J. Nig. Soc. Phys. Sci. 5 (2023) 915

Journal of the
Nigerian Society

of Physical
Sciences

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

Approaches

B. T. Ogunyemia,∗, F. K. Ojob

aPhysical and Computational Chemistry Unit, Department of Chemistry, Federal University Otuoke, Bayelsa State, Nigeria
bDepartment of Chemistry, Bingham University, Karu Nasarawa State, Nigeria

Abstract

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 (EHOMO), and the lowest unoccupied molecular orbital energy (ELU MO). Regression analysis was
carried out using the ordinary least square method to develop a model that establishes the relationship between chemical parameters and inhibition
efficiencies that have been measured experimentally. According to the results, quantum chemical parameters confirm the inhibition potential of
TSC5 to be greater than TSC2, while the predicted inhibition efficiencies of the studied thiosemicarbazide derivatives correspond to experimentally
reported values with a root mean square error (%) of 1.116 and correlation coefficient 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.

DOI:10.46481/jnsps.2023.915

Keywords: DFT, Corrosion inhibitors, QSAR, Thiosemicarbazide

Article History :
Received: 04 July 2022
Received in revised form: 04 November 2022
Accepted for publication: 07 December 2022
Published: 14 January 2023

c© 2023 The Author(s). Published by the Nigerian Society of Physical Sciences under the terms of the Creative Commons Attribution 4.0 International license

(https://creativecommons.org/licenses/by/4.0). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Communicated by: T. Owolabi

1. Introduction

Metals are utilized in most constructional operations be-
cause 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 im-
plications for national economies and human life in general.

∗Corresponding author tel. no: +234 8035811574
Email address: ogunyemibt@fuotuoke.edu.ng,

btogunyemi@yahoo.com (B. T. Ogunyemi)

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 protec-
tion, galvanizing, and the use of corrosion inhibitors (CIs), have
been explored to alleviate the corrosion problem [1-2]. Corro-
sion inhibitors are commonly used in industry because of their
low cost and ease of application [3-4]. Through molecule ad-
sorption, 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,

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 2

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 cor-
rosion 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]. Adsorp-
tion occurs as a result of the interaction of the inhibitor’s lone
pair and/or -orbitals with the metal surface atoms’ d-orbitals, re-
sulting 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 ac-
tion, 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 mod-
eling, 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 in-
hibitors interpreting the experimental results, resolving chem-
ical ambiguities, and predicting molecular parameters that are
closely associated with the corrosion inhibition property of the
chemical compound. Qualitative structural activities relation-
ship is commonly used to relate the quantum descriptors with
experimental inhibition efficiencies as well as, developing mod-
els for similar molecules with unknown inhibition efficiency. In
essence, it is used to investigate the structure-activity relation-
ship of molecules. The most promising technique used to as-
certain the electronic structure of matter at the moment is den-
sity functional theory, commonly known as chemical ]reactivity
theory [8].

In continuation of our work on the investigation and theoret-
ical 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 inhi-
bition of aluminum from the perspective of molecular modeling
and quantitative-structural-activity relationships. Thiosemicar-
bazides 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 [10-12] 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 essen-
tial 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 cor-
rosion inhibition of thiosemicarbazide derivatives studied has
been experimentally investigated by Fouda et al. [15]. This
work presents a theoretical investigation on molecular and elec-
tronic properties of thiosemicarbazide derivatives with the aim
of determining the relationship between the structural proper-
ties of thiosemicarbazide derivatives and their inhibition effi-
ciency. 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 (EHOMO and ELU MO),
the charge distribution, total energy (Etotal), a fraction of elec-
trons (∆N) transfer and electronegativity (χ) values were esti-
mated and correlated with inhibition efficiencies (% IE).

2. Methodology

2.1. 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 ex-
change 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 con-
straints 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), elec-
trons flow from the inhibitor which has lower electronegativity
to the metal which has higher electronegativity until the chem-
ical potentials are equal [19]. Equation 1, as a first approxima-
tion, gives the proportion of electrons transferred (∆N).

∆N =
χAl −χinh

2(ηAl + ηinh)
(1)

where χinh and χAl symbolize absolute electronegativities of or-
ganic inhibitor and aluminum (Al) respectively while ηinh and
ηAl symbolize absolute hardness of the organic inhibitor and
Aluminum (Al) respectively.

