Molecular Modelling and Dynamic Simulation of Some Corrosion Inhibitors in Acidic Medium

Molecular Modelling and Dynamic Simulation of Some Corrosion Inhibitors in Acidic Medium

Chapter One

Aim and Objectives

This study aims to investigate computationally inhibitory action and interaction of the Amino acids, Imidazole and Triazole derivatives as potential steel corrosion inhibitors.

This aim will be achieved through the following objectives:

Data
Optimization of starting geometries of the
Computation of molecular
Splitting of the dataset into training and test
Building of QSAR
Selection of the best QSAR
Validation of the QSAR
Molecular dynamics simulation

CHAPTER TWO

LITERATUREREVIEW

Corrosion

Corrosion may be defined as a destructive phenomenon, chemical or electrochemical, which attacks any metal or alloy reacting with the surrounding environment and in extreme cases may cause structural failure. Corrosion can be also defined as the deterioration of material by reaction with its environment. The corrosion occurs because of the natural tendency for most metals to return to their natural state; e.g., iron in the presence of moist air will revert to its natural state, iron oxide. Metals can be corroded by the direct reaction of the metal with a chemical; e.g., zinc will react with dilute sulfuric acid, and magnesium will react with alcohols(Nimmo and Hinds, 2003).

Importance of Corrosion Studies

The importance of corrosion studies is two folds. The first is economic, including the reduction of material losses resulting from the wasting away or sudden failure of piping, tanks, metal components of machines, ships, hulls, marine, structures…etc. The second is conservation, applied primarily to metal resources, the world‘s supply of which is limited, and the wastage of which includes corresponding losses of energy and water resources accompanying the production and fabrication of metal structures(Clark and Varney, 1962; Moore, 2013).

Conditions necessary for corrosion

For the purpose of this thesis, electrochemical corrosion is the most important classification of corrosion. Four conditions must exist before electrochemical corrosion can proceed:

There must be something that corrodes (the metalanode).
There must be continuous conductive liquid path (electrolyte, usually condensate and salt or othercontaminations).
There must be a conductor to carry the flow of electrons from the anode to the

This conductor is usually in the form of metal-to- metal contact such as in bolted or riveted joints. The elimination of any one of the four conditions will stop corrosion(Jones, 1996).

All metallic materials consist of atoms having valence electrons which can be donated or shared. In a corrosive environment the components of the metallic material get ionized and the movement of the electrons sets up a galvanic or electrochemical cell which causes oxidation, reduction, dissolution or simple diffusion of elements. The metallurgical approach of corrosion of metals is in terms of the nature of the alloying characteristics, the phases existing and their inter-diffusion under different environmental conditions. In fact, the process of corrosion is a complex phenomenon and it is difficult to predict the exclusive effect or the individual role involved by any one of the above mentioned Processes. Based on the above processes, corrosion can be classified in many ways as low temperature and high temperature corrosion, direct oxidation and electrochemical corrosion, etc. The preferred classification is: Dry or chemical corrosion and wet or electrochemical corrosion(Moniz et al., 1986). The other type are:

Chemical corrosion: In which the metal is converted into its oxide when the metal is exposed to a reactive gas or non-conducting
Electrochemical corrosion: The formation of hydrous oxide film occurs when the metal isimmersed in a conducting liquid containing dissolved reactive substance. The reaction is considered to take place at the metal solution interface, due to the heterogeneity on the metal surface, which creates local anodic and cathodic sites on the

CHAPTER THREE

MATERIALS AND METHODS

Materials

This work was carried out in-silico, the hardware and software that was used include: Dell Intel(R)Core(TM)i7-5500U CPU), 16.00GB RAM @ 2.400GHz 2.400GHz processor on Windows 8.1 Pro 64-bit Operating system, ×64-based processor, Spartan 14v.1.1.0 software, Material studios 8.0, ChemDraw ultra 12.0 and Microsoft office Excel 2013.

Data collection

Twenty- five amino acids/analogues derivatives, twenty imidazole and thirty-one triazole derivatives as potential steel corrosion inhibitors were collected from the literature (Babić-Samardžija et al., 2005; Hluchan et al., 1988; Khaled and Hackerman, 2003), (Aljourani et al., 2009; Khaled, 2003; Popova et al., 2003; Tang et al., 2013) and (Bentiss et al., 1999; Deng et al., 2012; González-Olvera et al., 2013; Gurudatt et al., 2015; Ma et al., 2017; Ouici et al., 2015; Quraishi and Sardar, 2002; Quraishi et al., 2012; Wang et al., 2004) respectively which was used for this present study. The chemical structure of each compound in the data sets was drawn with ChemDraw ultra V12.0, named and saved as cdx file. Their molecular structures and experimental inhibition efficiencies were shown in Table 3.1, 3.2 and 3.3 respectively.

CHAPTER FOUR

RESULTS

Quantum Chemical studies, GFA Derived models for %IE and molecular dynamics simulation studies of amino acids/analogous derivatives

To investigate the influence of the structure of the inhibitors (amino acids/analogous derivatives) on their inhibition efficiencies, quantum chemical parameters related to the structure were calculated at the B3LYP/6-311+G(d,p) level of theory.The results of quantum chemical parameters were listed in Table 4.1.The optimized molecular structures, HOMO and LUMO electronic density distributions of the first three inhibitors are shown in Table 4.2.Three QSAR models were developed and presented in scheme 4.1 asmodel (4.1, 4.2, and

4.3 respectively) on the basis of the training set out of which model 4.1 was chosen as the best model for predicting the %IE of the studied corrosion inhibitors based on statistical significance. Figure 4.1 shows a plot of the comparison of the experimental and predicted %IE inhibition efficiency (%IE) for both training and test sets inhibitors.

