American Journal of Chemistry

p-ISSN: 2165-8749    e-ISSN: 2165-8781

2025;  15(1): 1-9

doi:10.5923/j.chemistry.20251501.01

Received: Dec. 13, 2024; Accepted: Jan. 10, 2025; Published: Jan. 21, 2025

 

Reactivity Analyzing of Some Benzimidazole Derivatives in Inhibiting Aluminum Corrosion in Nitric Acid Solution

Toure Hadja Rokia1, Mougo Andre Tigori2, Aphouet Aurelie Koffi1, Paulin Marius Niamien1

1Matter Constitution and Reaction Laboratory, Training and Research Unit for the Sciences of Matter Structures and Technology, University of Felix Houphouet Boigny, Abidjan, Côte d’Ivoire

2Environmental Sciences and Technologies Laboratory, Environmental Training and Research Unit, University of Jean Lorougnon Guede, Daloa, Côte d’Ivoire

Correspondence to: Mougo Andre Tigori, Environmental Sciences and Technologies Laboratory, Environmental Training and Research Unit, University of Jean Lorougnon Guede, Daloa, Côte d’Ivoire.

Email:

Copyright © 2025 The Author(s). Published by Scientific & Academic Publishing.

This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/

Abstract

Gravimetric tests, density functional theory (DFT) and quantitative structure-property relationship (QSPR) calculations were employed to study three benzimidazole derivatives reactivity in inhibiting aluminum corrosion in nitric acid media. These compounds are: 2-(((4-chlorobenzyl) thiol) methyl) -1H-benzo[d]imidazole (2-4-CBTM1HBI), 2-(((4-chlorobenzyl) thio) methyl) 1-H benzo[d] imidazole(M-4 1HBI-2MTMB) and 2-((benzylthio) methyl) 1-H benzo[d] imidazole (2-BTM1HBI). Experimental data at 298K and Cinh= 5. 10-3M indicate that the inhibitory efficiencies of 2-4-CBTM1HBI, M-4 1HBI-2MTMB and 2-BTM1HBI are 97.74%, 98.51% and 96.09% respectively. These values show that the compound M-4 1HBI-2MTMB has the best inhibition performance. DFT calculations of quantum chemical descriptors in 6-311++G (d, p) basis with B3LYP functional clearly explained how each molecule inhibits aluminum corrosion in the solution studied. Which theoretical parameters support the good inhibition efficiency of M-4 1HBI-2MTMB. The statistical results of the QSPR (Quantitative Structure-Activity Relationship) model indicate that the set of parameters (ΔN, ω, χ) provides a robust relationship between theoretical and experimental data. Thus, the theoretical approach used in this study is in line with the available experimental results.

Keywords: Gravimetric tests, Density functional theory, Quantitative Structure-Activity Relationship, Benzimidazole derivatives, Inhibiting aluminum corrosion

Cite this paper: Toure Hadja Rokia, Mougo Andre Tigori, Aphouet Aurelie Koffi, Paulin Marius Niamien, Reactivity Analyzing of Some Benzimidazole Derivatives in Inhibiting Aluminum Corrosion in Nitric Acid Solution, American Journal of Chemistry, Vol. 15 No. 1, 2025, pp. 1-9. doi: 10.5923/j.chemistry.20251501.01.

Article Outline

1. Introduction
2. Experimental Part
    2.1. Gravimetric Experiments
    2.2. DFT Calculations
    2.3. Quantitative Structure-Property Relationship (QSPR) Simulation
3. Results and Discussion
    3.1. Gravimetric Data Analysis
    3.2. Theoretical Data Analysis
        3.2.1. Choosing the Right Calculation Basis
        3.2.2. Global Reactivity of Inhibitors
        3.2.3. Quantitative Structure-Property Relationship Calculation Analyzing
4. Conclusions

