ABSTRACT
The corrosion inhibition performance of amino acids, imidazole and triazole derivatives in an aqueous acidic medium on steel surfaces have been investigated by the quantum chemical calculation, QSAR analysis, and molecular dynamics (MD) simulation methods. Density functional theory (B3LYP/6-31G*) quantum chemical calculation method was used to find the optimized geometry of the studied 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 (𝜔), chemical softness (σ), chemical hardness (η) and fraction of electron transfer from the inhibitors molecule to the metallic surface (ΔN) have been calculated and well discussed. Additionally, a linear quantitative structure-activity relationship (QSAR) model was built by Genetic Function Approximation (GFA) method to run the regression analysis and establish a correlation between the computed descriptors and the experimental corrosion inhibition efficiencies which were used to predict the corrosion inhibition efficiencies of the studied inhibitors. The prediction of corrosion efficiencies of these inhibitors nicely matched the experimental measurements. The correlation parameters obtained for the best model in each of the three series are the squared correlation coefficients (R2) of 0.871, 0.942 and 0.862, adjusted squared correlation coefficients (R2adj) value of 0.818, 0.908 and 0.822, Leave one out (LOO) cross-validation coefficients (Q2) value of 0.750, 0.795 and 0.706, the external validations (R2pred) of 0.857, 0.983 and 0.835. These indicate that the generated models were excellent for verifying with internal and external validation parameters. Furthermore, molecular dynamics simulation is applied to search the best adorable adsorption configuration of inhibitor over Fe (1 1 0) surface. It is further confirmed by the MD simulations that adsorption of the inhibitor molecules on the metallic surfaces mainly occurred by chemical adsorption phenomenon. Thus, Quantum chemical studies, QSAR analysis along with MD simulation may be very powerful tool for the rational designing of several promising corrosion inhibitors and in prediction of their inhibition efficiencies.
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TABLE OF CONTENTS
Cover page…………………………………………………………………………………………………………………… i Fly leaf……………………………………………………………………………………………. ii
Title Page………………………………………………………………………………………………………………….……iiError! Bookmark not defined.
DECLARATION ……………………………………………………………………………………………………….. iv
CERTIFICATION……………………………………………………………………………………………………….. v
DEDICATION …………………………………………………………………………………………………………… vi
ACKNOWLEDGEMENT ………………………………………………………………………………………….. vii Table of Contents……………………………………………………………………….viii List of Tables……………………………………………………………………………xii List of Figures……………………………………………………………………………xiii List of Schemes…………………………………………………………………………………..xv List of Abbreviations……………………………………………………………………xvii
Abstract …………………………………………………………………………………………………………………. xviii
CHAPTER ONE………………………………………………………………………………………………………… 1
1.0 INTRODUCTION ……………………………………………………………………………………………….. 1 1.1 Background to the study……………………………………….…………………….1
1.2 Research problem…………………………………………………………………………………………………. 4
1.3 Justification to the research ………………………………………………………………………………….. 5
1.4 Research Questions ……………………………………………………………………………………………… 6
1.5 Theoretical Frame Work ………………………………………………………………………………………. 6
1.6 Aim and Objectives ………………………………………………………………………………………………. 8
1.7 The Significance of the Study ………………………………………………………………………………… 8
CHAPTER TWO……………………………………………………………………………………………………….. 9
2.0 LITERATURE REVIEW …………………………………………………………………………………….. 9
2.1 Corrosion ………………………………………………………………………………………………………………. 9
2.1.1 Importance of Corrosion Studies……………………………………………………………………………. 9
2.1.2 Conditions necessary for corrosion ………………………………………………………………………. 10
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2.1.3 Classification of Corrosion………………………………………………………………………………….. 10
2.1.4Factors Influence Corrosion …………………………………………………………………………………. 11
2.1.5 Electrochemical theory of corrosion …………………………………………………………………….. 12
2.2 Types of Corrosion …………………………………………………………………………………………….. 14
2.2.1 General or uniform corrosion ………………………………………………………………………………. 14
