Inhibition Efficiency Values Research Articles

Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds

Corrosion is a major challenge affecting various industrial sectors, leading to increased operational costs and decreased equipment efficiency. The use of organic corrosion inhibitors is one of the promising solutions. This study applies an ensemble algorithm with a stacking method to estimate pyridazine-derived compounds corrosion inhibition efficiency. This study utilized various molecular characteristics of pyridazine compounds as inputs to predict inhibition efficiency values. After evaluating several boosting models, the stacking technique was chosen as it showed the best results. Stacking Model 6, which combines XGB, LGBM, and CatBoost as the base model with Random Forest as the meta-model, produced the most accurate prediction with an RMSE of 0.055. These findings indicate that machine learning approaches can effectively and efficiently predict corrosion inhibitor performance. This method offers a faster and more economical alternative to conventional experimental methods.

A sustainable approach for the corrosion control of mild steel using Cocous nucifera gum: An electrochemical investigation

The study of corrosion inhibition on mild steel (MS) in an acid medium (1 M HCl) solution was performed using a crude extract of Cocousnucifera gum (CNG). Cocousnucifera develops gum exudates on its palm, the exudate process occurs mainly after some physical or microbiological injuries and is formed on the fruit of the palm. The gum was utilized to study the corrosion inhibition of MS by electrochemical and surface studies. The characterization of the crude gum was done by Fourier transform infrared spectroscopy (FT-IR) which revealed the presence of several active components (aromatic and oxygen-containing functional groups) in its structure. The electrochemical studies resulted in the maximum inhibition efficiency value of 75.64 % at the highest inhibitor concentration of 4000 ppm at 308 K. It was inferred that both the anodic dissolution of the metal and cathodic hydrogen evolution were effectively decreased in the presence of Cocous nucifera gum extract, revealing that inhibitor is of mixed type. From the adsorption studies, it was found that the best fit with R2 ∼ 0.9 followed Langmuir adsorption isotherm indicating the monolayer adsorption of the inhibitor on the metal surface. Gibb’s free energy of adsorption values was found <−20 kJ/mol, a characteristic of predominant physical adsorption. To support the electrochemical data SEM (scanning electron microscopy), AFM (Atomic force microscopy), and X-ray diffraction analysis were performed and these are in good agreement.

Assessment of the inhibition performance of ZIF-8 on corrosion Mitigation of API 5L X65 steel in 3.5 wt% NaCl: Experimental and theoretical insight

The synthesis and application of ZIF-8 as an effective corrosion inhibitor for the corrosion of API 5L X65 steel in 3.5 wt% NaCl have been studied. The synthesized ZIF-8 was characterized using some surface probe approaches, which include field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HR TEM), and X-ray photoelectron spectroscopy (XPS), respectively. The results showed that ZIF-8 exhibited regular hexagonal and orthorhombic-shaped nanoparticles. An approximately 62% yield was achieved. Electrochemical studies such as open circuit potential (OCP), potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS) were performed. The polarization results show that ZIF-8 is a mixed-type inhibitor, revealing a strong impact on both the anodic and cathodic polarization current values. The EIS studies reveal an increase in the charge transfer resistance upon the introduction of ZIF-8. The existence of a strong protective film adsorbed on X65 steel was recognized via XPS and FT-IR analysis. The highest inhibition efficiency value of 98.1% was recorded at a concentration of 0.120 wt% ZIF-8. Quantum chemical computations in the framework of DFT and molecular dynamic simulation were used to determine the reaction sites on the inhibitor, and as such, in-depth insight into the reaction mechanism was revealed.

Insight into the corrosion resistance of mild steel in an acidic environment in the presence of an organic extract: Experimental and computational approach

Green corrosion inhibitors are crucial in lowering the rate at which metals corrode as well as the negative environmental effects of hazardous substances. Considering this, Curcuma longa (CL) root extract was used as an effective and safe corrosion inhibitor in this study. The inhibitory efficacy was thoroughly investigated using the weight loss (WL), open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP) techniques, respectively. The inhibitor was found to be effective at impeding the rate of deterioration of mild steel in 1 M HCl and 0.5 M H2SO4 environments. In a mixed-type mode, the CL reduced the anodic and cathodic reactions. However, in a 0.5 M H2SO4 environment, CL proved to be slightly more effective compared to a 1 M HCl environment. Inhibition efficiency values of 91.8 % (0.5 M H2SO4) and 89.6 % (1 M HCl) were obtained. The Langmuir adsorption isotherm (LAI) was used to validate the results. Some surface probe analysis such as scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FR-IR), and X-ray photoelectron spectroscopy (XPS), were used to complement the electrochemical results. Quantum chemical computations confirmed some active sites on the molecular structures of a few selected active constituents of the CL extract.

Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs

This study explores the potential of quantum machine learning (QML) 's potential in predicting expired pharmaceutical compounds' corrosion inhibition capacity. This investigation utilized a QSPR model where features derived from density functional theory (DFT) calculations are used as input, while the corrosion inhibition efficiency (CIE) values derived from the experimental study are used as target output. The proposed QML model exhibits varying performance through evaluation metrics, particularly concerning encoding and ansatz design. Notably, the quantum support vector machine (QSVM) demonstrates superior predictive performance compared to the variational quantum circuit (VQC) and quantum neural network (QNN). Specifically, the QSVM model achieves the highest scores in evaluation metrics, with a root mean squared error (RMSE) of 4.36, mean absolute error (MAE) of 3.19, and mean absolute deviation (MAD) of 3.08. The research highlights the importance of larger datasets to improve predictability and emphasizes the potential of QML in investigating anti-corrosion materials. Despite its limitations, this study establishes a foundational framework for utilizing QML to forecast anti-corrosive qualities.

Unveiling green corrosion inhibitor of Aloe vera extracts for API 5L steel in seawater environment

This study evaluated Aloe vera extract as a green inhibitor to prevent corrosion in seawater environments. A. vera extract was produced by maceration with methanol–water at room temperature. Electrochemical techniques were used to evaluate the corrosion inhibitor effectiveness of the A. vera extract. The morphology of the corrosion products was analyzed by FE-SEM equipped with EDS and AFM. FT-IR and LCMS characterized the functional and structural groups in this extract. The electrochemical measurements show that A. vera extract could effectively reduce the corrosion of API 5L steel in seawater environments. Inhibition efficiency (IE) increases with increasing concentration. Optimal corrosion inhibition efficiency of around 83.75% (PDP) and 88.60% (EIS) was obtained by adding 300 mg L−1 of extract at 310 K. Furthermore, the higher the concentration of A. vera extract, the greater the activation energy (Ea), with the highest activation energy being 48.24 kJ mol−1 for the concentration of 300 mg L−1. Conversely, increasing the temperature and exposure duration reduces the corrosion inhibition efficiency (IE) values; the best exposure period was 30 min with 88.34% IE by a concentration of 300 mg L−1 at 300 K. This corrosion inhibition is achieved by the adsorption process of A. vera bioactive on metal surfaces with a mixed inhibitor through a physisorption-chemisorption mechanism. This finding was confirmed by the smoother surface morphology of the steel treated with A. vera extract than without. This unveiling investigation found that A. vera extract has the potential to be an environmentally friendly corrosion inhibitor in the seawater environment.

Corrosion Protection of Mild Steel in Simulated Offshore Environment Using Ocimum gratissimum Leaf Extract

The inhibitive action of Ocimum gratissimum leaf extracts on mild steel corrosion in 1M HCl and 0.5 NaCl solutions was studied using the Gravimetric technique. Four weighed well-polished coupons suspended into sample bottles labeled 200mg/l, 400mg/l, 800mg/l, 1200mg/l and the blank respectively was used. The results shown in Tables 1 to 5 is the weight loss data of mild steel in 0.1 M HCl and the effects of Ocimum gratissimum introduction and how the mild steel corroded progressively thereby increasing the weight loss value over immersion time as seen in Fig. 1. Fig. 2 shows the effects of Ocimum gratissimum extract concentration on the corrosion rate of mild steel in HCl, it is evident that increase in the concentration of the extract led to the decrease in the value of weight loss leading to appreciable inhibition efficiency. The plot in Fig. 3 showed that the values of the inhibition efficiency increased with the concentration of Ocimum gratissimum extract in the acidic environment except the value for 9 days which decreased with concentration. These results led to decrease in the values of inhibition efficiency over time as shown in Fig. 4. Tables 6 to 10 shows the weight loss data for the corrosion of mild steel in 0.5 M NaCl and the effects of Ocimum gratissimum introduction. The result showed that mild steel corroded, leading to fluctuations in the inhibition efficiency. Figs. 5-8 showed the variations of efficiency with time with different concentrations of Ocimum gratissimum in 0.5 M NaCl. Addition of Ocimum gratissimum extract synergistically increased the efficiency of the inhibitor and helped reduce the corrosion rate. A mechanism of adsorption is proposed to explain inhibition behaviour.