These parameters are related to electron affinity (A) and ion-
ization potential (I), both of which can be used to predict chem-
ical behavior as shown in equations 2 to 3 [20].

η =
I P − EA

2
(2)

χ = −µ =
I P + EA

2
(3)

Theoretically, Aluminum (Al) has absolute electronegativity(χAl)
and hardness (ηAl) values of 3.23 eV/mole and 0 eV/mole) re-
spectively[19, 21].

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 3

Figure 1. Structures of the studied thiosemicarbazide derivatives

Meanwhile, the inverse of hardness, softness, is likewise a
quantum parameter that may be determined using equation 4
[22].

S =
1
η

(4)

Koopman’s theorem [18] asserts that electron affinity (A) and
ionization potential (I) are also related to the molecular orbital
energies (EHOMO and ELU MO) of an inhibitor, as shown in Eqs.
(5) and (6)

I P = −EHOMO (5)

EA = −ELU MO (6)

Equation (7) was used to determine the electrophilicity in-
dex which evaluates the stabilization energy when charges are

transferred to a system from an environment [23]

ω =
µ2

4η
(7)

Whenever the values of chemical potential (µ) and electrophilic-
ity 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 reac-
tivity of a specific chemical atom in relation to an organic in-
hibitor’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].

f +(r) = qk
N+1
(r) − qk

N
(r) (for nucleophilic attack) (8)

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 4

f −(r) f k == qk
N
(r) − qk

N−1
(r) (for electrophilic attack) (9)

Where qkN+1(r) , qk
N
(r) qk

N−1
(r) are the electronic densities of an-

ionic, neutral, and cationic species respectively.

2.2. Quantitative structural activity relationship (QSAR)

QSAR was developed to explain the structure-activity rela-
tionship 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 com-
monly employed in corrosion inhibitor research to allow the
correlation of quantum molecular descriptors with experimen-
tal inhibitory efficacy [25] as shown in equation 10 below

%IE = ά + β1 X1 + β2 X2..........βn Xn (10)

X1, X2.... Xn indicate are quantum chemical descriptors of
the modelled inhibitors while ά and β are the regression coeffi-
cients calculated from regression analysis.

2.3. Test of model

The model developed was statistically validated by utiliz-
ing the squared fitting factor (R2), adjusted fitting factor (R2a ),
cross-validation (CV.R2) and variation ratio (F).

The adjusted fitting factor(R2a), is defined as follows:

R2a =
N − 1) X R2 − P

N − P − 1
(11)

Where N represents the number of observations (study molecules)
and p is the number of descriptors, cross-validation (CV.R2) is a
mathematical approach for ensuring the reliability of the QSAR
model as given in equation 12.

CV.R2 =
∑

(Yobs − Ycal)
2∑ (

Yobs − Ŷobs
)2 (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:

F =

∑
(Ycal−Ŷobs)2

p∑
(Yobs−Ycal )

2

N−P−1

(13)

At p < 0.05, a model’s estimated F value should be significant;
consequently, the F value for the overall significance of the re-
gression coefficients should be high [27].

3. Results and Discussion

3.1. Quantum descriptors of inhibitors

The optimized structures of the six thiosemicarbazide deriva-
tives using DFT/B3LYP/6-311 G* are displayed in Figure 2.
Suggested quantum descriptors that may be responsible for the
effective inhibition of the investigated molecules are; Energy
band gap (∆E), EHOMO, ELU MO, dipole moment (DM), polar-
izability, ovality, Log P, Affinity (EA), global electrophilicity
(ω), Ionization Potential (IP), softness (S), electronegativity (χ),
chemical hardness (µ), total energy, electron transfer (∆N) and
solvation energy (Esolv), (Table 1).

The EHOMO is associated with the electron donor ability of
an inhibitor. The high or elevated EHOMO of inhibitors indi-
cates a higher tendency to donate electrons to the corresponding
lower molecular orbital of the metal. This improves the adsorp-
tion capacity and inhibition efficiency of inhibitors to the sur-
face 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 EHOMO value for the six thiosemicarbazide
derivatives molecules (TSC1−6) corresponds to TSC5 > TSC2 >
TSC1 > TSC6 > TSC4 > TSC3. The highest EHOMO (-5.54 eV)
for TSC5, 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 d-
orbitals but also accept electrons from the metal’s d-orbital.