2.2.2 Pitting corrosion ………………………………………………………………………………………………… 14
2.2.3 Stress corrosion cracking ……………………………………………………………………………………. 14
2.2.4 Intergranular corrosion ……………………………………………………………………………………….. 15
2.2.5 Corrosion fatigue ……………………………………………………………………………………………….. 15
2.2.6 Filiform corrosion ……………………………………………………………………………………………… 15
2.2.7 Crevice corrosion ………………………………………………………………………………………………. 15
2.2.8 Galvanic or bi-metallic corrosion…………………………………………………………………………. 16
2.2.9 Fretting corrosion ………………………………………………………………………………………………. 16
2.2.10 Erosion corrosion …………………………………………………………………………………………….. 16
2.2.11 Selective leaching or demetalification ………………………………………………………………… 16
2.3 Cost of Corrosion ……………………………………………………………………………………………….. 16
2.4 Methods of Corrosion Protection…………………………………………………………………………. 17
2.4.1 Application of protective coatings ……………………………………………………………………….. 17
2.4.2 Polarize or shift the potential of the metal …………………………………………………………….. 17
2.4.3 Cathodic protection ……………………………………………………………………………………………. 18
2.4.4 Materials selection……………………………………………………………………………………………… 18
2.4.5 Corrosion inhibitors……………………………………………………………………………………………. 19
2.6 Literature Review on Inhibition of steel corrosion in Acidic Medium …………………… 21
2.7 Theoretical studies of Corrosion Inhibitors………………………………………………………….. 25
2.8 Quantum Chemical studies …………………………………………………………………………………. 27
2.8.1 Quantum Chemical studies on corrosion inhibitors ………………………………………………… 27
2.9 Quantitative Structure Activity Relationship Study (QSAR) ………………………………… 29
2.9.1. QSAR on corrosion inhibitors…………………………………………………………………………….. 30
2.10 Molecular dynamic simulation…………………………………………………………………………… 31
2.10.1 Molecular dynamic simulation on corrosion inhibitors …………………………………………. 31
CHAPTER THREE …………………………………………………………………………………………………. 32
3.0 MATERIALS AND METHODS………………………………………………………………………….. 32
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3.1 Materials ……………………………………………………………………………………………………………. 32
3.1.1 Data collection…………………………………………………………………………………………………… 33
3.2 Methods ……………………………………………………………………………………………………………… 42
2.2.1 Geometry Optimization ………………………………………………………………………………………. 43
3.2.2 Quantum chemical calculation …………………………………………………………………………….. 46
3.3 QSAR Methods …………………………………………………………………………………………………… 48
3.3.2 Training and test sets ………………………………………………………………………………………….. 53
3.3.3 QSAR Model Building……………………………………………………………………………………….. 57
3.3.4 Procedure involved in building GFA models…………………………………………………………. 58
3.3.5 Evaluation of the QSAR model……………………………………………………………………………. 61
3.3.6 Applicability Domain …………………………………………………………………………………………. 64
3.4.1 Building Steel Crystal ………………………………………………………………………………………… 68
3.4.2 Solution Construction…………………………………………………………………………………………. 72
3.4.3 Importing inhibitor and Molecular optimization…………………………………………………….. 76
3.4.5 Molecular dynamic simulation…………………………………………………………………………….. 81
CHAPTER FOUR ……………………………………………………………………………………………………. 84 4.0 RESULTS……………………………………………………………………………82
4.1 Quantum Chemical studies, GFA Derived models for %IEand molecular dynamics simulation studies of amino acids/analogous derivatives……………………….. 84
4.2 Quantum Chemical studies, GFA Derived models for %IEand molecular dynamics simulation studies of Imidazole derivatives ………………………………………….. 96
4.3 Quantum Chemical studies, GFA Derived models for %IEand molecular dynamics simulation studies of Triazole derivatives …………………………………………….. 96
CHAPTER FIVE……………………………………………………………………………………………………. 120
5.0 DISCUSSION OF RESULTS…………………………………………………………………………….. 120
5.1 Quantum Chemical studies, GFA-QSAR Derived models for %IEand molecular dynamics simulation studies of amino acids derivatives……………………………………… 120
5.1.2 Quantitative structure–activity relationship (QSAR) Studies for Amino Acids Derivatives………………………………………………………………………………………………………… 125
5.1.3 Molecular Dynamic Simulation Studies for Amino Acids Derivatives ……………………. 127
5.2 Quantum Chemical studies, GFA Derived models for %IEand molecular dynamics simulation studies of Imidazole derivatives ………………………………………… 129
5.2.2 Quantitative structure–activity relationship (QSAR) Studies for Imidazole Derivatives………………………………………………………………………………………………………… 133
