotoluene had a binding energy in the range from -0.28 eV to -1.46 eV. It was found that peptides may adapt to the approaching analyte by changing their volume up to a maximum value of approx. 13%, in order to confine electron clouds around the adsorbed molecule. Based on the results obtained, the prospects for using the proposed peptide configurations in gas sensor devices are good.