The external validation of the test set inhibitors showing the experimental and predicted %IEs with residuals for model 4.1are presented in the Tables 4.3. The stability, reliability, and robustness of the generated model 4.1 were confirmed by predictive R² (Table 4.4). The experimental predicted and residual %IE values for the training set of the developed model 4.1 are presented in Tables 4.5, while the applicability domain for Model 4.1 is presented in Figure 4.2.

CHAPTER FIVE

DISCUSSION OF RESULTS

Quantum Chemical studies, GFA-QSAR Derived models for %IEand molecular dynamics simulation studies of amino acids derivatives (Steel corrosioninhibitors)

Quantum chemical calculations were performed on the AM- inhibitors in order to relate their inhibition potentials to molecular structures. According to the frontier orbital approximation, donor-acceptor interactions do occur between frontiers molecular orbitals (HOMO and LUMO) of reacting species(Bereket et al., 2003).The adsorption process of a corrosion inhibitor molecule onto a metal surface increases with increase of the HOMO energy (E-HOMO) and a decrease of the LUMO energy (E-LUMO). This is because, from the HOMO orbital the inhibitor molecule will donate the electrons to the d-orbital of the metal molecule, and the LUMO orbital of the inhibitor will receive the electrons from the d-orbital of the metal molecule, in-electron-donation and electron-back-donation process. Thus, E-HOMO is often associated with the electron donating ability of a molecule; high value of E-HOMO indicates the tendency of the inhibitor to donate electrons to the acceptor metal.

CHAPTER SIX

SUMMARY, CONCLUSION AND RECOMMENDATIONS

Summary of the Findings

Quantum chemical Studies, quantitative structure−activity relationship (QSAR) analysis and molecular dynamics simulation was used to evaluate the structural, electronic and reactivity parameters of Amino acids, Imidazole and Triazole derivatives in relat ion to their effectiveness as corrosion inhibitors. Quantum chemical parameters such as energy of the highest occupied molecular orbital (E-HOMO), the energy of the lowest unoccupied molecular orbital (E-LUMO), energy band gap ΔE, Dipole Moment, electrophilicity index (��), chemical softness (σ), chemical hardness (η) and fraction of electron transfer from the inhibitors molecule to the metallic surface (ΔN) were obtained. QSAR model between the inhibition efficiencies and structural properties was built by the Generic function algorithm. The predictive ability of the models was judged from the prediction of the %IE of the test set inhibitors.

A comparison of statistical quality of different models indicates that model 4.1, 4.4 and 4.7 are the most significant statistically. The best linear models (model 4.1, 4.4 and 4.7 based on external validation parameter R²pred) showed good internal and external prediction power. Furthermore, molecular dynamics simulations were performed to study the adsorption behavior of the studied inhibitors on the Fe (1 1 0) surface and it was observed that the adsorption occurs mostly through the lone pair of electrons of the hetero- atoms (mainly N-atom for both AM, IM and TR- inhibitors) and p-electrons of the azole moiety. All values of adsorption energies, (E_ads), are negative, which means that the adsorption could occur spontaneously.

Conclusion

A Molecular modelling approach was performed to study the corrosion inhibition performance of amino acids, imidazole and triazole derivatives on steel surface. It was evident from this investigation that theoretical studies can provide an insight into the chemical reactivity of the studied inhibitors. It also offers atomic level investigation of the experimental findings. The followings outcomes can be concluded from this study:

Quantum chemical calculation reveals that electron donation and electron acceptance capability of the studied inhibitors is in good agreement with the results obtained from previously performed experimental
The prediction of corrosion efficiencies of the studied inhibitors by the built QSAR models gave a very good correlation coefficients for all the three series.The robustness and applicability of the QSAR models has been established by internal and external validation
The interaction energies (E_adsand E_bind) between the studied inhibitors and Fe (1 1 0) plane via MD-simulation were large indicating chemical bond formation and that the inhibitors can more tightly adsorb on iron surface. The order of an adsorption energy and binding energy values are in good agreement with the experimental findings. Furthermore, MD simulation reveals that the all the shortest bond distances between the heteroatoms of the inhibitors and Fe atoms are lying within a range of 3.5Å, which indicate the formation of a chemical bond between the inhibitors and Fe atoms.

Recommendations

In future design of novel corrosion inhibitors against steel, the following recommendations are made:

From the results obtained in this study, it is good to carry out more computational calculations on other organic inhibitors that are less harmful to the environment to evaluate their efficiencies based on their molecular
The quantum chemical calculations, QSAR analysis and MD simulations techniques used in this study should be extended to corrosion inhibition studies involving other inhibitors and metals / alloys. This is because the method is simple and effective in determining corrosion inhibition and
Further work is needed to design and synthesize more efficient inhibitor against steel corrosion that will be more acceptable to the environment than the existing

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