Computational prediction of nucleobase-derived organic compounds as high-efficiency corrosion inhibitors on Fe, Cu, Al, Sn: a DFT.

CONTEXT: This study aims and objectives of the present research are to provide fundamental insights into their electronic properties and adsorption behavior, and predict the corrosion inhibition performance of seven nucleobase-derived organic compounds-adenine (S1), cytosine (S2), glutamine (S3), guanine (S4), purine (S5), pyrimidine (S6), and thymine (S7)-on Cu, Fe, Al, and Sn metal surfaces. Results revealed that cytosine (S2) and guanine (S4) exhibit the highest inhibition efficiency on Cu surfaces, driven by their small HOMO-LUMO energy gap (~ 5.00, ~ 5.10 eV), high softness (0.203, 0.198 eV-1), and molecule-to-metal electron charge transfer (0.193, 0.237), indicating strong adsorption and electron exchange capabilities from the HOMOdonor ⟶ LUMOacceptor. The importance of the results highlights the potential of these compounds for developing sustainable corrosion inhibitors and advanced Cu-based anticorrosive coatings and biosensors.

METHODS

Using density functional theory (DFT) at the B3LYP/6-311 + + G(d,p) level of theory with applying Gaussian 09, Revision D.01, to optimize the structures combined with Monte Carlo simulations applied to point out interactions surface, and evaluated adsorption energies on Fe (110), Sn (111), Cu (111), and Al (111) surfaces of S1-S7 title compounds. Topological analyses, including electron localization function (ELF), localized orbital locator (LOL), and density of states (DOS), were performed using Multiwfn software to understand electron distribution and bonding characteristics. The comprehension and knowledge gathered from these techniques can help create a prediction of nucleobase-derived capacity to suppress corrosion inhibitors that are both sustainable and effective.