Synthesis and characterization of selenium-polyvinyl alcohol nanoparticles as eco-friendly corrosion inhibitor for carbon steel in 1.0 M H2SO4

Selenium nanoparticles capped with polyvinyl alcohol (Se-PVA NPs) have been synthesized by chemical reduction using ascorbic acid as a reducing agent. The synthesized nanoparticles were characterized using XRD, DLS, zeta potential, FT-IR, and TEM analysis and found to be stable. The solution of Se-PVA NPs was used as a carbon steel corrosion inhibitor in 1.0 M H2SO4. The effect of the presence of Se-PVA NPs in the corrosion medium was studied by potentiodynamic polarization and gravimetric analyses In the presence of Se-PVA NPs with a concentration of 600 ppm at 303 K, corrosion inhibition efficiency values of 92.86 % and 92.3 % were recorded by potentiodynamic polarization and gravimetric analysis methods, respectively. Over the Se-PVA NPs concentration range 100–600 ppm, it was found that increasing the concentration increases the inhibition efficiency, while the temperature has an adverse effect, inhibition efficiency values of 80.0 % was recorded at 323 K for the 600 ppm Se-PVA NPs solution. The potentiodynamic polarization measurements suggested that Se-PVA NPs work as a mixed-type inhibitor. The corrosion inhibition process was confirmed by testing the used specimens’ metal surface using SEM and AFM techniques, which showed that the acidic medium’s negative impact (distortion and roughness) on the carbon steel surface was reduced significantly in the presence of Se-PVA NPs. The inhibitor’s adsorption on the metal surface was studied. It obeys the Langmuir isotherm with a mixed-type adsorption process. The related kinetic and thermodynamic parameters were investigated.

Ensemble Learning Model in Predicting Corrosion Inhibition Capability of Pyridazine Compounds

Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys.

A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.

Comparative Investigation of the Corrosion Protection Efficacy of Spondias mombin Leaf Extracts on AA2024 Alloy and Low Carbon Steel in Acidic Medium

The anti-corrosive property of Spondias mombin (SM) leaves extract on low carbon steel (LCS) and aluminium alloy (AA2024) in 1M HCl solution was investigated using gravimetric and electrochemical methods. The results obtained from the weight loss experiment performed at various temperatures (30, 45, and 60 0C) show that the inhibition efficiency values decreased with temperature rise but increased as the inhibitor concentrations were increased. At 30 0C, optimum inhibition efficiency (IE) of 80.11% and 78.06% were obtained for LCS and AA2024 respectively. The potentiodynamic polarization (PDP) result reveal that an optimum IE values of 92.1% and 74.0% were respectively obtained for LCS and AA2024 while the electrochemical impedance spectroscopy (EIS) data show IE values of 91.5% and 74.5% for LCS and AA2024 respectively. In all cases, the values of inhibition efficiency obtained for LCS were greater than those obtained for AA2024. This implies that SM leaf extracts offers a better protective-layer on LCS surface compared to the AA2024 alloy in the acidic medium. The isotherm study reveal that Langmuir model best fitted the adsorption of the leaf extracts on LCS while Freundlich model best fitted the leaf extracts adsorption on AA2024 surface.