Thus ELU 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 ELU MO for
TSC1−6 follows the sequence: TSC6 > TSC2 > TSC1 > TSC4
>TSC5 >TSC3, indicating that the TSC3 has an improved propen-
sity to accept electrons from the surface of a metal. The evalua-
tion 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 en-
ergy (∆E) which is the difference in ELU MO and EHOMO 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 in-
crease in the inhibitory efficiency (%IE) of the inhibitor since
the energy required to remove the electron from the highest oc-
cupied molecular orbital will be less. The addition of -C6H5,
-C6H3(NO2)2 -COCH2CN, and –COC6H5 substituents to semi-
carbazide (TSC6) to give their respective derivatives (TSC1−5)
greatly have an effect band gap. A very low band gap of 4.42 eV
was recorded for TSC2 when the phenyl group was attached to
TSC1 and a further reduction to 2.94 eV in TSC3 when the NO2
substituent was attached to the attached phenyl group could be
a result of the destabilization of the highest unoccupied molec-
ular orbital (LUMO) to lower energy level. The presence of
a positive charge on the Nitrogen atom in NO2 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].

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 5

Figure 2. Optimized molecular structures of thiosemicarbazide derivatives using DFT/B3LYP/6-311G*

Table 1. Quantum chemical parameters of some of the thiosemicarbazide derivatives using DFT/B3LYP/6-31 1G∗ method
Quantum parameters TSC 1 TSC2 TSC 3 TSC 4 TSC 5 TSC 6
EHOMO (eV) -5.68 -5.56 -6.22 -6.07 -5.54 -5.76
ELU MO (eV) -0.63 -1.14 -3.28 -1.13 -1.36 -0.20
IP (eV) 5.68 5.56 6.22 6.17 5.94 5.76
EA (eV) 0.63 0.53 3.28 1.13 1.36 0.20
Hardness (η) 2.53 2.221 1.47 2.52 2.09 2.78
Softness (S) 0.404 0.397 0.680 0.396 0.436 0.35
Electronegativity (χ) 3.155 3.35 4.75 3.65 3.45 2.98
Electron transfer (∆N) e− 0.0145 -0.027 -0.517 -0.083 -0.053 0.045
EBack−donation -0.788 -0.838 -1.18 -0.91 -0.86 -0.75
Electrophilicity index (ω)D2/eV 1.967 2.526 7.674 2.643 2.85 1.597
Energy difference (∆E) eV 5.05 4.42 2.94 5.04 4.18 5.56
polarizability (α) 53.50 60.54 64.44 57.93 62.25 46.43
Log p 0.33 1.11 0.37 0.34 1.14 -0.67
Area (A) cm3 189.62 273.01 322.49 248.90 294.11 108.60
Volume (V) cm3 164.20 250.67 292.94 218.81 270.74 78.11
ovality 1.31 1.42 1.51 1.42 1.45 1.22
Solvation energy (Esol) kJ/mol -58.13 -41.67 -40.08 -52.90 -46.05 -67.65
Dipole moment (debye) 5.65 5.02 2.13 6.07 2.06 4.07
Molecular weight (amu) 167236 243.334 333.328 235.271 271.244 91.138
PSA 49.083 32.635 104.823 69.777 42.974 66.43
Exp. IE 27.4 94.3 92.8 60.8 95.5 27

Polar surface area(PSA), ionization potential (IA), Electron Affinity (EA)

The ∆E value shown in Table 1 indicates TSC3 < TSC5 <
TSC2 < TSC4 < TSC1 < TSC6, suggesting that TSC3 TSC5 and
TSC2 have a low energy gap, higher reactivity, and therefore,
better performance than other molecules. However, the higher
experimental inhibition efficiency reported for TSC3 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 TSC3 which can aid

the desorption of TSC3 thus making it a less effective corrosion
inhibitor than TSC2 and TSC5

Absolute hardness, electronegativity, and softness are indi-
cators of reactivity that are derived from electronic energy (E)
with respect to the number of electrons (N) at a constant ex-
ternal 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]

