Corrosion Inhibition Potential of Thiosemicarbazide Derivatives on ALuminium: Insight from Molecular Modelling and QSARs Approaches

The potentials of six thiosemicarbazide derivatives towards corrosion inhibition were investigated theoretically using density functional theory (DFT) and quantitative structural-activity relationships (QSARs) methods. Their performance as corrosion inhibitors were evaluated using their calculated quantum chemical parameters such as molecular weight, softness, electronegativity, dipole moments, hardness, bandgap energy ( ∆ E), highest occupied molecular orbital energy (E HOMO ), and the lowest unoccupied molecular orbital energy (E LUMO ). Regression analysis was carried out using the ordinary least square method to develop a model that establishes the relationship between chemical parameters and inhibition e ﬃ ciencies that have been measured experimentally. According to the results, quantum chemical parameters conﬁrm the inhibition potential of TSC 5 to be greater than TSC 2 , while the predicted inhibition e ﬃ ciencies of the studied thiosemicarbazide derivatives correspond to experimentally reported values with a root mean square error (%) of 1.116 and correlation coe ﬃ cient of 0.998. The high correlation demonstrates and validates the quantum chemical approach’s reliability in studying corrosion inhibition on a metal surface. The validation of the developed model internally and externally demonstrates that it is robust and stable, with high predictability.


Introduction
Metals are utilized in most constructional operations because of their high strength contrasted with other material classes.However, the pace at which these metals revert to their natural oxide form, a process known as corrosion, has far-reaching implications for national economies and human life in general.
Although, corrosion is unavoidable yet substantial barriers in form of corrosion control technology can be utilized to slow it down.Over the years, painting, cathodic and anodic protection, galvanizing, and the use of corrosion inhibitors (CIs), have been explored to alleviate the corrosion problem [1][2].Corrosion inhibitors are commonly used in industry because of their low cost and ease of application [3][4].Through molecule adsorption, the corrosion inhibitor forms a passive coating that protects the metal from aggressive corrosion thus limiting the rate of corrosion.Rather than utilizing high-grade carbon steel, corrosion inhibitors have permitted lower-grade carbon steel to be utilized, thus lowering capital costs in most industries [3].Aside from this, organic inhibitors are often employed due to their low toxicity, good solubility, compatibility with metals, and effectiveness at a wide temperature range [5][6].
The efficiency of organic corrosion inhibitors to inhibit corrosion largely depends on the adsorption bond strength which in turn depends on many factors such as type of metal, type of inhibitor, the concentration of inhibitor, and the environment.Besides these variables, most of the notable organic corrosion inhibitors are plane conjugated systems that include various aromatic cycles containing electronegative atoms [7].Adsorption occurs as a result of the interaction of the inhibitor's lone pair and/or -orbitals with the metal surface atoms' d-orbitals, resulting in higher adsorption of the inhibitor molecules onto the surface and the creation of a corrosion protection coating [7].Therefore, the electronic structure of organic inhibitors has a significant impact on how effective they are in preventing metal corrosion.The selection of effective inhibitors has mostly been based on an empirical understanding of their mechanisms of action, macroscopically physicochemical features, and capacity to donate electrons.Thus, molecular structure, hydrophobicity, electron density at donor atoms, dispensability, and solubility are the most important factors to consider when choosing an inhibitor.
While the experimental determination of inhibition efficiency is a critical step in identifying effective molecules in corrosion chemistry, computational techniques such as density functional theory (DFT), qualitative structural activities relationship modeling, and others are becoming increasingly more popular for identifying likely effective molecules in a timely and efficient manner.For instance, quantum chemical computations are a useful tool for investigating and understanding the electronic structures and reaction mechanisms of organic corrosion inhibitors interpreting the experimental results, resolving chemical ambiguities, and predicting molecular parameters that are closely associated with the corrosion inhibition property of the chemical compound.Qualitative structural activities relationship is commonly used to relate the quantum descriptors with experimental inhibition efficiencies as well as, developing models for similar molecules with unknown inhibition efficiency.In essence, it is used to investigate the structure-activity relationship of molecules.The most promising technique used to ascertain the electronic structure of matter at the moment is density functional theory, commonly known as chemical ]reactivity theory [8].
In continuation of our work on the investigation and theoretical prediction of organic molecules as inhibitors of corrosion [9], this research work investigates and predicts the corrosion of thiosemicarbazide derivatives (Figure 1) in the corrosion inhibition of aluminum from the perspective of molecular modeling and quantitative-structural-activity relationships.Thiosemicarbazides are Schiff bases-organic compounds formed as a result of condensation of a carbonyl and an amine and according to literature, they have shown to be potential inhibitors.Several authors [10][11][12] have observed that the presence cyano group and other heteroatoms such as oxygen and sulfur atoms make Schiff bases effective inhibitors in metallic corrosion in alkaline and acidic conditions [13].The nitrogen (N) atom's lone pair of electrons and planarity (π) of the Schiff Bases are also essential structural properties that influence their adsorption on metal surfaces [14].These molecules also are useful intermediates in the production of pharmaceutical and bioactive materials, and they are widely utilized in medicinal chemistry.The corrosion inhibition of thiosemicarbazide derivatives studied has been experimentally investigated by Fouda et al. [15].This work presents a theoretical investigation on molecular and electronic properties of thiosemicarbazide derivatives with the aim of determining the relationship between the structural properties of thiosemicarbazide derivatives and their inhibition efficiency.The model derived from the QSAR relationships might be useful in predicting other derivatives of thiosemicarbazide theoretically.The quantum parameters like hardness (η), dipole moment (µ), molecular orbital energies (E HOMO and E LU MO) , the charge distribution, total energy (E total ), a fraction of electrons (∆N) transfer and electronegativity (χ) values were estimated and correlated with inhibition efficiencies (% IE).

