Clindamycin an antibacterial drug was assessed for its potential as a corrosion inhibitor for Al, Zn, Cu and Fe metals, the optimized structures were used for calculating their quantum chemical properties and the values of Log P (0.79) and free Gibbs energy (-0.00258) of the inhibitor confirms the spontaneous nature of clindamycin, other QSPR parameters such as hardness, softness, EA, IP and total energy (E) were also calculated and correlated with the binding energy of clindamycin with their respective surfaces. The inhibitor was used for a molecular dynamic simulation study using Material Studio 7 software and the binding energies of clindamycin on the selected metal surfaces were determined via quenching process and Monte Carlo algorithm. The energy of the metals was found to be significantly correlated with the binding energy and hence it was used in the development of a QSPR model (Binding Energy = - 70.833*E (kJ/mol)-106.348) via GFA method for predicting the binding energy of clindamycin with any metal surface, the model was found to be significant and the R2 and CVR2 were found to be 0.976 and 0.765 respectively.

Keywords: Clindamycin; Binding energy; Corrosion inhibitor; QSPR; Adsorption; Drug.

Corrosion in various media is a significant problem that normally causes things made of compressed composition of metals to flop in the factory and industrial spaces, structures such as steel rebar corrosion in solid constructions. Corrosion resistance is also a property of a metal that gives it the ability to withstand attacks from chemical, or electrochemical conditions, but these metals no matter how tough can only withstand corrosion for a while.

 Over the past decade, several corrosion experimental methods have been industrialized for the analysis of corrosion difficulties, for the forecast of care requirement, and for the identification of the most effective inhibitor and optimum inhibitor dosages [1]. Organic compounds have been described in the books as some of the most effective corrosion inhibitors, due to the fact that most of them have lone pair electrons found in heteroatoms present as a motif of their structure (P, S, N, and O) [2,3].However, the use of most of these substances has been hidered, due to a number of uncomfortable side effects they trigger inside the environment [4]. Thus, this development with the novel corrosion inhibitors regarding its low toxicity has been regarded as a much more important casing point [5,6].

Because they are basically synthesized from natural occurring phytochemicals [7,8], as well as their environmentally friendly properties [9] and also minimal adverse influences around the aquatic surroundings [10], drugs (chemical medicines) seem to be suitable candidates to replace standard poisonous corrosion inhibitors.

On expenses of aforementioned structural differences, corrosion inhibition potentials of most studied drugs have attracted a good number of interest in recent years. This paper provides a theoretical insight into the use of antibacterial drug Clindamycin as a potential corrosion inhibitor, recent developments that have been reported in the literature shows that it is a fairly good inhibitor for corrosion of Zinc in a minimum concentration of sulphuric acid [8]. The main aim is to identify the binding affinity of this drug to a group of metals and compare for them for its optimum use as a corrosion inhibitor.

Corrosion scientists are attempting to integrate environmental considerations directly into corrosion inhibitor selection processes, in order to respond to an increased awareness of the need to protect the environment [11].  Some of the drugs reported to have shown an impressive potential as corrosion inhibitors includes Penicillin G [12], penicillin V[13], Ampicillin [11], cloxacillin [5], cefixime [14] , Norfloxacin [15], quinolone, quinaldine and quinaldic acid [1] . Intense research by several groups has produced some models, but none of these have enough experimental backup to be identified as the true inhibition mechanism [16,17]. Further research, both theoretical and experimental, remains vital.

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Computational Detail

The present calculations were achieved by means of Spartan 14 V 1.1.4 (Wavefunction program package [18]. 

Figure 1: The molecular structures of the investigated inhibitor (Clindamycin)

Geometry optimization of clindamycin was carried-out via Density Functional Theory by employing Becke’s three parameter ex-change functional (B3LYP) [19,20,21], and embraces DFT electron exchange functionals coupled with the gradient-corrected correlation functional of Lee, Yang, and Parr (LYP) [21,22] and the 6-311 ++ G (d,p) basis set. In the geometry optimizations progression, every part of the chemical structure were unfettered of strain energy by first exposing the structure to molecular mechanics (MM) calculation. The structures acceptable from constrains were then optimized in their aqueous forms, to give the optimized geometries of clindamycin and the metals. Frontier molecular orbitals such as the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) was used employed in correctly predicting the adsorption points of clindamycin on the metal surface. In lieu of the unassertive electrons assignment, the adsorption of clindamycin should take place where the softness property, which is a local parameter, takes the maximum value. As stated by Koopman’s theorem, the energies of the HOMO and the LUMO orbitals of the inhibitor molecule are associated with the direction of the ionization energy and the electron affinity in that order (equation 1&2), through the resulting relationships:

I=-E_Homo     --------------  (1)

A=-E_Lomo---------------(2)

Absolute electronegativity (χ), and absolute hardness (η), of clindamycin, were calculated in equation 3&4

χ=(I+A)/2    -----------------(3)

η =(I – A)/2    -----------------  (4)

The softness quantity is the transposed value for the hardness (equation 5)

σ =1/η     ----------------     (5)

Electronegativity, softness, and hardness are important quantities in theorizing the reactivity of chemicals. When clindamycin is brought to the metal surface it is expected that electrons will flow from lesser value χ (clindamycin) to higher χ (Metal), until the chemical potentials become equal. Hence the total number of transferred electrons (ΔN) was calculated using equation 6

∆N =(χ_Metal- χ_inh)/2(η_Metal+ η_inh )      ----------    (6)

Where η_Metal  and η_inh  in equation (6) represents the total electronegativity of iron and clindamycin respectively, while χ_Metal and χ_inh represent the absolute hardness of the metal and clindamycin respectively [22].

The change in electronegativity initiates the electron transfer, and the addition of the hardness parameter acts as a struggle or opposition to the electron flow. The total electrophilicity index was developed by Parr [13,17,18] and is given as

ω =μ^2/2η      ----------   (7)

According to equation 7, this index parameter measures the tendency of chemical species to accept electrons. A good responsive nucleophile is considered by the insignificant value of χ and ω (equation 7), while for an effective electrophile a significant value of ω and χ is noticed.

This paper aims to extend these inquiries in order to confer the link between quantum chemical calculations and Binding energy of clindamycin on various considered metals (Fe, Al, ZnO, and Cu) by defining the quantum chemical descriptors responsible for this inhibition efficacy of clindamycin, this parameters includes the energies of the lowest unoccupied molecular orbital (ELUMO) and highest occupied molecular orbital (EHOMO), the energy difference (DE) between EHOMO and ELUMO, dipole moment (l), softness (σ), electron affinity (A), global hardness (η), ionization potential (I), the portion of electrons transferred (ΔN), electronegativity (v), and the total energy (TE)

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ang et al. [23], specified that the frontier orbital (highest occupied molecular orbital-HOMO and lowest unoccupied molecular orbital-LUMO) of a chemical species play a significant part in defining its reactivity.

Figure 2: Electronic properties of Clindamycin: (a) optimized structure, (b) HOMO orbital, (c) LUMO orbital and (d) total electron density (atom legend: light gray H, dark gray C, red O, purple Cl, yellow S and light green N). The isosurfaces (larger lobes) depict the electron density difference; the darker regions show electron accumulation, whereas the lighter regions show electron loss

As EHOMO is frequently linked with the electron giving ability of a molecule, high value of EHOMO are expected to show the tendency of the molecule to donate electrons to appropriate acceptor molecules with lower energy molecular orbital [19,24]. Increasing values of EHOMO facilitate adsorption and therefore enhance the inhibition efficiency, by influencing the transport process through the adsorbed layer. Table 1 shows the quantitative structural properties related parameter computed using a DFT method, the area of molecule was given as 424.26 Armstrong, while the free Gibbs energy is given as -0.00258 au. The result is evident on the reported experiment analysis on the spontaneity and surface area of the molecule for efficient adsorption of the molecule on Zn surface and truly in this context metal surface.

 

Table 1: Quantitative Structural Property Related parameters for Clindamycin calculated using

B3LYP/6-311 ++ G(d,p).