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 6

established that the hardness of all atoms in a molecule is ac-
tually equalized, and molecular hardness equals the geometric
mean of component atoms’ chemical hardness [32]. The trans-
fer of electrons between chemical species occurs in every re-
action and in contrast to hard molecules, soft molecules are af-
fected 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 result-
ing from chemical reaction perturbations. The chemical hard-
ness 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 TSC1−6 follows the sequence: TSC3 < TSC5 < TSC2 ≈
TSC4 < TSC1< TSC6. This result shows that TSC2, TSC3 and
TSC5 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: TSC3 < TSC5 < TSC2 < TSC4 < TSC1
< TSC6. This trend shows that the first three soft thiosemi-
carbazide derivatives (TSC2, TSC3 and TSC5) have experimen-
tal 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 in-
teractions [34]. The electronegativities of atoms in a molecule
are equilibrated during molecule formation and molecular elec-
tronegativity is the geometric mean of the atoms’ electronega-
tivities [34]. The electronegativity values of studied molecules
TSC1− 6 (Table 1) are: TSC6 < TSC1< TSC2 < TSC5 < TSC3 <
TSC4.

The dipole moment (DM) is the first derivative of the en-
ergy 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 co-
valent bond, predicts the direction of the corrosion inhibition
process, and gives information about the distribution of elec-
trons in the molecules [36-38]. 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 TSC3. However, TSC1 and
TSC4 with high dipole moment (5.65 and 6.07 Debye respec-
tively) have poor inhibition efficiency, while TSC2 and TSC5

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 reac-
tion.

In the examined compounds, our theoretical results con-
firmed that there is no substantial link between inhibitory ef-
fectiveness 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 in-
crease when the solubility of the molecule in water decreases
[42]. The results obtained for log P showed that TSC5 > TSC2
> TSC3 > TSC4 > TSC1 > TSC6. 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 in-
hibitor on the surface of metal are determined by using molec-
ular weight and volume quantum parameters. These parame-
ters 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: TSC3 >
TSC5 > TSC2 > TSC4 > TSC1 > TSC6. The presence of the
phenyl group increases the molecular size of TSC1, would lead
to a larger surface coverage that TSC6 on the aluminum. Also,
TSC2 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 sur-
face of the metal. It also presents the inhibitor’s ability to trans-
fer electrons. When the electron-donating capacity of an in-
hibitor 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 trans-
fers 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 ef-
ficiency, but if the ∆N is greater than 3.6 (electrons) the inhi-
bition efficiency decreases [25]. Molecules with higher elec-
tron transfer value, has a greater tendency to donate electrons
to molecules that accept electrons. This implies that inhibit-
ing 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 stud-
ied molecules range from -0.517- 0.45 e−. For the molecules
TSC1−6, the largest proportion of electron transferred (∆N) is

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 7

Figure 3. Molecule orbital density distribution of studied thiosemicarbazides: HOMO (left), LUMO (middle) and electrostatic potential (right)

associated with molecule TSC3, while the lowest proportion
is associated with TSC6 which has the lowest inhibition effi-
ciency. ∆N for molecules TSC1−6 increases in the following
order: TSC3 > TSC4 > TSC5 > TSC2 > TSC1 > TSC6. The

results indicate that there is no correlation between the trend
in the ∆N values of these studied compounds and the trend in
the experimentally determined inhibition efficiency. The ∆N
values are strongly influenced by the molecular structure and

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 8

substituent groups attached to the thiosemicarbazide skeletal
ring. This result demonstrates that the studied thiosemicar-
bazide 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 occur-
rence of a back donation process. Back-donation charges for
the studied molecules are less than zero (-0.75 to -1.18 e) in-
dicating that the charges transferred to the molecules, accom-
panied by a back-donation from the molecule, are energetically
favoured since η > 0 and ∆Eback−donation < 0. Therefore, TSC3
and TSC4 could be more energetically favoured than TSC1 and
TSC2. 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 di-
rectly 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 in-
hibitors. 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 val-
ues (Table 1) show that TSC3 has the highest value and as a
result, the largest ability to receive electrons from the metal
thereby increasing the adsorption capacity of the TSC3 on the
surface of the metal. The electrophilicity values show the fol-
lowing sequence: TSC3 > TSC5 > TSC4 > TSC2 > TSC1 >
TSC6. 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 or-
ganic inhibitors influences adsorption. The charge distribution
on a molecule might reasonably be taken as an indication for lo-
cating 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 do-
nate back charges via the same center or another center. More-
over, In the physisorption model, electrostatic attraction is ex-
pected 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 TSC4, TSC3, TSC1, TSC6, TSC2, and TSC5, in-
hibitors 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 TSC4 and TSC3 and TSC1 heteroatoms would pro-
mote its physical adsorption on the surface of the metal.