Computational Modelling and Comparative Analysis of aSchiff Base Ligand and Its Analog as Inhibitors Against Mild Steel Corrosion in 1M HCl

Schiff bases, alternative anticorrosive additive wassynthesized, characterized and investigated for the inhibition of mild steel corrosion in 1M Hydrochloric acid at concentrations of 20 ppm, 40 ppm, 60 ppm, 80 ppm and 100 ppm using weight loss (WL) and electrochemical methods. The novel Schiff base ligands obtained were characterized by Ultraviolet-visible and Fourier-Transform Infrared Spectroscopy. The elemental analysis data for the Schiff base ligands were used to confirm the general formula of the Schiff bases. Fourier-Transform Infrared spectroscopy provided evidence of formation of a complex surface film due to adsorption of the Schiff bases on the mild steel surface. Maximum inhibition efficiency for Schiff base ligand 1 (SBL1) and Schiff base ligand 2 (SBL2) obtained were respectively 76.92% and 86.21% at concentrationof100ppm,andthetrendfollowedSBL2&gt;SBL1, indicating the effectiveness of SBL2 in corrosion prevention as compared SBL1. Potentiodynamic polarization(PDP) measurements showed that the Schiff bases acted as mixed type inhibitors. Electrochemical Impedance Spectroscopic (EIS) measurement revealed that the corrosion process was controlled by charge transfer process. Inhibition efficiency values obtained from the different techniques were comparable. Quantum chemical parameters such as highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital(LUMO) and energy gap (ΔE) were obtained using Hartree-fock Density Functional Theory by Hamiltonian method. The results showed that SBL2 was more reactive than SBL1. In conclusion, the inhibition of mild steel corrosion was due to adsorption of active molecules leading to formation of a protective layer on surface of mild steel.

Exploring mechanism of corrosion inhibition of WE43 and AZ31 alloys by aqueous molybdate in Hank’s solution by multisine impedimetric monitoring

The concept of utilizing multisine dynamic electrochemical impedance spectroscopy to monitor effectiveness of a model molybdate inhibitor for AZ31 and WE43 Mg alloys is proposed. The corrosion kinetics and instantaneous values of inhibition efficiency (IE) of molybdate at concentrations up to 150 mM were examined. The inhibitor provides inhibition in Hank’s solution at concentrations starting from ca. 25 mM, with the IE of about 90%. These data together with results of the surface analysis allowed to propose the mechanism of the corrosion inhibition of AZ31 and WE43 Mg alloys by aqueous molybdate in Hank’s solution.

Efficiency of alcohol and ester-imidazole in preventing mild steel corrosion: An integrated approach combining experimental and computational studies

In the present work, the two imidazopyridine derivatives namely ethyl 6,8-dibromoimidazo [1,2-a] pyridine-2-carboxylate (ES-IMD) and (5-methylimidazo [1,2-a] pyridine-2-yl) methanol (OL-IMD) were presented as powerful anti-polarizing agent for steel surface in aggressive system related to the obtained results in experimental and computational methods. 1H NMR and mass spectrometry (MS) were used to confirm the structures of resynthesized molecule. We assessed the effectiveness of OL-IMD and ES-IMD inhibitors in safeguarding mild steel from corrosion in a 1 M hydrochloric acid environment. We adopted an integrated experimental and computational approach to evaluate the inhibition efficiency and mechanism of OL-IMD and ES-IMD, utilizing gravimetric method, polarization curves and electrochemical impedance spectroscopy. Quantum chemical calculations and molecular dynamics simulations provided atomic-level insights into adsorption behaviour. Scanning electron microscopy characterized surface morphology. Findings revealed that OL-IMD and ES-IMD compounds performed as mixed-type inhibitors. At the greatest inhibitor concentration (10−3 M), the polarization curves gave inhibition efficiency values of 98.91% (ES-IMD) and 95.42% (OL-IMD). Thermodynamic activation parameters indicated a spontaneous adsorption process of OL-IMD and ES-IMD on the steel surface, which effectively increased the energy barrier for the corrosion process. The EIS parameters demonstrated that when inhibitor concentration raised, the double-layer capacitance (Cdl) dropped which suggested that the dielectric constant was decreasing. The results reveal a substantial correlation produced by the three analysed approaches EIS, PDP, and WL. Thermodynamic activation parameters indicated a spontaneous adsorption process of OL-IMD and ES-IMD on the steel surface, which effectively increased the energy barrier for the corrosion process. Furthermore, the calculated ΔGads values for the considered compounds were -47.39 (kJ/mol) and 49.26 (kJ/mol) for OL-IMD and ES-IMD respectively, proving the chemical nature of the adsorption. Furthermore, SEM surface studies displayed a homogeneous surface while the tested inhibitors were present, but a damaged surface in the blank solution. The theoretical models closely aligned with the experimental data, presented an excellent overview of the investigated inhibitors' reactivity against mild steel confirming the validity of the findings.