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5.2.3 Molecular Dynamic Simulation Studies for Imidazole Derivatives ………………………… 134
5.3 Quantum Chemical studies, GFA Derived models for %IEand molecular dynamics simulation studies of Triazole derivatives …………………………………………… 135
5.3.2 Quantitative structure–activity relationship (QSAR) Studies for Triazole Derivatives………………………………………………………………………………………………………… 138
5.3.3 Molecular Dynamic Simulation Studies for Imidazole Derivatives ………………………… 139
5.4 Comparison of the Effectiveness of AM, IM and TR in Inhibiting Corrosion of Steel………………………………………………………………………………..140 CHAPTER SIX…………………………………………………………………………………..………….141
6.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS …………………………….. 143
6.1 Summary of the Findings ………………………………………………………………………………….. 143
6.2 Conclusion………………………………………………………………………………………………………… 144
References …..…………………………………………………………………………146
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background to the study
Corrosion is an undesirable process that affects several areas of industrial activity, especially the oil industry, resulting in huge economic losses (Szklarska-Smialowska and ZS-Smialowska, 2005). It is a common problem for steel and directly impacts its cost and safety. The corrosion of iron can cause structural damage and lead to changes in the mechanical and chemical properties of plants, vessels, pipes, and other processing equipment. Several countries have attempted to relate the cost of corrosion to their gross national product. The annual cost of corrosion worldwide was estimated at $ 2.2 Trillion in 2010, which was about 3 % of the world‘s gross domestic product of $ 73.33 Trillion (Al Hashem, 2011; Obot, 2014). Preventing the corrosion of steel has played an important role in various industries, especially in the chemical and petrochemical processing industries that employ the use of steel (Singh et al., 2016). Although it is not possible to completely avoid the corrosion process there are several ways to prevent it or slow down the corrosion rate (Nwankwo et al., 2016). Several organic compounds especially those that contain N, O, S, and P heteroatoms, as well as π-electron systems have been previously used as corrosion inhibitors for metals in aqueous solutions (Murulana et al., 2012; Zhao et al., 2014). Although many heterocyclic compounds have been successfully used as corrosion inhibitors in several metallic systems, most of them were toxic and non-biodegradable (Eddy and Mamza, 2009).
With the current advancement of environmental safety, researchers were focused on the environmentally friendly corrosion inhibitors. Amino acids which were non-toxic,
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easily available and completely soluble in aqueous media are considered to be the most promising green inhibitors (Lyon, 2004). So also, Azoles and particularly their derivatives are known as efficient corrosion inhibitors (Kuznetsov and Kazansky, 2008). For example, some derivatives of benzimidazole have been demonstrated as excellent inhibitors for metals and alloys in acidic solution, and exhibit different inhibition performance with the difference in substituent groups and substituent positions on the imidazole ring (Ahamad and Quraishi, 2009; Tang et al., 2013; Zhang et al., 2012). In recent years, N- and S-containing triazole derivatives have attracted more attention for their excellent corrosion inhibition performance (Wang et al., 2004). Not only can some N- and S-containing triazole compounds give very high values of inhibition efficiency, but they can bring down the hydrogen permeation current to a considerable extent (Muralidharan et al., 1995). In contrast to most commercial acid corrosion inhibitors which are highly toxic, many N- and S-containing triazole derivatives are environmentally friendly corrosion inhibitors (Bentiss et al., 1999). In the past two decades, the research in the field of ‗‗green‘‘ corrosion inhibitors has been addressed toward the goal of using cheap, effective molecules at low or ‗‗zero‘‘ environmental Impact (Obot and Obi-Egbedi, 2010). The pie chart in Figure 1.1 shows world consumption of corrosion inhibitors on a value basis as at 2016 (Anonymous, 2017).