1. Introduction

Corrosion is a natural phenomenon which strongly affects metals, leading to frequent repair and maintenance requirements in various industrial sectors. Maintenance operations are often aimed at extending the life of metal equipment, although their effectiveness is limited over time [1]. As a result, the protection of metals and alloys against corrosion becomes essential to reduce the need for frequent repairs. This protection requires the adoption of techniques adapted to each type of metal or alloy and to specific environmental conditions. Aluminum, widely used because of its excellent properties, is particularly prone to the formation of scale on its surface. Tartar, a product of corrosion, adversely affects the performance of metal equipment, making regular descaling and cleaning with acid solutions necessary. To guarantee the durability of such equipment and minimize metal dissolution in acid solutions, the use of corrosion inhibitors is increasingly recommended [2-4]. These inhibitors, often organic compounds, act by slowing down corrosion by forming a protective layer on metals, or by modifying the environment to make it less favorable to corrosion [6]. They can be added directly to corrosive media or incorporated into surface treatment products such as paints. Current research is focusing on the discovery of environmentally-friendly and effective inhibitors, combining experimental and theoretical approaches [7-9]. Among these approaches, density functional theory (DFT) has shown itself to be a promising method for better understanding the reactivity of organic molecules [10]. Indeed, DFT offers detailed information on the interactions between an inhibitor and the metal surface, while explaining the actual behavior of the inhibitor in contact with the metal [11,12]. Studies have shown that various organic compounds can inhibit metal corrosion using this theoretical approach, which is based on quantum chemistry [13,14]. DFT provides data on the electronic properties of inhibitors and their ability to adsorb to metal surfaces [15]. The computational descriptors obtained by this method can be used to characterize the reactivity of inhibitors and provide an in-depth explanation of their inhibition mechanism. This characterization, based on the quantitative structure-property relationship (QSPR), enables experimental research to be directed and new, more effective molecules to be synthesized. The aim of this study is to investigate the reactive properties of three benzimidazole derivatives in the inhibition of aluminum corrosion, in 1M nitric acid medium, using gravimetric, DFT and QSPR methods. The specific aim is to compare experimental data with theoretical results and to determine the quantum chemical parameters capable of linking these different data.

2. Experimental Part

2.1. Gravimetric Experiments

The aluminum samples, cylindrical in shape (1 cm in height and 0.25 cm in diameter), were carefully polished with abrasive paper of decreasing grit size, ranging from 40 to 600. After sanding, they were rinsed with distilled water, then cleaned in an acetone solution before being rinsed again with distilled water. The samples were then dried in an oven at 80°C. Once dried, each sample was weighed. Each pretreated sample was then immersed in 50 mL of 1M nitric acid solution, with or without inhibitor, for one hour. After this period, the samples were removed from the solution, dried again, then weighed. Experiments were carried out at a temperature of 298 K and an inhibitor concentration of 5 × 10-3 M. These conditions were chosen as they enabled optimum inhibition efficiency values to be obtained in the gravimetric tests.
Three molecules used in this work were synthesized and characterized by a team at Organic Chemistry and Natural Synthesis Laboratory, Felix Houphouët Boigny University. The characteristics of these compounds are listed in table 1.
Table 1. Molecular structure and gross formula of inhibitors
     
The corrosion rate (W), aluminum recovery rate (θ) and inhibition efficiency of each inhibitor (IE(%)) are expressed from the following expressions [16]:
(1)
(2)
Where:
W0: corrosion rate in the absence of inhibitor;
W: corrosion rate in the presence of inhibitor.
(3)

2.2. DFT Calculations

DFT calculations were performed using Gaussian 03 [17] with the B3LYP (Becke, three parameter, Lee-Yang-Parr exchange correlation function) [18,19]. The molecular structures of the inhibitors were geometrically optimized using DFT.
These calculations were performed in the bases 6-31G (d), 6-31+G (d,p) and 6-311++G (d,p). Global reactivity quantum chemical parameters such as highest occupied orbital energy (EHOMO), lowest vacant orbital energy (ELUMO), energy gap (∆E), dipole moment(μ), electronegativity (𝜒), hardness (𝜂), softness (S), electrophilicity index (𝜔), electroaccepting power (ω+) (omega at power plus close parenthesis and electrodonating power (ω-) were calculated. The expressions used to access these quantities are given as follows [20-24]:
(4)
(5)
(6)
(7)
(8)
(9)
(10)
In this case and denote the absolute electronegativity of copper and inhibitor molecule respectively. The calculations were performed using the following theoretical values: [25] and [25]
(11)
(12)
(13)

2.3. Quantitative Structure-Property Relationship (QSPR) Simulation

Studying the inhibitory reactivity of a molecule also involves predicting the activity of analogous molecules in this domain. In this work, a quantitative structure-property relationship approach was applied to correlate the experimental inhibitory efficiencies of each molecule with the quantum chemical parameters obtained by DFT calculations [26,27]. This process is based on the model of Lukovits et al [28], which aims to identify a set of quantum chemical parameters capable of predicting theoretical inhibitory efficacy values from experimental data. The linear model used is based on the following relationship:
(14)
Four inhibitor concentrations were used in the study: 100 µM, 500 µM, 1000 µM and 5000 µM. Sets of three parameters were tested to analyze the data obtained. This approach results in a system of four equations with four unknowns: A, B, D and E. The aim is to determine the values of the coefficients A, B, D and E for each molecule, so that the theoretical inhibition efficiency corresponds as closely as possible to that measured experimentally. The calculations required to solve this system were carried out using EXCEL software.