Condensed Fukui functions are often used to investigate lo-
cal reactivity in inhibitors. The Fukui function reveals the lo-
cation in a chemical compound where electrophilic( f −k ), nucle-
ophilic ( 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 val-
ues 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 analy-
sis are given in Table 2 for TSC1, TSC2, TSC3, TSC4, TSC5,
and TSC6, respectively. The highest value of f −k for the non-
hydrogen atoms was found on C7 (0.06) of TSC1, C7 (0.96) and
N (0.96), for TSC2, C7 (0.082), C12 (0.065) and O4 (0.0854)
for TSC3, C8 (0.05) and C7 (0.047) for TSC4, C12 (0.037), C7
(0.065) and O1 (0.063) for TSC5 and C (0.042) for TSC6 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 TSC1, S1 (0.262) and
N1 (0.048) for TSC2, 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 TSC5, S1 (0.233) and N (0.311) for TSC6. These
represent the most probable centers for nucleophilic attack in
the molecules.

3.2. Quantitative Structure-Activity Relationships (QSARs) mod-
eling

Experimentally, the potentiodynamic polarization method
has been used to evaluate the inhibitory efficiency (IE) of thiosemi-
carbazide derivatives (TSC 1−−6) (15). However, theoretically
calculated inhibition efficiencies (IE%) of studied thiosemicar-
bazide derivatives (TSC1−6) were predicted using the QSARs
model (equation 14) developed via linear regression of molec-
ular descriptors (Table 1). The experimentally measured in-
hibitory efficiency (IE percent) against steel served as depen-
dent variables, whereas the appropriate molecular descriptors as
determined by Pearson’s matrix (Figure 4) served as indepen-
dent variables. These selected descriptors were used to build a
linear QSAR model to understand how linear regression equa-
tions might explain structural key points corresponding to dif-
ferential behavior in chemical descriptors against corrosion.

As indicated in equation 14, the molecular descriptors sol-
vation energy (Esolv), softness (S) electronegativity, and dipole
moment define the corrosion inhibition of the thiosemicarbazide
derivatives.

%I E = 262 − 117 ∗ so f tness + 7.81 ∗ Electronegativity
+ 2.90 ∗ Esolv − 4.87 ∗ Dipole (14)

The acceptability of the quality of the model developed from
the QSAR investigation is determined by its predictabilities and

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Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 9

Table 2. Fukui indices for the atoms of the studied thiosemicarbazide derivatives
TSC 1 TSC 2 TSC 3 TSC 4 TSC 5 TSC 6

Atoms f k− f k + atoms f k− f k + atom f k− f k + atom f k− f k + atoms f k− f k + atoms f k− f k +

CI 0.026 0.041 CI 0.067 0.027 CI 0.001 0.029 CI 0.016 0.023 CI 0.007 0.014 N1 -
0.01

0.051

H2 0.032 0.026 H2 0.024 0.026 H2 0.007 0.024 H2 0.045 0.042 H2 0.018 0.031 H1 0.036 0.042
C2 0.008 0.014 C2 0.006 0.009 C2 0.003 0.011 C2 0.007 0.01 C2 0.002 0.009 C7 0.067 -

0.024
H4 0.071 0.069 H4 0.055 0.059 H4 0.022 0.063 H4 0.065 0.06 H4 0.038 0.052 S1 0.026 0.323
C3 0.048 0.04 C3 0.036 0.038 C3 0.006 0.044 C3 0.045 0.039 C3 0.027 0.032 N2 0.031 0.019
H3 0.089 0.08 H3 0.067 0.07 H3 0.024 0.076 H3 0.08 0.069 H3 0.051 0.062 H11 0.049 0.041
C4 0.006 0.014 C4 0.004 0.009 C4 0.002 0.011 C4 0.006 0.009 C4 0.003 0.009 N3 -

0.006
-
0.003

H6 0.076 0.073 H6 0.056 0.06 H6 0.016 0.066 H6 0.07 0.062 H6 0.046 0.056 H7 0.052 0.04
C5 0.017 0.033 C5 0.014 0.025 C5 0.001 0.028 C5 0.023 0.027 C5 0.013 0.023
H5 0.068 0.067 H5 0.042 0.051 H5 -