Several experimental techniques have been used to study the corrosion and corrosion inhibition in acid solution such as weight-loss method, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and so on (Amin and Ibrahim, 2011; Zhang et al., 2008). However, experiments are usually time-consuming, expensive, and deficient in elucidating the inhibition mechanism of the system at the Sub-atomic and molecular levels (Wazzan et al., 2016). Therefore, quantum chemical calculation and
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molecular dynamics simulation were recommended as potent and fast tools to assist in the interpretations of experimental findings and to overcome such deficiencies (Atalay et al., 2006; Ebenso et al., 2010). Quantum chemical methods have already proven to be very useful in determining the molecular structure as well as elucidating the electronic and reactivity centers of a compound (Gece, 2008). Recently, theoretical prediction of the efficiency of corrosion inhibitors has become very popular in parallel with the progress in computational hardware and the development of efficient algorithms which assisted the routine development of molecular quantum mechanical calculations (Saha and Banerjee, 2015).
Figure 1.1: World Consumption of Corrosion Inhibitors
Quantitative structure-activity relationships (QSAR) has been a subject of intense interest in the field of medicinal chemistry in determining the molecular structure as well as
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elucidating the electronic structure and reactivity (Kraka and Cremer, 2000), but to a less extent in the field of corrosion (Bentiss et al., 2003; El Sayed et al., 2012). However, in recent years, quantitative structure-activity relationship (QSAR) has aroused many researchers‘ interest in the studies of corrosion inhibitors (Zhao et al., 2014). The success of the QSAR approach can be explained by the insights offered for the structural determination of chemical properties, and the possibility of estimating the properties of the new chemical compounds without any need for them to be synthesized and tested (Asadollahi et al., 2011). However, the success of any QSAR model depends on the accuracy of input data, selection of the appropriate descriptors, statistical tools, and most important validation of the developed models (Tong et al., 2005; Tropsha et al., 2003) It has been proved to be very helpful for predicting the inhibition efficiencies of novel corrosion inhibitors(Zhao et al., 2014).
Moreover, with the advances in computational chemistry and the development of new algorithms, theoretical methods have been used to investigate corrosion inhibition mechanism and to design new and environment-friendly corrosion inhibitors. Molecular dynamics (MD) simulation was recently considered as a modern tool to study the adsorption behavior of the inhibitor molecules on the metallic surfaces of interest (Wazzan et al., 2016). In recent years, most of the theoretical Molecular dynamics (MD) simulation calculations have not yielded desirable results because they have been performed on an isolated molecule in vacuum without considering corrosion medium(Khaled, 2008). Since the interactions of the inhibitor-metallic surface in a corrosion medium were not taken into account, these calculations do not provide full pictures of the inhibition mechanism.
1.2 Research problem
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Organic compounds have been reported to be effective metals corrosion inhibitors in various media. The adsorption layer formed between the compounds, acting as an inhibitor, and the metal surface is one of the main factors that control the inhibition efficiency. Other factors responsible for the formation of a strong adsorption layer on the metal surface are inhibitor‘s molecular structure and size, number and type of substituents on the inhibitor molecules and the type of adsorption (whether it is a physisorption or chemisorption) (Palomar-Pardavé et al., 2012; Sherif, 2006).
Several experiments were extensively carried out to determine the influence of these factors. However, experiments are usually time-consuming, expensive, and deficient in elucidating the inhibition mechanism of the system at the sub-atomic and 3D-molecular levels (Wazzan et al., 2016). Therefore, quantum chemical calculation, molecular modeling and dynamic simulation were recommended as potent and fast tools to assist in the interpretations of experimental findings and to relate it with the molecular properties of the inhibitor.
1.3 Justification to the research
Experimental approaches used for corrosion study were often costly and time-consuming since large-scale trial experiments are required. Therefore, computational techniques which can overcome these shortcomings are required.This research will contribute to computational prediction and identification of highly efficient and environmentally friendly compounds for corrosion inhibition of steel.