3. Results and Discussion

3.1. Gravimetric Data Analysis

The evolution of inhibition efficiency (IE) as a function of each inhibitor is shown in Figure 1.
Figure 1. IE versus inhibitor at 298K and 5. 10-3M
Figure 1 examination shows that compound M-4 1HBI-2MTMB has the highest inhibition efficiency value at 298K and concentration Cinh = 5.10-3M. This high performance reveals that M-4 1HBI-2MTMB absorbs strongly on aluminum surface at low temperatures.

3.2. Theoretical Data Analysis

3.2.1. Choosing the Right Calculation Basis
Appropriate base choice base was made on basis calculation time and energy gap value. Calculations were performed in three different bases: 6-31G (d, p), 6-31+ G (d, p) and 6-311++G (d, p). The evolution of calculation time and energy gap (∆E) are shown in Figures 2 and 3 respectively.
Figure 2. Theoretical computations time versus basis set for each inhibitor
Figure 3. Energie gap (∆E) versus basis set for each inhibitor
Analysis of Figures 2 and 3 shows that, for all molecules, the computation time and energy gap are relatively lower for the basis set 6-311++G (d, p) than for 6-31G and (d, p), 6-31+ G (d,p). The 6-311++G (d, p) basis will therefore be used to calculate the other quantum chemical parameters. In fact, this triple Zeta basis with diffuse functions enables us to better describe the anisotropic distribution of electrons in space, and to obtain reliable results.
3.2.2. Global Reactivity of Inhibitors
Quantum chemical parameters calculations of each inhibitor were based on structures optimized in 6-311++G (d, p) basis with B3LYP functional. These optimized structures are shown in Figure 4. Table 2 shows the global reactivity parameter values obtained in the 6-311++G (d, p) basis with the B3LYP functional.
Table 2. Global reactivity parameter values for the inhibitors studied
     