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors

This paper presents a quantitative structure–property relationship (QSPR)-based machine learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values in natural organic inhibitor compounds. The modeling dataset comprises 50 natural organic compounds, with 11 quantum chemical properties (QCP) serving as input features, and the target variable being the corrosion inhibition efficiency (CIE) value. To enhance the predictive accuracy of the ML model, the kernel density estimation (KDE) function is employed to generate virtual samples during the training process, with the overarching goal of refining the precision of the ML model. Three distinct models, namely random forest (RF), gradient boosting (GB), and k-nearest neighbor (KNN), are tested in the study. The results demonstrate a noteworthy enhancement in the prediction performance of the models, attributable to the incorporation of virtual samples that effectively improve the correlation between input features and target values. Consequently, the accuracy of the predicted CIE values is significantly augmented, aligning more closely with the actual CIE values. Performance improvements were evident across all models after the incorporation of virtual samples. The GB, RF, and KNN models exhibited increments in R2 values from 0.557 to 0.996, 0.522 to 0.999, and 0.415 to 0.994, respectively, concomitant with the introduction of 500 virtual samples. Additionally, each model demonstrated a notable reduction in RMSE values, transitioning from 1.41 to 0.19, 1.27 to 0.10, and 1.22 to 0.16, respectively. While the GB model initially outperformed others before the addition of virtual samples, the performance of the model exhibited fluctuation as the number of virtual samples varied. This behavior suggests that the KDE function provides a certain level of resilience against model variations. The proposed approach contributes to the effective design and exploration of corrosion inhibitor candidates, offering a reliable and accurate predictive tool that bridges the gap between theoretical studies and experimental synthesis.

A MIXTURE OF ACACIA BARK EXTRACT (Acacia mangium Willd) AND POTASSIUM IODIDE AS A CORROSION INHIBITOR IN SULFURIC ACID SOLUTION

Corrosion is a decrease in the quality of a metal material caused by a reaction with the environment. Corrosion cannot be eliminated, but corrosion can be slowed down in several ways by adding corrosion inhibitors and synergistic effects. Acacia bark extract (Acacia mangium Willd) with the addition of 0.02 M KI has the potential as a corrosion inhibitor for steel because it contains secondary metabolites, so it can provide a synergistic effect and increase the value of inhibition efficiency on steel. This research aimed to analyze the effect of a mixture of acacia bark extracts (Acacia mangium Willd) with the addition of KI on inhibition efficiency corrosion of mild steel in sulfuric acid media. The method used in this study is the method of weight loss, adsorption isotherms, and thermodynamic parameters, which were carried out by varying the concentration of acacia bark extract and the immersion temperature of the steel. To strengthen the research results, characterization was carried out using FTIR and SEM. The results showed that the corrosion rate of mild steel immersed in 0.75 M H2SO4 corrosive medium with the addition of 0.02 M KI increased with increasing immersion temperature. These results are reinforced by the results of FTIR and SEM analysis which show that there is an interaction between mild steel and acacia bark extract (Acacia mangium Willd) and KI 0.02 M. Based on the thermodynamic parameters, the resulting values of ∆Hads, ∆Gads, and ∆Sads show that the adsorption process of acacia bark extract (Acacia mangium Willd) in corrosive medium H2SO4 0.75 M with the addition of 0.02 M KI took place spontaneously, showing the type of physical adsorption, adsorbed stable, the adsorption process was exothermic and the degree of irregularity in the adsorption process. The inhibition efficiency of acacia bark extract (Acacia mangium Willd) with the addition of 0.02 M KI was highest at 60 °C with an extract concentration of 2.5 g/L of 81%. The higher the immersion temperature, the greater the resulting synergistic effect

Perbandingan Algoritma Multilinear Regression dan Decision Tree Regressor dalam Memprediksi Efisiensi Penghambatan Korosi Piridazin