Molecular modeling
Molecular modeling of six ( 6) thiosemicarbazides (Figure 1) were performed using the SPARTAN 14 program package [16].Density Functional Theory (DFT) with the B3LYP exchange functional [17][18] at 6-311G* basis set was utilized to optimize geometrical structures.During optimization, dihedral and bond angles, as well as all bond lengths, were free of constraints while real vibrational frequencies were ensured for the entire geometries.The Fukui functions of thiosemicarbazides derivatives were evaluated using electron populations for their neutral and ionic neutral species.
When an inhibitor is in contact with aluminum (Al), electrons flow from the inhibitor which has lower electronegativity to the metal which has higher electronegativity until the chemical potentials are equal [19].Equation 1, as a first approximation, gives the proportion of electrons transferred (∆N).
where χ inh and χ Al symbolize absolute electronegativities of organic inhibitor and aluminum (Al) respectively while η inh and η Al symbolize absolute hardness of the organic inhibitor and Aluminum (Al) respectively.These parameters are related to electron affinity (A) and ionization potential (I), both of which can be used to predict chemical behavior as shown in equations 2 to 3 [20].
Koopman's theorem [18] asserts that electron affinity (A) and ionization potential (I) are also related to the molecular orbital energies (E HOMO and E LU MO ) of an inhibitor, as shown in Eqs.
(5) and ( 6) Equation ( 7) was used to determine the electrophilicity index which evaluates the stabilization energy when charges are transferred to a system from an environment [23] Whenever the values of chemical potential (µ) and electrophilicity index (ω) are low, it indicates that inhibitory compounds are more reactive nucleophiles, whereas high values indicate that they are more reactive electrophiles Furthermore, the Local reactivity index describes the reactivity of a specific chemical atom in relation to an organic inhibitor's adherence to a certain surface of a metal.Also, using the computation of the determinate variance, the difference in electron density for a nucleophile f + (r) and electrophile f − (r) as the Funki functions may be computed [24].

Quantitative structural activity relationship (QSAR)
QSAR was developed to explain the structure-activity relationship of molecular descriptors based on quantum chemical calculations of six thiosemicarbazide derivatives as corrosion inhibitors.The quality of a model in this type of analysis is determined by the model's ability to fit and predict accurately.This method looks for a relationship in the form of an equation that connects molecular descriptors to the inhibition efficiency determined experimentally.Lukovits' linear equation is commonly employed in corrosion inhibitor research to allow the correlation of quantum molecular descriptors with experimental inhibitory efficacy [25] as shown in equation 10 below X 1 , X 2 .... X n indicate are quantum chemical descriptors of the modelled inhibitors while ά and β are the regression coefficients calculated from regression analysis.

