Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies

A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques

Machine learning (ML) techniques have shown great potential for screening corrosion inhibitors. In this study, a data-driven quantitative structure-property relationship (QSPR) model using the gradient boosting decision tree (GB) algorithm combined with the permutation feature importance (PFI) technique was developed to predict the corrosion inhibition efficiency (IE) of organic compounds on carbon steel. The results showed that the PFI method effectively selected the molecular descriptors most relevant to the IE. Using these important molecular descriptors, an IE predictive model was trained on a dataset encompassing various categories of organic corrosion inhibitors for carbon steel, achieving RMSE, MAE, and R2 of 6.40%, 4.80%, and 0.72, respectively. The integration of GB with PFI within the ML workflow demonstrated significantly enhanced IE predictive capability compared to previously reported ML models. Subsequent assessments involved the application of the trained model to drug-based corrosion inhibitors. The model demonstrates robust predictive capability when validated on available and our own experimental results. Furthermore, the model has been employed to predict IE for more than 1500 drug compounds, suggesting five novel drug compounds with the highest predicted IE on carbon steel. The developed ML workflow and associated model will be useful in accelerating the development of next-generation corrosion inhibitors for carbon steel.

Surfactants as biodegradable sustainable inhibitors for corrosion control in diverse media and conditions: A comprehensive review

BACKGROUND

Corrosion is a challenging and potentially harmful process that involves the continuing, impulsive deterioration of metallic structures via reactions involving environmental components and electro- or chemical processes. To inhibit corrosion, various additives are added. Traditional additives, on the other hand, contain environmentally hazardous substances. Surfactants are less expensive, easier to manufacture, and have high inhibitory efficacy and low toxicity compared to standard corrosion inhibitors. They are often employed as corrosion inhibitors to protect metallic materials against corrosion.

METHODS

Surfactant molecules' amphiphilic nature promotes adsorption at surfaces such as the metal/metal oxide-water interface. Surfactant adsorption on metals and metal oxides forms a barrier that can prevent corrosion.

SIGNIFICANT FINDINGS

This review of surfactants as corrosion inhibitors aims to offer a systemic evaluation of various surfactant physical and chemical properties, surfactant influence in corrosion inhibition, and surfactant used in corrosion inhibition that can be used to enhance the efficacy of surfactant use as corrosion inhibitors in a variety of environments. The effect of several parameters on the potential to suppress corrosion of surfactant molecule series is also discussed here.

Electrochemical, surface morphological and computational evaluation on carbohydrazide Schiff bases as corrosion inhibitor for mild steel in acidic medium

Anticorrosion and adsorption behaviour of synthesized carbohydrazide Schiff bases, namely (Z)-N'-(4-hydroxy-3-methoxybenzylidene)-6-methyl-2-oxo-4-phenyl-1,2,3,4-tetrahydropyrimidine-5-carbohydrazide(MBTC) and (Z)-N'-(3,4-dichlorobenzylidene)-6-methyl-2-oxo-4-phenyl-1,2,3,4-tetrahydropyrimidine-5-carbohydrazide (CBTC) was examined for mild steel (MS) in 15% HCl medium. The corrosion inhibition study was performed by using gravimetric, thermodynamic, electrochemical and theoretical studies including density functional theory (DFT), molecular dynamic simulation (MDS) and Monte Carlo simulations (MCS). The outcomes in terms of corrosion inhibition efficiency using electrochemical impedance spectroscopy (EIS) method at 303 K and 150 ppm concentration were 96.75% for MBTC and 95.14% for CBTC. Both inhibitors adsorbed on the MS surface through physical as well as chemical adsorption and followed the Langmuir isotherm. The mixed-type nature of both inhibitors was identified by polarization results. Surface analysis was done using FESEM, EDX, AFM and XPS studies and results showed that a protective layer of inhibitor molecules was developed over the surface of MS. The results of DFT, MCS and MDS are in accordance with experimental results obtained by weight loss and electrochemical methods.

Essential Oil of Origanum vulgare as a Green Corrosion Inhibitor for Carbon Steel in Acidic Medium

In this study, Oregano (Origanum vulgare) leaf essential oil was studied as an environmental-friendly anticorrosion agent for carbon steel in aggressive hydrochloric acid. The corrosion inhibition of O. vulgare was characterized by surface morphology, electrochemical, weight loss, theoretical and computational methods. It was found that the highest inhibition performance of O. vulgare was 85.64% at 2 g/l in 1 M HCl. The results of Langmuir isotherm and adsorption thermodynamics investigation demonstrated that the O. vulgare inhibitor adsorbed on the metal surface by the formation of rigid covalent bonds. The adsorption and inhibition centers of the selected inhibitor were studied by the computational methods, resulting in that the hydroxyl functional groups and benzoyl rings are mainly responsible for the high inhibition efficiency.

Corrosion Inhibition Studies of 8-Hydroxyquinoline Derivatives for N80 Steel in a 1.0 M HCl Solution: Experimental, Computational Chemistry, and Quantitative Structure-Activity Relationship Studies

Twelve kinds of 8-hydroxyquinoline derivatives were synthesized and characterized. The weight loss method was used to evaluate their inhibition efficiencies (IEs) in a 1.0 M HCl solution at 333 K. The results showed that the alkyl chain length, heteroatoms (S, N, and O), and number of benzene rings significantly affect the IE. Herein, the IE of 5-[(dodecylthio)methyl]-8-quinolinol reached 98.71%. Meanwhile, the potentiodynamic polarization results indicated that all 8-hydroxyquinoline derivatives were mixed-type inhibitors. Electrochemical impedance spectroscopy results revealed that 8-hydroxyquinoline derivatives can increase polarization resistance, supporting their adsorption on the N80 steel surface. Moreover, according to density functional theory (DFT), the frontier orbital distribution and quantum chemical parameters (E_HOMO, E_LUMO, dipole moment μ, etc.) were calculated, and the results confirmed that the substituents of protonated 8-hydroxyquinoline derivatives significantly influenced the frontier orbital distribution. Molecular dynamics simulation illustrated that all protonated 8-hydroxyquinoline derivatives were adsorbed parallel to the Fe(110) surface, and the interaction energy (E_int) evidenced that the molecular size would affect their strength of adsorption on the Fe(110) surface. The linear and nonlinear quantitative structure-activity relationship models were established by linear regression (LR) methods and BP neural networks (NN), respectively. The LR model was established by using E_int and μ, and the coefficient of determination (R²) was 0.934. In addition, the nonlinear NN model was obtained according to IE and all parameters (DFT parameters and E_int). Then, the two calculation inhibition efficiencies (IE_cal) were obtained from the LR and NN models, and the R² values of the linear correlation between the IE_cal and the experimental IE were 0.940 and 0.951, respectively. In addition, the IE of the tested inhibitor was 51.86% and the IE_cal values predicted by the LR and NN models were 52.68% and 53.06%, respectively. Our results demonstrate that both the LR and NN models have good fits and predictive ability.