 

The value of hydrophobicity (Log P) which defines the lipophilicity or hydrophobicity of a compound is high (0.79) signifying the ability of the drug to protect the surface of the metal from an attack of corrosive agents directly on the metal surface. Other quantum chemical parameters like ELUMO presented in Table 2, indicates the ability of the molecule to accept electrons, hence the binding ability of the inhibitor to a metal surface, and influenced by increasing of the HOMO and decreasing of the LUMO energy values of Clindamycin. Frontier molecular orbital diagrams of Clindamycin are presented in Figure 2.  According to the frontier molecular orbital theory (FMO) of chemical reactivity, the transition of electron is due to interaction between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of reacting species [25,26].The higher values of EHOMO of clindamycin will donate electrons to orbital of iron (Fe), followed by copper (Cu) and finally aluminium (Al), zinc is found to have a much higher EHOMO and lower ELUMO than that of the inhibitor, thereby retarding the possibility of clindamycin adsorbing on zinc surface and decreasing the overall corrosion inhibition potential of clindamycin in zinc environment, hence the higher the corrosion inhibition efficiency through better adsorption.

 

	Clindamycin	Zinc (Zn)	Copper (Cu)	Iron (Fe)	Aluminium (Al)
E (kJ/mol)	-1104.16	130.42	337.65	786.06	309.91
E HOMO (kJ/mol)	-861.52	-900.54	-806.78	-742.73	-580.11
E LUMO (kJ/mol)	21.21	-156.76	12191.22	279.52	83.63
IP (kJ/mol)	861.52	900.54	806. 78	742.73	580.11
EA (kJ/mol)	-21.21	156.76	-12191.22	-279.52	-83.63
Hardness (kJ/mol)	441.365	371.89	6499	511.125	331.87
Softness (kJ/mol)	0.002265	0.002689	0.000154	0.001956	0.003013
Global electrophilicity index	-7871208.2	-63564008.2	-396759578	-77098303.9	-14071902.1
ΔN		0.057	0.579	-0.145	-0.129
Molecular Wt.(amu)	424.99	65.39	63.546	55.847	26.982

 

Table 2: Calculated quantum chemical properties for the most stable conformations of Clindamycin, Zn, Cu, Fe and Al metal

 

Absolute softness and hardness are significant properties to measure in understanding the molecular stability and reactivity of clindamycin with the surface of the metal. It is important to note that the absolute chemical hardness is fundamentally responsible for the resistance given towards the deformation or polarization of the electron cloud of the atoms, molecules and ions under small trepidation of chemical reaction agrees with the result of the ionization potential (IP) and electron affinity of the species presented in Table 2 (EA). Aluminium is found to have the smallest hardness (331.87 kJ/mol) and highest softness value (0.0030 kJ/mol) making it the least reactive surface with an exception of Zinc whose value deviates from those of the other metals because of its stability corresponding with the completely filled d-orbital it outermost shell. The ∆N values were presented in Table 2, this represents the total number of electronic charges that will be conveyed between the surface and the adsorbed species. The larger value of -0.145 for Fe signifies the maximum transfer of electron and hence greater inhibition efficiency, while the least transferred electron was noticed in Cu (0.579) hence signifying the possibility of finding the least corrosion inhibition property of clindamycin.

 

The correlation analysis of the binding energy of the metal to the inhibitor (clindamycin) presented in Table 3. It showed that their binding energies (Fe, Al, Zn and Cu) inversely correlated with the total energy of the metals studied; the correlation was significant at 99% confidence interval. Pearson correlation test revealed that the decrease in energy of the metals increase the binding energy of the inhibitor to the metal complex.

 

Molecular dynamics simulation

 

In order to model various low energy conformations and find the energy minima, molecular dynamics (MD) virtual reality of the interactions between a single molecule of interest and Fe/Al/Cu/ZnO surface was done by means of Forcite quench molecular dynamics cutting-edge  Material Studio (MS) 7.0 program. Computations on the inhibitor interaction with the metal surface measured was carried out using COMPASS II forcefield and Smart algorithm in a simulation box 17Å x 12 Å x 28 Å with an intermittent limit situation, to model a functional part of the Fe/Al/Cu/ZnO slab and a vacuum layer of 20 Å height. The metal’s model was hewn beside the (110) plane with a fractional distance of 3.0 Å.