Test of model
The model developed was statistically validated by utilizing the squared fitting factor (R 2 ), adjusted fitting factor (R 2 a ), cross-validation (CV.R 2 ) and variation ratio (F).
The adjusted fitting factor(R 2 a ), is defined as follows: Where N represents the number of observations (study molecules) and p is the number of descriptors, cross-validation (CV.R 2 ) is a mathematical approach for ensuring the reliability of the QSAR model as given in equation 12.
The variance ratio (F) was also used to determine the overall significance of the regression coefficients [26].As shown in equation 13 below, it is defined as the ratio of the regression mean square to deviations mean square: At p < 0.05, a model's estimated F value should be significant; consequently, the F value for the overall significance of the regression coefficients should be high [27].
The E HOMO is associated with the electron donor ability of an inhibitor.The high or elevated E HOMO of inhibitors indicates a higher tendency to donate electrons to the corresponding lower molecular orbital of the metal.This improves the adsorption capacity and inhibition efficiency of inhibitors to the surface of a metal.Therefore, the effectiveness of an inhibitor can be improved by enhancing the transferring process.In Table 1, it is clear that the E HOMO value for the six thiosemicarbazide derivatives molecules (TSC 1−6 ) corresponds to TSC 5 > TSC 2 > TSC 1 > TSC 6 > TSC 4 > TSC 3 .The highest E HOMO (-5.54 eV) for TSC 5 , is indicated as the best electron-donating inhibitor to the empty d-orbital of the metal.Organic inhibitors, on the other hand, not only donate electrons to the metal's empty dorbitals but also accept electrons from the metal's d-orbital.
Thus E LU MO shows the capability of an inhibitor to accept electrons from the metal which would definitely improve the inhibition effect on the surface of the metal.The E LU MO for TSC 1−6 follows the sequence: TSC 6 > TSC 2 > TSC 1 > TSC 4 >TSC 5 >TSC 3 , indicating that the TSC 3 has an improved propensity to accept electrons from the surface of a metal.The evaluation of the distribution of electron density in molecular orbitals (Fig 3) indicates that electrons are localized on the atoms of both aromatic rings of the six studied molecules.
The energy difference which is also known as bandgap energy (∆E) which is the difference in E LU MO and E HOMO of a molecule, relates the inhibitor's reactivity to adsorption on the metal surface.Decreasing the ∆E value of inhibitors increases the reactivity between the metal and inhibiting molecule which consequently increases the binding capacity at the surface of the metal.This increase in binding capacity can result in an increase in the inhibitory efficiency (%IE) of the inhibitor since the energy required to remove the electron from the highest occupied molecular orbital will be less.The addition of -C 6 H 5 , -C 6 H 3 (NO 2 ) 2 -COCH 2 CN, and -COC 6 H 5 substituents to semicarbazide (TSC 6 ) to give their respective derivatives (TSC 1−5 ) greatly have an effect band gap.A very low band gap of 4.42 eV was recorded for TSC 2 when the phenyl group was attached to TSC 1 and a further reduction to 2.94 eV in TSC 3 when the NO 2 substituent was attached to the attached phenyl group could be a result of the destabilization of the highest unoccupied molecular orbital (LUMO) to lower energy level.The presence of a positive charge on the Nitrogen atom in NO 2 deactivates the aromatic system and "sucks" electron density from the aromatic system by positive inductive effect and influences the resonance stabilization of the system [28].The ∆E value shown in Table 1 indicates TSC 3 < TSC 5 < TSC 2 < TSC 4 < TSC 1 < TSC 6 , suggesting that TSC 3 TSC 5 and TSC 2 have a low energy gap, higher reactivity, and therefore, better performance than other molecules.However, the higher experimental inhibition efficiency reported for TSC 3 with the lowest band gap could be a result of an external factor acting on the inhibitor.In an acidic solution, the hydrogen evolved can cause a reduction of the nitro group in TSC 3 which can aid the desorption of TSC 3 thus making it a less effective corrosion inhibitor than TSC 2 and TSC 5 Absolute hardness, electronegativity, and softness are indicators of reactivity that are derived from electronic energy (E) with respect to the number of electrons (N) at a constant external potential t(r) [29].The absolute hardness and softness reactivity descriptors are associated with the description of soft and hard solutions through the acid and base theory [30][31] established that the hardness of all atoms in a molecule is actually equalized, and molecular hardness equals the geometric mean of component atoms' chemical hardness [32].The transfer of electrons between chemical species occurs in every reaction and in contrast to hard molecules, soft molecules are affected by charge transfer.The Principle of Maximum Hardness (PMH) [33][34] states that chemical stability is directly linked to chemical hardness and that molecules that are hard are more stable and less reactive.As a result, the chemical hardness of a molecule denotes the resistance of the electron cloud of ions, atoms or molecules to polarization or deformation resulting from chemical reaction perturbations.The chemical hardness of a reaction also gives vital information for predicting the reaction mechanism and estimating the products generated throughout the reaction [35].The values of absolute hardness for TSC 1−6 follows the sequence: TSC 3 < TSC 5 < TSC 2 ≈ TSC 4 < TSC 1 < TSC 6 .This result shows that TSC 2, TSC 3 and TSC 5 have a low hardness value of 1.47, 2.09, and 2.51 eV respectively, compared to the other three studied derivatives.Hence, these three molecules are less stable and more reactive.Derivatives with low hardness values show low bandgap and hence the high inhibition efficiency.This corresponds to the widely held idea that soft molecules should have a small energy gap whereas hard molecules should have a high energy gap.The softness values of the studied molecules using DFT is as follows the sequence: TSC 3 < TSC 5 < TSC 2 < TSC 4 < TSC 1 < TSC 6 .This trend shows that the first three soft thiosemicarbazide derivatives (TSC 2 , TSC 3 and TSC 5 ) have experimental inhibition efficiencies that are more than 90%.Hence low global hardness value (that is, the high global softness value) is likely to show high inhibition efficiency.The experimental inhibitory efficiency of the investigated compounds agrees with this trend.
The tendency of an atom in a molecule to attract shared pair of electrons to itself is known as electronegativity and it is a key concept to understanding the nature of chemical interactions [34].The electronegativities of atoms in a molecule are equilibrated during molecule formation and molecular electronegativity is the geometric mean of the atoms' electronegativities [34].The electronegativity values of studied molecules TSC 1− 6 (Table 1) are: TSC 6 < TSC 1 < TSC 2 < TSC 5 < TSC 3 < TSC 4 .
The dipole moment (DM) is the first derivative of the energy with respect to an applied field [36] electronic parameter resulting from the unequal distribution of charges on various atoms in a molecule.It measures the polarity of a polar covalent bond, predicts the direction of the corrosion inhibition process, and gives information about the distribution of electrons in the molecules [36][37][38].The total dipole moment, on the other hand, simply represents a molecule's global polarity.
The overall molecular dipole moment can be estimated as the vector sum of individual bond dipole moments for a whole molecule.A high dipole moment may increase the adsorption between the metal surface and the molecules of inhibitors [39].This statement is consistent with TSC 3 .However, TSC 1 and TSC 4 with high dipole moment (5.65 and 6.07 Debye respectively) have poor inhibition efficiency, while TSC 2 and TSC 5 with low dipole moment, have a high inhibition efficiency.These results show that there is inconsistency with the use of dipole moment predicting the direction of a corrosion inhibition reaction.