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1.4 Research Questions
i. What are the dominant structural features responsible for the inhibition efficiency of the studied inhibitors against corrosion of steel in acidic media?
ii. Which of the derivatives of the studied inhibitors have the highest binding energy based on molecular dynamic simulation?
iii. What is the dominant substituent influencing the high binding energy of the studied inhibitors?
1.5 Theoretical Frame Work
Quantitative structure-activity relationships (QSAR) has been a subject of intense interest in the field of medicinal chemistry in determining the molecular structure as well as elucidating the electronic structure and reactivity (Kraka and Cremer, 2000), but to a less extent in the field of corrosion (Bentiss et al., 2003; El Sayed et al., 2012). The concept of assessing the efficiency of a corrosion inhibitor with the help of computational chemistry is to search for compounds with desired properties using chemical intuition, experience and a mathematically quantified and computerized form. Once a correlation between the structure and activity or property is found, any number of compounds, including those not yet synthesized, can be readily predicted employing computational methodology (El Sayed et al., 2012) via a set of mathematical equations which are capable of representing accurately the chemical phenomenon under study (Asadollahi et al., 2011). However, the success of any QSAR model depends on the accuracy of input data, selection of the appropriate descriptors, statistical tools, and most importantly validation of the developed model (Gece,
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2008). A major step in constructing the QSAR models is to find a set of molecular descriptors that represent variation in the structural properties of the molecules.
With the improvement of sophisticated software and hardware related computational supportive systems, computer-aided simulation has been explored as an easy and powerful tool from where we can investigate the complex system in corrosion process and may successfully predict their relative inhibition efficiency well in advance. In this occasion, proper theoretical modeling and corresponding quantum chemical calculation is very efficient for exploring the relationship between the molecular properties of the inhibitors and its corrosion inhibition efficiencies(Gece and Bilgiç, 2010; Sun et al., 2012). Corrosion inhibition capability of the molecules can be determined by the frontier molecular orbital energies, energy gap, dipole moment, global hardness, softness, a fraction of electron transfer from the inhibitors molecule to the metallic surface etc. Kokalj recently proposed that only quantum chemical approach alone is not sufficient enough to envisage the inhibition efficiency trend of the inhibitors molecules(Kokalj, 2010). In many cases, results obtained from DFT cannot be correlated well with obtained experimental findings (Ebenso et al., 2010; Quraishi, 2013). In such circumstances, a precise modeling of experimentation should be emphasized to correlate the theoretical results with the experimental inhibition effectiveness.
In real practice, modeling of an experiment can only provide the actual interfacial interactions between the concerned metallic surface and inhibitor molecules. As a result, recently molecular dynamics (MD) simulation has emerged as a modern tool from where we can reasonably predict actual interfacial configuration and adsorption energies of the surface adsorbed inhibitor molecules. Till date, only a few certain groups are working on it to get the interaction, as well as the binding energy of surface, adsorbed inhibitor
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molecules. Obot and Gasem recently employed MD simulation to study the adsorption behavior of pyrazine derivatives on the steel surface(Obot and Gasem, 2014). Xia et al(2008) explored the correlation between the structural conformation of the imidazoline derivatives and their corresponding inhibition efficiencies by employing MD.
1.6 Aim and Objectives
The aim of this study is 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:
i. Data collection.
ii. Optimization of starting geometries of the molecules.
iii. Computation of molecular descriptors.
iv. Splitting of the dataset into training and test set.
v. Building of QSAR models.
vi. Selection of the best QSAR model.
vii. Validation of the QSAR models.
viii. Molecular dynamics simulation
1.7The Significance of the Study
This study is based on the premise that corrosion inhibition and protection of metals/alloys play very important role in the chemical, manufacturing, construction, food,
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and beverages, oil and gas industry of the economy. The study therefore provides the basis for the evaluation of amino acids, imidazole and triazoles derivatives as suitable alternatives to highly expensive and non-environmentally friendly corrosion inhibitors of steel.
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