 

	Binding Energy (kcal/mol)	E (kJ/mol)	E HOMO	E LUMO	EA	Hardness	Softness	Global electrophilicity index
Binding Energy	1
E (kJ/mol)	-0.992*	1
E HOMO	-0.355	0.278	1
E LUMO	0.027	-0.099	-0.228	1
IP	0.356	-0.277	-1	0.225
EA	-0.027	0.099	0.227	-1	1
Hardness	0.034	-0.105	-0.249	0.999*	-0.999*	1
Softness	0.218	-0.168	0.332	-0.944*	0.944*	-0.947*	1
Global electrophilicity	-0.027	0.100	0.228	-1	1	-0.999*	0.944*	1
ΔN	0.276	-0.327	-0.451	0.955*	-0.954*	0.960*	-0.876*	-0.955*
Molecular Wt.	0.102	-0.038	-0.954	0.389	-0.389	0.408	-0.548	-0.3893

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

* Significant at 99% confidence interval

Table 3: Pearson Correlation Analysis of Quantum chemical Properties and the Binding Energy of Clindamycin with the Considered Metal Surfaces.

 

For cite optimized structures of Clindamycin and the metal faces were utilized to mock-up all the interactions of the molecule with the surfaces [27-29]. Figure 3-6 shows the adsorption of clindamycin on all four metals, emphasizing the epitaxial adsorption pattern of the inhibitor on the several metal surface, a specific nitrogen and also oxygen atoms were obtained in all cases to be the main point of adsorption, the end of the metal surface displays that these atoms having excess lone pair of electron positions this molecule for efficient donation of electrons to the metal surface and hence supporting the result from the calculated quantum chemical parameters such as hardness, softness, IP, EA, EHOMO and ELUMO accordingly.

 

 

Figure 3: Representative snapshots from molecular dynamics models of Clindamycin adsorption on           ZnO (110), highlighting the soft epitaxial adsorption mechanism with accommodation of the molecular backbone in characteristic epitaxial grooves on the metal surface

 

 

Figure 4: Molecular dynamics model of a single Clindamycin molecule adsorbed on Fe (110):  (a) side view; (b) on-top view

 

Figure 5: Molecular dynamics model of a single Clindamycin molecule adsorbed on Cu (110): (a) side view; (b) on-top view

 

Figure 6: Molecular dynamics model of a single Clindamycin molecule adsorbed on Al (110): (a) side view; (b) on-top view

 

Metal-Inhibitor complex	Binding Energy (kcal/mol)
Al-clindamycin	-92.880
Cu-clindamycin	-100.400
Fe-clindamycin	-202.317
ZnO-clindamycin	-29.796

Table 4:   Binding Energy of the metal-inhibitor (Clindamycin) complex

 

The binding energy of the inhibitor on the metals surface was calculated using the relationship in Eq. 8:

 

A negative value of Ebind corresponds to a stable adsorption structure, whereas E_Mol, E_Fe and E_total correspond, respectively to the total energies of the molecule, Fe (110) slab and the adsorbed Mol-Fe (110) couple in the gas phase. The total energies were calculated by averaging the energies of the five most stable representative adsorption configurations and the binding affinity of clindamycin on Fe surface was found to be the best (-202.317Kcal/mol), while that of ZnO was found to have binding affinity (-29.796), suggesting that Fe surface would be most favourable for the use of clindamycin as a corrosion inhibitor.

 

STATISTICAL PARAMETER
Friedman LOF	404.540181
R-squared	0.984614
Adjusted R-squared	0.976921
Cross validated R-squared	0.765784
Significant Regression	Yes
Significance-of-regression F-value	127.990485
Critical SOR F-value (95%)	17.416641
Replicate points	0
Computed experimental error	0
Lack-of-fit points	2
Min expt. error for non-significant LOF (95%)	5.39568

Table 5: Statistical Parameters of the GFA model for Binding Energy of Clindamycin on metal Surfaces

 

 

Figure 7: The cross validated plot of the Actual/Calculated Values and Predicted values of the Binding Energy of the Metal-Inhibitor complex

 

The genetic function approximation (GFA) is a method developed by Rogers and Hopfinger [30] has been employed to uncover significant quantitative structure-property relationship (QSPR) models. This technique brings together Holland’s hereditary protocol (GA) [31] having Friedman’s multivariate adaptive regression splines (MARS) [32]. The GFA algorithm gives a new nonlinear way for developing actual structural models such as QSPR models.  The result along with statistical analysis of the model is presented in Table 5 and 6, this also confirms that a Reasonable performance was obtained with the GFA procedure with F= 404.54 and 1.0 for the limit value intended for introducing variables. The R² and CV R² (Figure 7) were 0.976 and 0.765, respectively, the advantages of GFA over other methods for modelling could be due to a particular introduction, connected with spline terms in making Quantitative Structure-Property Relationship (QSPR) models, since the spline terms make it possible for modelling nonlinear relationships [22, 32], The QSPR model developed via genetic functional approximation (GFA) is given in equation (9) as