In the examined compounds, our theoretical results confirmed that there is no substantial link between inhibitory effectiveness and dipole moment.The electrostatic potential may be used to deduce the dipole's orientation (right panels of Fig. 3).The colors represent the electrostatic potential value.Colors lean toward red represent places with negative potential (where a positive charge is most likely to be attracted), while colors that lean toward blue represent areas of positive potential (where a positive charge is least likely to be attracted).The region of highest electron density is found around the sulphur atom and around the nitrogen atom.
Log P is responsible for the hydrophobicity (property of a molecule to repel water) of a molecule [40][41].In corrosion studies, hydrophobicity can be related to the process at which oxide/hydroxide layers which retards the corrosion process are formed on the surface of the metal.Hydrophobicity will increase when the solubility of the molecule in water decreases [42].The results obtained for log P showed that TSC 5 > TSC 2 > TSC 3 > TSC 4 > TSC 1 > TSC 6 .It was also found that the values of log P are closely related to the corrosion inhibition efficiencies of the investigated derivatives.
The effective surface coverage and molecular size of the inhibitor on the surface of metal are determined by using molecular weight and volume quantum parameters.These parameters determine how well a molecule can be adsorbed atop and cover a metal surface, thereby isolating it from the corroding environment.The molecular volume and weight for the six studied molecules increases in the following order: TSC 3 > TSC 5 > TSC 2 > TSC 4 > TSC 1 > TSC 6 .The presence of the phenyl group increases the molecular size of TSC 1 , would lead to a larger surface coverage that TSC 6 on the aluminum.Also, TSC 2 has two phenyl groups and consequently more effective than TSC1.Hence as the value of this parameter increases, so do the molecule's corrosion inhibition potential increases The fraction of electrons transferred (∆N) is the number of electrons transferred from the inhibiting molecule to the surface of the metal.It also presents the inhibitor's ability to transfer electrons.When the electron-donating capacity of an inhibitor is improved, its inhibitory efficiency on the surface of metal also improves.Similarly, the efficiency of an inhibitor increases on the face of metal as the number of electron transfers increases.The number of electrons transferred (∆N) in an organic inhibitor should be less than 3.6 (electrons) for it to be regarded effective in increasing the corrosion inhibition efficiency, but if the ∆N is greater than 3.6 (electrons) the inhibition efficiency decreases [25].Molecules with higher electron transfer value, has a greater tendency to donate electrons to molecules that accept electrons.This implies that inhibiting molecules with higher ∆N have greater tendency to adsorb on the surface of the metal which consequently increases their inhibition efficiencies [25].The ∆N values for the six studied molecules range from -0.517-0.45e − .For the molecules TSC 1−6 , the largest proportion of electron transferred (∆N) is substituent groups attached to the thiosemicarbazide skeletal ring.This result demonstrates that the studied thiosemicarbazide derivatives are electron-donating molecules while the aluminum surface could be an electron-accepting molecule.
It's crucial to evaluate the circumstance involving a molecule that will acquire a particular amount of charge at one center and then back-donate the same amount of charge through the same or a different center.In a simple model of charge transfer for donation and back donation of charges [42], an electronic back donation process can be a result of the interaction between the inhibiting molecule and the surface of a metal.Though, it is necessary to note that these values do not predict the occurrence of a back donation process.Back-donation charges for the studied molecules are less than zero (-0.75 to -1.18 e) indicating that the charges transferred to the molecules, accompanied by a back-donation from the molecule, are energetically favoured since η > 0 and ∆E back−donation < 0. Therefore, TSC 3 and TSC 4 could be more energetically favoured than TSC 1 and TSC 2 .The result is consistent with the concept that states that if both charge transfer (i.e. to the molecule and back-donation processes from the molecule) occurs, the change in energy is directly proportional to the hardness of the molecule in equation 3.
The global electrophilicity index (ω) gives information on the nucleophilicity and electrophilicity nature of organic inhibitors.An inhibitor with a high electrophilicity index (ω) value indicates a high tendency to act as an electrophile and conversely a low electrophilicity index (ω) value indicates a high tendency to act as a nucleophile.The electrophilicity values (Table 1) show that TSC 3 has the highest value and as a result, the largest ability to receive electrons from the metal thereby increasing the adsorption capacity of the TSC 3 on the surface of the metal.The electrophilicity values show the following sequence: TSC 3 > TSC 5 > TSC 4 > TSC 2 > TSC 1 > TSC 6 .The inhibitors function as Lewis bases in a corroding system, whereas the metal acts as a Lewis acid.
The distribution of charges in the chemical structure of organic inhibitors influences adsorption.The charge distribution on a molecule might reasonably be taken as an indication for locating the positions of interaction between the inhibitor and the metal surface.Charge distribution in the studied molecules is estimated using Mulliken population density [9,43].It is a way of estimating inhibitors' adsorption centers and consequently determining the site that corresponds to a molecular center that accepts the charge as well as a molecular center that will donate back charges via the same center or another center.Moreover, In the physisorption model, electrostatic attraction is expected between the metal surface and inhibiting molecules.The charges on heteroatoms of inhibitors can apparently be utilized as an index to account for physical adsorption.According to this hypothesis, the molecule with the highest atomic charge on a heteroatom will have a greater potential to physically adsorb on the metal surface [44].The average Mulliken charges for heteroatoms present in each of the studied thiosemicarbazides (denoted as Heteroatom) are -3.16,-2.57, -2.18, -1.768, -1.75, and -0.57, for TSC 4 , TSC 3 , TSC 1 , TSC 6 , TSC 2, and TSC 5 , inhibitors respectively.Inhibitors with more negatively charged heteroatom will adsorb more on the surface of the metal through the donor-acceptor reaction [45].Therefore, a higher electron density on TSC 4 and TSC 3 and TSC 1 heteroatoms would promote its physical adsorption on the surface of the metal.
Condensed Fukui functions are often used to investigate local reactivity in inhibitors.The Fukui function reveals the location in a chemical compound where electrophilic( f − k ), nucleophilic ( f + k ) and radical reactions are most likely to occur.f + k measures the change in density when the inhibiting molecule gains electrons which is related to the reactivity with respect to nucleophilic attack.Conversely, f − k is related to the reactivity when the molecule losses electrons which is reactivity related to electrophilic attack.Atoms with larger Fukui function values are better reactive atomic centers than lower values in a given molecule.The Fukui indices ( f + k and f − k ) of the studied molecules calculated from Mulliken charge population analysis are given in Table 2 for TSC 1 , TSC 2 , TSC 3 , TSC 4 , TSC 5 , and TSC 6 , respectively.The highest value of f − k for the nonhydrogen atoms was found on C7 (0.06) of TSC 1 , C7 (0.96) and N (0.96), for TSC 2 , C7 (0.082), C12 (0.065) and O4 (0.0854) for TSC 3 , C8 (0.05) and C7 (0.047) for TSC4, C12 (0.037), C 7 (0.065) and O1 (0.063) for TSC 5 and C (0.042) for TSC 6 which represents the most probable centers for electrophilic attack.However, the highest values for f + k for non-hydrogen atoms are found on S1 (0.323) and N1 (0.051) for TSC 1 , S1 (0.262) and N1 (0.048) for TSC 2 , S1 (0.321) and N1 (0.057) for TSC3, S1 (0.386) and N1 (0.065) for TSC4, S1 (0.195), N1 (0.064), N2 (0.044) for TSC 5 , S1 (0.233) and N (0.311) for TSC 6 .These represent the most probable centers for nucleophilic attack in the molecules.