Corrosion is one of the main problems in various industries, causing increased production costs, maintenance expenses, and decreased equipment efficiency. Inhibitors, especially organic compounds, have become an increasingly sought-after solution to reduce corrosion in an effective and environmentally friendly manner. This research aims to compare linear algorithms based on multilinear regression (MLR) and nonlinear algorithms based on decision tree regressor (DTR) for a case study of predicting corrosion inhibition efficiency (CIE) values of pyridazine derivative compounds as corrosion inhibitors. This research is a data-based theoretical study using a machine learning (ML) approach. In this study, we used a dataset of pyridazine compounds from published literature consisting of 20 pyridazine compounds with molecular properties as features (independent variables) and CIE values as targets (dependent variables). The analysis consists of analyzing the prediction model's performance and important features that support model performance. The research results show that the DTR-based nonlinear model has better performance than the MLR-based linear model based on evaluation metrics and prediction data distribution plots. We also find that the molecular properties fraction of electron transferred (∆N) and electron affinity (A) respectively emerge as the features most responsible for the prediction performance of the DTR model. We conclude that the DTR-based nonlinear algorithm can be used as an accurate and reliable predictive model to predict the corrosion inhibition ability for potential new pyridazine derivative compounds.

Water-soluble chitosan salt as ecofriendly corrosion inhibitor for N80 pipeline steel in artificial sea water: Experimental and theoretical approach

Chitosan, as a proficient biopolymer, has enormous potential as an ecofriendly corrosion inhibitor (CI), but their limited solubility restricts practical applications. Herein, an eco-friendly and water-soluble chitosan salt (CS) was utilized as a green CI on N80 pipeline steel in artificial sea water. Several structural and surface analytical tools were engaged in describing the characteristics of novel CS polymer. The corrosion inhibition efficiencies of CS on steel at different concentrations were investigated through gravimetric, conventional and advanced electrochemical techniques along with the surface analyses. Tafel polarization tests specified that CS performed as mixed-type CI with prevalent anodic inhibition characteristics. At a concentration of 1000 ppm, CS provided an inhibition efficiency (IE) of 96.68 %, following physiochemical adsorptions of CS on N80 surface validated by fitting Langmuir adsorption isotherm. However, the reductions in the values of IE at high temperature specified that the CS is the temperature dependent CIs. Scanning electrochemical microscopic evaluation confirmed the formation of thin CS inhibitors films with high electrochemical stability on N80 steel in saline. The performed surface characterizations on inhibited surfaces validated the adsorption of CS on the N80 surface by forming thin inhibitor film to obstruct metal corrosion. The theoretical simulation studies using molecular dynamics and density functional theory corroborated the experimentally obtained results.

Assessing the potentials of free base and gallium metalated tertbutylphthalocyanines as aluminium corrosion inhibitors

The adsorption behaviors of 2,9,16,23 tetrakis(4-(tert-butyl)phenoxy)phthalocyanine (1) and gallium (III) 2,9,16,24 tetrakis(4-(tert-butyl)phenoxy)phthalocyanine (2) were studied as corrosion inhibitors at the aluminum−HCl solution interface. In addition to weight loss (gravimetric) and electrochemical methods, the study includes measurements utilizing scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and X-ray diffraction. The findings of the characterization show that the inhibitor molecules were well adsorbed on the aluminum surface, supporting the gravimetric and electrochemical measurements. At 28 °C and the greatest inhibitor concentration (10 μM), the potentiodynamic polarization method gave inhibition efficiency values of 95.2% (1) and 89.1% (2). These compounds showed good corrosion inhibition efficiencies as mixed-type corrosion inhibitors, but they also had a significant anodic character. Theoretical simulations produced conclusions that closely match experimental data.

Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors

This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three new amino acid compounds, namely L-asparagine (LA), L-isoleucine (LI), and L-proline (LP). The result is that the Gradient Boosting Regressor (GBR) model is proven to be the best predictive model based on the coefficient of determination (R2) and root mean square error (RMSE) metrics used. The study found that the three amino acid compounds LA, LI, and LP tested had high CIE values, ranging from 90.49% to 93.67%. These results are also relevant to the CIE values resulting from experimental studies and show a trend that is by the adsorption energy trend. This engineering breakthrough can be used to predict the corrosion inhibition properties of new compounds before experimental synthesis.