Figure 4. Optimized structures for different inhibitors
The ability of an organic compound to readily donate its electrons to a suitable acceptor depends on its highest occupied molecular orbital energy value (EHOMO). Indeed, a high EHOMO value reveals the compound's good ability to supply electrons to any suitable acceptor [29]. The EHOMO values obtained for the compounds studied are in the order:
EHMO(2-BTM1HBI)>EHOMO(2-4-CBTM1HBI)>EHOMO(M-41HBI-2MTMB). This order suggests that the 2-BTM1HBI compound is more inclined to donate electrons to aluminum, so it would imbibe more than the other two compounds. These theoretical data do not agree with the experimental results. Furthermore, the lowest vacant molecular orbital energy values of the compounds are in the order:
ELUMO(2-BTM1HBI)> ELUMO(2-4-CBTM1HBI) > ELUMO (M-41HBI-2MTMB). According to the literature, any compound with a low ELUMO value is more likely to receive electrons [29,30]. These results confirm that the good inhibition performance of M-41HBI-2MTMB obtained experimentally stems from its ability to receive electrons from aluminum.
This exchange of electrons between each inhibitor and the metal indicates the formation of covalent bonds. The electron distribution at each molecular orbital is illustrated in Figure 5.
Figure 5. Electron distribution density of HOMO and LUMO orbitals by B3LYP/6-311++G (d)
The ionization energy (I) and electron affinity (A) values show that there is a strong interaction between each inhibitor and the metal [30].
As for the energy gap (∆𝐸) values, they are in the following order: ΔE(M-41HBI-2MTMB) < ΔE(2-4-CBTM1HBI) < ΔE(2-BTM1HBI). The lower ∆𝐸 is, the more exchanges between molecule and metal are favored for each inhibitor [31,32].
The values obtained indicate that the various molecules are reactive, as the energy required to remove an electron from the last occupied orbital will be low. The compound M-41HBI-2MTMB, which has the lowest value, has excellent inhibition activity compared with the other two. This excellent reactivity is due to the presence of several heteroatoms (N, O, S).
These heteroatoms are the basis for electronic exchanges between the metal and the inhibitor. This result is in line with the experimental data obtained.
According to some authors, low values of dipole moment (µ) favor the accumulation of inhibitor molecules on the metal surface, thus increasing inhibition efficiency [33,34] This suggests that the high inhibition efficiencies obtained can be justified by these low values of the molecule’s dipole moment. Compound M-41HBI-2MTMB has the highest value for softness (𝜎) and the lowest value for hardness (η). These values indicate that this molecule is soft and reacts readily with aluminum to form a complex capable of reducing the dissolution of the metal in the solution studied [35]. These theoretical data are in line with experimental tests.
The positive values for the fraction of electrons transferred and electronegativity describe the electron transfer between each inhibitor and the metal. The order N(M-41HBI-2MTMB)>ΔN(2-BTM1HBI) >ΔE(2-4-CBTM1HBI) obtained from the theoretical values justifies the good inhibition action of the molecules studied. Consequently, M-41HBI-2MTMB is the best inhibitor [36].
The high values of the electrophilicity index (ω) obtained relate to the electrophilic character of each inhibitor [29]. In this case, the compounds studied are more likely to readily accept electrons from the metal or would readily undergo nucleophilic attack. In addition, the electron acceptor value of electroaccepting power 𝜔+ of each inhibitor is closer to electron affinity values (A). This proximity confirms the electrophilic nature of the inhibitors studied [37].
The negative total energy (ET) values indicate a strong interaction between each inhibitor and aluminum, highlighting the ease of electronic transactions between the two species [38].
3.2.3. Quantitative Structure-Property Relationship Calculation Analyzing
The correlation between experimental and theoretical values was carried out using quantitative structure-property relationship (QSPR) method [39]. For this approach, experimental values measured at 298 K were used. Several sets of parameters were tested for each inhibitor. We selected the sets for which the inhibition efficiency values obtained were closest to the experimental values. The calculated coefficient values A, B, C and D are listed in Table 3. Figures 6, 7, 8 show the different correlations between experimental and theoretical data for each inhibitor.
Table 3. Values of coefficients A, B, C and D
     
Figure 6. Experimental and theoretical inhibition efficiency correlation for 2-4-CBTM1HBI
Figure 7. Experimental and theoretical inhibition efficiency correlation for M-41HBI-2MTMB
Figure 8. Experimental and theoretical inhibition efficiency correlation for 2-BTM1HBI
In order to validate this correlation from this model, statistical parameters are used to select the optimal set of descriptors, enabling a reliable relationship to be established from the available data. These parameters are determined from the following expressions [40]:
The sum of square errors (SSE):
(15)
The root mean square error (RMSE):
(16)
The values of these parameters are listed in Table 4.
Table 4. R2 values and statistical parameters
     
Analysis of Table 4 shows that the set of parameters (ΔN, ω, χ) has the lowest statistical parameter values. These data indicate that this parameter set can be used to correlate the experimental and theoretical inhibition efficiencies of 2-4-CBTM1HBI; M-41HBI-2MTMB; 2-BTM1HBI as well as analogous molecules.

4. Conclusions

In this work, the inhibition activities of three benzimidazole derivatives were evaluated using theoretical and experimental simulations. The key points summarizing this work are as follows:
v The three molecules studied display good reactivity in inhibiting aluminum corrosion in 1M nitric acid media, with the following order of inhibition performance: IE (%) (M-41HBI-2MTMB) >IE (%) (2-4-CBTM1HBI) > IE (%) (2-BTM1HBI).
v Theoretical data calculated in base 6-311++G (d, p) such that EHOMO and ΔE follow the following order: EHMO(2-BTM1HBI) > EHOMO(2-4-CBTM1HBI) > EHOMO(M-41HBI-2MTMB); ΔE(M-41HBI-2MTMB) < ΔE(2-4-CBTM1HBI) < ΔE(2-BTM1HBI) this confirms the compound M-41HBI-2MTMB has the better inhibition activity than the others.
v The set of parameters (ΔN, ω, χ) used to correlate the experimental and theoretical inhibition efficiencies of 2-4-CBTM1HBI; M-41HBI-2MTMB; 2-BTM1HBI as well as analogous molecules. This suggests that the model is capable of accurately reproducing or predicting behavior observed in the laboratory.
v The theoretical approach in agreement with the reported experimental data is a positive indicator of the validity and reliability of QSPR model in this specific context.

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