3.2.
Quantitative Structure-Activity Relationships (QSARs) modeling Experimentally, the potentiodynamic polarization method has been used to evaluate the inhibitory efficiency (IE) of thiosemicarbazide derivatives (TSC 1−−6 ) (15).However, theoretically calculated inhibition efficiencies (IE%) of studied thiosemicarbazide derivatives (TSC 1−6 ) were predicted using the QSARs model (equation 14) developed via linear regression of molecular descriptors (Table 1).The experimentally measured inhibitory efficiency (IE percent) against steel served as dependent variables, whereas the appropriate molecular descriptors as determined by Pearson's matrix (Figure 4) served as independent variables.These selected descriptors were used to build a linear QSAR model to understand how linear regression equations might explain structural key points corresponding to differential behavior in chemical descriptors against corrosion.
As indicated in equation 14, the molecular descriptors solvation energy (E solv ), softness (S) electronegativity, and dipole moment define the corrosion inhibition of the thiosemicarbazide derivatives.The acceptability of the quality of the model developed from the QSAR investigation is determined by its predictabilities and    The model was also statistically validated using the squared fitting factor (R 2 ), adjusted fitting factor (R 2 a ), cross-validation (CV.R 2 ), and variation ratio (F).The estimated R 2 (0.9982) revealed a reasonable fitness as well as the model's efficiency, as shown in equation 14.The calculated CV.R 2 (0.9342) is greater than 0.5 (standard) while the R 2 a (0.9143) is greater than 0.6.These results show that the model is statistically reliable and acceptable and have strong external predictability.