 

Binding Energy = - 70.833*E (kJ/mol)-106.348 -------- (9)

 

Predicted values (Binding Energy)	Actual values (Binding Energy)
-40.1715	-29.796
-92.7975	-100.4
-206.671	-202.317
-85.7529	-92.88

 

Table 6:  Predicted and Actual values of the Binding Energy of Metal-Inhibitor Complex

 

This result is consistent with the correlation analysis of the binding energy of the inhibitor to the metal surface, hence confirming the significance of the total energy of the metals to the predictive ability of the model and important for improving the inhibitory property of clindamycin.

 

Corrosion Rate Study Using Clindamycin on the Metal Plates Preparation of the metals

Mild steel, Copper, Zinc plates and Aluminum were obtained from the Department of Metallurgical Engineering, Ahmadu Bello University, Zaria. The chemical composition in wt %  for Mild steel was C= 0.181, Si = 0.056, Mn = 0.474, S = 0.039, P = 0.039, Ni = 0.078, Cr = 0.038 and the rest Fe, while Al contains Mn= 1.281, Pb = 0.064, Ti = 0.024, Cu = 0.51, Si = 0.381, Fe = 0.057 and Al = 96.55%. Mild Steel specimens of size 2 cm × 1 cm × 0.27 cm were used in weight loss experiments, while for the other metals the specimen size 5 cm × 4cm × 0.11cm were used. The concentration of HCl used for the experiments were 1M. Thread was tied to each of mild steel pieces for easy suspension in the media. Each coupon was mechanically polished using emery papers to remove scaling, surface contaminants and oxide film on the surface of the coupons. This was followed by employing the chemical cleaning procedure for removal of corrosion product from the surface of the metals [33,34]. The methods in C.1.1 (Al), C.2.1 (Cu), C.3.3 (Fe) and C.9.1. (Zn) described by ASTM G1-03-E (Standard Practice for Preparing, Cleaning, and Evaluating Corrosion Test Specimens) was used specifically for this study.

 

Gravimetric Method

Weight loss technique was employed in the experiment as follows. Each coupon was weighed using Analytical weighing balance and recorded as weight W₁. The coupon was suspended in a 100 cm³ beaker using a thread. A 100 cm³ of 1M HCl was introduced into reaction beakers. The experimental set-up was kept in the laboratory away from direct sunlight, while the time of exposure for each coupon was carefully noted. Each coupon was retrieved from the test medium in intervals of 24 hours. The corroded coupons were washed in 20%NaOH in 100 g/L zinc dust to stop the corrosion reaction and dried using acetone. The coupons were reweighed and the final weights, W₂ recorded. Weight losses, ΔW = W₁ – W₂ were calculated. The inhibition efficiency % IE and surface coverage θ was determined by using the following equations:

 

ⱳ_o - ⱳ_i

Ɵ =                                                      

ⱳ_o

 

ⱳ_o - ⱳ_i

% IE  =                                                  ×  100

ⱳ_o

 

Where w_i and w₀ are the weight loss value in presence and absence of inhibitor, respectively.

The corrosion rate (CR) is expressed as an increase in corrosion depth per unit time in (mg cm^-2 hr^-1). The corrosion rate equation is given as: 

C R = ΔW/At                                                       

 Where ΔW = weight loss of coupon, t = immersion time, and A = area of coupon.

 

Effect of immersion time and temperature on corrosion rate

The Figures below shows the plot of corrosion rate against immersion time. The corrosion rate increases as the immersion period is lengthen at a fixed concentration of the inhibitor (clindamycin), the changes seem to follow the trend for the weight loss of the metals in the absence of the inhibitor with a slight difference in aluminum. 

 

Figure 8:  Variation of weight loss with immersion time for the corrosion of mild steel, aluminum, copper and zinc plate in blank solution of 1M HCl.

 

Figure 9:  Variation of corrosion rate with immersion time for the corrosion of mild steel, aluminum, copper and zinc plate in solution of 1M HCl containing 0.1 g/L of the clindamycin inhibitor.

