N-heterocycle compounds as aqueous phase corrosion inhibitors: A robust, effective and economic substitute

Exploring the Efficacy of Benzimidazolone Derivative as Corrosion Inhibitors for Copper in a 3.5 wt.% NaCl Solution: A Comprehensive Experimental and Theoretical Investigation

This study focuses on the synthesis, theoretical analysis, and application of the corrosion inhibitor known as benzimidazolone, specifically 1-(cyclohex-1-enyl)-1,3-dihydro-2H-benzimiazol-2-one (CHBI). The structure of CHBI was determined by X-ray diffraction (XRD). The inhibitory properties of CHBI were investigated in a 3.5 wt.% NaCl solution on pure copper using various electrochemical techniques such as potentiodynamic polarization curves (PDPs) and electrochemical impedance spectroscopy (EIS), as well as scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), UV-visible spectroscopy, and theoretical calculations. The obtained results indicate that CHBI is an excellent inhibitor, exhibiting remarkable effectiveness with an inhibition rate of 86.49% at 10-3 M. To further confirm the extent of adsorption of the inhibitory molecule on the copper surface, density functional theory (DFT) and Monte Carlo (MC) simulation studies were conducted. The results of this study demonstrate the synthesis and characterization of CHBI as a corrosion inhibitor. The experimental and theoretical analyses provide valuable insights into the inhibitory performance of CHBI, indicating its strong adsorption on the copper surface.

Corrosion protection properties of tetraphenylethylene-based inhibitors toward carbon steel in acidic medium

Four novel corrosion inhibitors (1, 2, 3 and 4) integrating different tetraphenylethylene (TPE) cations and thiocyanate (SCN^-) anions were developed. Weight-loss and electrochemical measurements were employed to assess their protective properties toward carbon steel in 0.5 M H₂SO₄, revealing them as effective corrosion inhibitors in the order of 3 > 4 > 2 > 1, with the inhibition efficiencies of 2, 3 and 4 all exceeding 97%. The inhibitory effect could be attributed to hard and soft acids and bases theory and the synergistic effect of the charged ingredients. The efficiency trend of the corrosion inhibition, as well as inhibition mechanism, was verified by multi-scaled theoretical simulations combined with grand canonical Monte Carlo and molecular dynamic methods.

Coco Monoethanolamide Surfactant as a Sustainable Corrosion Inhibitor for Mild Steel: Theoretical and Experimental Investigations

Recent studies indicate that surfactants are a relatively new and effective class of corrosion inhibitors that almost entirely meet the criteria for a chemical to be used as an aqueous phase corrosion inhibitor. They possess the ideal hydrophilicity to hydrophobicity ratio, which is crucial for effective interfacial interactions. In this study, a coconut-based non-ionic surfactant, namely, coco monoethanolamide (CMEA), was investigated for corrosion inhibition behaviour against mild steel (MS) in 1 M HCl employing the experimental and computational techniques. The surface morphology was studied employing the scanning electron microscope (SEM), atomic force microscope (AFM), and contact measurements. The critical micelle concentration (CMC) was evaluated to be 0.556 mM and the surface tension corresponding to the CMC was 65.28 mN/m. CMEA manifests the best inhibition efficiency (η%) of 99.01% at 0.6163 mM (at 60 °C). CMEA performs as a mixed-type inhibitor and its adsorption at the MS/1 M HCl interface followed the Langmuir isotherm. The theoretical findings from density functional theory (DFT), Monte Carlo (MC), and molecular dynamics (MD) simulations accorded with the experimental findings. The MC simulation's assessment of CMEA's high adsorption energy (-185 Kcal/mol) proved that the CMEA efficiently and spontaneously adsorbs at the interface.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R² of 0.92 and 0.95, respectively. Acceptable predictive performance (test R² of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.

Green β-cyclodextrin-based corrosion inhibitors: Recent developments, innovations and future opportunities

β-Cyclodextrin-based compounds are used to develop and innovate materials that protect against corrosion due to their sustainability, low cost, environmental friendliness, excellent water solubility and high inhibition efficiency. However, corrosion potentials of β-CD-based compounds were not reviewed with the modern trends. The essence of the problem is that a deep understanding of the development and innovation of β-CD-based compounds as corrosion inhibitors is very important in creating next-generation materials for corrosion protection. In this review, the fundamental behaviour, importance, developments and innovations of β-CD modified with natural and synthetic polymers, β-CD grafted with the organic compounds, β-CD-based supramolecular (host-guest) systems with organic molecules, polymer β-CD-based supramolecular (host-guest) systems, β-CD-based graphene oxide materials, β-CD-based nanoparticle materials and β-CD-based nanocarriers as corrosion inhibitors for various metals were reviewed and discussed with recent research works as examples. In addition, the corrosion inhibition of β-CD-based compounds for biocorrosion, microbial corrosion and biofouling was reviewed. It was found that (i) these compounds are sustainable, inexpensive, environmentally friendly, and highly water-soluble and have high inhibition efficiency; (ii) the molecular structure of β-CD makes it an excellent molecular container for corrosion inhibitors compounds; (iii) the β-CD is excellent core to develop the next generation of corrosion inhibitors. It is recommended that (i) β-CD compounds would be synthesized by green methods, such as using biological sustainable catalysts and green solvents, green methods include irradiation or heating, energy-efficient microwave irradiation, mechanochemical mixing, solid-state reactions, hydrothermal reactions and multicomponent reactions; (ii) this review will be helpful in creating, enhancing and innovating the next green and efficient materials for future corrosion protection in high-impact industries.

A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine

A study of 250 commercial drugs to act as corrosion inhibitors on steel has been developed by applying the quantitative structure-activity relationship (QSAR) paradigm. Hard-soft acid-base (HSAB) descriptors were used to establish a mathematical model to predict the corrosion inhibition efficiency (IE%) of several commercial drugs on steel surfaces. These descriptors were calculated through third-order density-functional tight binding (DFTB) methods. The mathematical modeling was carried out through autoregressive with exogenous inputs (ARX) framework and tested by fivefold cross-validation. Another set of drugs was used as an external validation, obtaining SD, RMSE, and MSE, obtaining 6.76%, 3.89%, 7.03%, and 49.47%, respectively. With a predicted value of IE% = 87.51%, lidocaine was selected to perform a final comparison with experimental results. By the first time, this drug obtained a maximum IE%, determined experimentally by electrochemical impedance spectroscopy measurements at 100 ppm concentration, of about 92.5%, which stands within limits of 1 SD from the predicted ARX model value. From the qualitative perspective, several potential trends have emerged from the estimated values. Among them, macrolides, alkaloids from Rauwolfia species, cephalosporin, and rifamycin antibiotics are expected to exhibit high IE% on steel surfaces. Additionally, IE% increases as the energy of HOMO decreases. The highest efficiency is obtained in case of the molecules with the highest ω and ΔN values. The most efficient drugs are found with pK_a ranging from 1.70 to 9.46. The drugs recurrently exhibit aromatic rings, carbonyl, and hydroxyl groups with the highest IE% values.