Conclusion
The quantum chemical descriptors of thiosemicarbazide derivatives were investigated in order to elucidate their electronic structure, reactivity, and predict their potential for corrosion inhibition using a computational approach.Through DFT/B3LYP/6-311G* computational method, a relationship between quantum descriptors of six thiosemicarbazide derivatives and their effectiveness in preventing corrosion was established.The correlations were found to be effective in developing thiosemicarbazide inhibitors with appropriate substituents capable of donating electrons to the metal's surface.Because of their high E HOMO , ∆N, and low ∆E values, TSC 2 , TSC 3 and TSC 5 are projected to have the best inhibition efficiency, allowing for efficient electron transfer and hence a higher performance as corrosion inhibitors.This theoretical work has an excellent correlation with experimental corrosion inhibition efficiency, indicating that the approach used in this study is reliable.The additional NO 2 and CN functional groups present in TSC 3 and TSC 4 respectively may give insight into the preferred moieties to look for when designing corrosion inhibitors while the correlations and model developed might aid in the design of novel thiosemicarbazide inhibitors with appropriate substituents capable of donating electrons to a metal's surface.

Figure 1 .
Figure 1.Structures of the studied thiosemicarbazide derivatives

Figure 5 .
Figure 5. Graphical representation of the experimental and predicted inhibition efficiency (%) of the studied thiosemicarbazise derivatives

Table 2 .
Fukui indices for the atoms of the studied thiosemicarbazide derivatives

Table 3 .
Experimental and Predicted inhibition efficiencies of thiosemicarbazide derivatives IE) to illustrate the relationship between the two.The developed model was quite resilient in predicting good experimental values, Therefore, the theoretical percentage inhibition efficiency for the studied compounds follows: TSC 2 > TSC 6 > TSC 3 > TSC 5 > TSC 4 > TSC 1 .