 

Metal	%IE
Fe	79.62
Cu	61.48
Al	40.74
Zn	44.21

 

Table 7: Percentage inhibition efficiency of clindamycin inhibitor on some metal surfaces in the presence of 1M HCl solution

 

The percentage (IE) inhibition efficiency greatly correlate with the value of the binding affinity of the inhibitor to the metal surface, the result though significantly confirms our theoretical study differ a bit when considering aluminum. Aluminum when oxidized exhibits a mild inert presence that prevents further corrosion of the metal surface. The high inhibition efficiency of the inhibitor to Fe and Cu, could be associated with the fact the these metals have empty or partial filled d-orbitals for which they could accept lone pair of electron, hence increasing the stability of their newly formed bonds with the inhibitor.

The theoretical computed binding energy (kcal/mol) of clindamycin on the metals Al, Cu, Fe and ZnO were found to be (-92.880, -100.40, -202.317 and 29.796) kcal/mol respectively, this shows that inhibition efficiency of clindamycin will be best achieved when used on iron (Fe) and least when used on zinc (Zn). The quantum chemical parameters point out that this is because Zn having a completely filled d-orbitals limits the possibility of accepting lone pairs of electron from Sulphur, oxygen and nitrogen atoms present in clindamycin and hence have a low reactive index parameters in the adsorption of the inhibitor (clindamycin) on their surfaces.

1. Acharya S, S. Upadhyay (2004) The inhibition of corrosion of mild steel by some fluoroquinolones in sodium chloride solution. Transactions-Indian Institute of Metals 57: 297-306.

2. Lece H.D, K.C. Emregül ,O. Atakol (2008) Difference in the inhibitive effect of some Schiff base compounds containing oxygen, nitrogen and sulfur donors. Corrosion Science 50:1460-1468.

3. Samiento-Bustos E, J.G. Rodriguez, J. Uruchurtu, G. Dominguez-Patiño, V. Salinas-Bravo (2008) Effect of inorganic inhibitors on the corrosion behavior of 1018 carbon steel in the LiBr+ ethylene glycol+ H2O mixture. Corrosion Science 50:2296-2303.

4. Broussard G, O. Bramanti, F. Marchese (1997) Occupational risk and toxicology evaluations of industrial water conditioning. Occupational medicine 47: 337-340.

5. Raja P.B, M.G. Sethuraman (2008) Inhibitive effect of black pepper extract on the sulphuric acid corrosion of mild steel. Materials letters 62:2977-2979.

6. Raja P.B, M.G. Sethuraman (2008) Natural products as corrosion inhibitor for metals in corrosive media-a review. materials letters 62: 13-116.

7. Newman D.J, G.M. Cragg (2007) Natural products as sources of new drugs over the last 25 years. Journal of natural products.

8. Newman D.J, G.M Cragg, K.M. Snader (2003) Natural products as sources of new drugs over the period 1981− 2002. Journal of natural products 66:1022-1037.

9. Struck S, U. Schmidt, B. Gruening, I.S. Jaeger, J. Hossbach et al. (2008) Toxicity versus potency: Elucidation of toxicity properties discriminating between toxins, drugs, and natural compounds, in Genome Informatics,Genome Informatics Series ,World Scientific 231-242.

10. Enick O.V (2006) Do pharmaceutically active compounds have an ecological impact? Biological Sciences Department-Simon Fraser University.

11. Abdallah M (2004) Antibacterial drugs as corrosion inhibitors for corrosion of aluminium in hydrochloric solution. Corrosion Science 46: 981-1996.

12. Eddy N, S. Odoemelam, P. Ekwumemgbo (2009) Inhibition of the corrosion of mild steel in H2SO4 by penicillin G. Scientific Research and Essays 4:33-38.

13. Eddy N, S. Odoemelam (2008) Inhibition of the corrosion of mild steel in acidic medium by penicillin V potassium. Advances in Natural and Applied Sciences 2: 225-233.

14. Laucournet R, C. Pagnoux, T. Chartier, J. Baumard (2001) Catechol derivatives and anion adsorption onto alumina surfaces in aqueous media: influence on the electrokinetic properties. Journal of the European Ceramic Society 21: 869-878.

15. Pang  X, W. Guo, W. Li, J. Xie, B. Hou (2008) Electrochemical, quantum chemical and SEM investigation of the inhibiting effect and mechanism of ciprofloxacin, norfloxacin and ofloxacin on the corrosion for mild steel in hydrochloric acid. Science in China Series B: Chemistry 51: 928-936.

16. Karaman M, D. Antelmi, R. Pashley (2001) The production of stable hydrophobic surfaces by the adsorption of hydrocarbon and fluorocarbon carboxylic acids onto alumina substrates. Colloids and Surfaces A: Physicochemical and Engineering Aspects 182: 285-298.

17. Verma C, M. Quraishi, E. Ebenso (2013) Electrochemical Studies of 2-amino-1, 9-dihydro-9-((2-hydroxyethoxy) methyl)-6H-purin-6-one as Green Corrosion Inhibitor for Mild Steel in 1.0 M Hydrochloric Acid Solution. Int. J. Electrochem. Sci 8: 7401-7413.

18. Hehre W.J,W.W. Huang (1995) Chemistry with Computation: An introduction to SPARTAN. Wavefunction.

19. Kumar A, A.K. Srivastava, S. Gangwar, N. Misra, A. Mondal et al.(2015) Combined experimental (FT-IR, UV–visible spectra, NMR) and theoretical studies on the molecular structure, vibrational spectra, HOMO, LUMO, MESP surfaces, reactivity descriptor and molecular docking of Phomarin. Journal of Molecular Structure 1096: 94-101.

20. Tanak H. , M. Toy (2016)  Molecular structure and vibrational assignment of 1-[N-(2-pyridyl) aminomethylidene}-2(1H)-Naphtalenone by density functional theory (DFT) and ab initio Hartree–Fock (HF) calculations. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 152: 525-529.

21. Yanai T, D.P. Tew, N.C. Handy (2004) A new hybrid exchange–correlation functional using the Coulomb-attenuating method (CAM-B3LYP). Chemical Physics Letters 393:51-57.

22. Wazzan, N.A, F.M. Mahgoub (2014) DFT calculations for corrosion inhibition of ferrous alloys by pyrazolopyrimidine derivatives. Open Journal of Physical Chemistry 4: 6.

23. Perdew J.P, Y. Wang (1992) Accurate and simple analytic representation of the electron-gas correlation energy. Physical Review B 45: 132-144.

24. Johnson E.R, R.A. Wolkow, G.A. DiLabio (2004) Application of 25 density functionals to dispersion-bound homomolecular dimers. Chemical physics letters, 394: 334-338.

25. Ponti A, DFT-based regioselectivity criteria for cycloaddition reactions (2000) The Journal of Physical Chemistry A 104: 8843-8846.

26. Torrent-Sucarrat, M. F. De Proft, P. Ayers, P. Geerlings (2010) On the applicability of local softness and hardness. Physical Chemistry Chemical Physics 12: 1072-1080.

27. Haile J (1992) Molecular dynamics simulation,Wiley, New York.

28. Shuichi N (1991) Constant temperature molecular dynamics methods. Progress of Theoretical Physics Supplement 103: 1-46.

29. Zhong W.-R , M.-P. Zhang, B.-Q. Ai, D.-Q. Zheng (2011) Chirality and thickness-dependent thermal conductivity of few-layer graphene: A molecular dynamics study. Applied Physics Letters 98: 107-113.

30. Rogers D, A.J. Hopfinger (1994) Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. Journal of Chemical Information and Computer Sciences 34:854-866.

31. Holland J.H (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence.

32. Friedman J.H (1991) Multivariate adaptive regression splines. The annals of statistics 1-67.

33. Chand S , Tyagi, M, Tyagi, P, Chandra S, Sharma D (2018). Synthesis,Characterization, DFT of novel, symmetrical, N/O-donor tetradentate Schiff’s base, its Co (II), Ni (II), Cu (II), Zn (II) complexes and their in-vitro human pathogenic antibacterial activity. Egyptian Journal of Chemistry.

34. Abou Elmaaty, Tarek, Eman Abdelaziz, Dalia Nasser, Khaled Abdelfattah, Sherif Elkadi et al. (2018) Khaled Ibrahim El-Nagar. "Microwave and Nanotechnology Advanced Solutions to Improve Ecofriendly Cotton's Coloration and Performance Properties." Egyptian Journal of Chemistry 61: 493-502.