0.014
0.062 H5 0.061 0.052 H5 0.037 0.048

C6 0.039 0.021 C6 0.023 0.015 C6 0 0.02 C6 0.037 0.001 C6 0.014 0
N1 -

0.01
0.051 N1 -

0.005
0.048 N1 -

0.005
0.057 N1 -

0.007
0.069 N1 0.001 0.064

H1 0.036 0.042 H1 0.024 0.031 H1 -
0.013

0.04 H1 0.043 0.041 H1 0.03 0.042

C7 0.067 -
0.024

C7 0.096 -
0.029

C7 0.082 -
0.029

C7 0.047 -
0.042

C7 0.065 -
0.031

S1 0.026 0.323 S1 0.092 0.262 S1 0.042 0.321 S1 0.028 0.286 S1 0.042 0.295
N2 0.031 0.019 N2 0.022 0.02 N2 -

0.022
0.026 N2 0.019 0.03 N2 0.003 0.044

H11 0.049 0.041 H11 0.037 0.027 H11 0.117 -
0.125

H11 0.018 0.018 H11 0.265 0.023

N3 -
0.006

-
0.003

N3 0.094 -
0.836

N3 0.016 -
0.02

N3 -
0.003

-
0.005

N3 0.007 0.049

H7 0.052 0.04 H7 0.03 0.031 H7 0.026 0.011 H7 0.019 0.011 H7 0.011 0.026
C8 0.004 0.004 C8 0.067 -

0.005
C8 0.05 0.016 C8 0.065 0.014

C9 -
0.388

0.01 C9 -
0.029

-
0.451

C9 0.025 0.005 C9 0.009 0.001

C10 0.031 0.006 C10 -
0.045

0.01 N4 0.001 0.007 C10 0.023 0.011

C11 0.079 -
0.15

C11 0.003 0.009 N5 0.042 0.026 C11 0.005 0.008

C12 0.009 0.006 C12 0.065 0.009 O1 0.058 0.024 C12 0.037 0.017
C13 0.032 0.012 C13 0.012 -

0.002
C13 0.003 0.006

N4 0.052 0.005 O1 0.043 0.093
N5 0.024 0.002 C14 0.025 0.008
O1 0.133 0.03
O2 0.123 0
O3 0.071 0.019
O4 0.854 -

0.761

fitting capabilities (9). The developed QSAR model in equation
14, reproduced the experimental %IE (R2 = 0.9982) with devi-
ation ranging between 0.03 and 0.06 while the standard error

of residuals is 0.116212. Table 3 compares the predicted cor-
rosion inhibition efficiency (% IE) of molecules 1 through 6 to
their experimental %IE. Figure 5 depicts a graph of experimen-

9



Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 10

Figure 4. Generated Pearson’s matrix

Table 3. Experimental and Predicted inhibition efficiencies of thiosemicarbazide derivatives
Molecules Experimental inhibition efficiency Predicted inhibition efficiency Residual
TSC 1 27.4000 27.4352 -0.0352155
TSC 2 94.3000 94.2488 0.0512029
TSC 3 92.8000 92.7691 0.0308704
TSC 4 60.8000 60.8311 -0.0311049
TSC 5 95.5000 95.5642 -0.0642269
TSC 6 27.0000 26.9515 0.0484739

tal corrosion inhibition efficiency (% IE) vs predicted corro-
sion inhibition efficiencies (% 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 fol-
lows: TSC2 > TSC6 > TSC3 > TSC5> TSC4 > TSC1.

The model was also statistically validated using the squared
fitting factor (R2), adjusted fitting factor (R2a), cross-validation
(CV. R2), and variation ratio (F). The estimated R2 (0.9982)
revealed a reasonable fitness as well as the model’s efficiency, as
shown in equation 14. The calculated CV.R2 (0.9342) is greater

than 0.5 (standard) while the R2a (0.9143) is greater than 0.6.
These results show that the model is statistically reliable and
acceptable and have strong external predictability.

4. Conclusion

The quantum chemical descriptors of thiosemicarbazide deriva-
tives were investigated in order to elucidate their electronic struc-
ture, reactivity, and predict their potential for corrosion inhibi-
tion using a computational approach. Through DFT/B3LYP/6-
311G* computational method, a relationship between quantum

10



Ogunyemi & Ojo / J. Nig. Soc. Phys. Sci. 5 (2023) 915 11

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

descriptors of six thiosemicarbazide derivatives and their effec-
tiveness in preventing corrosion was established. The corre-
lations were found to be effective in developing thiosemicar-
bazide inhibitors with appropriate substituents capable of do-
nating electrons to the metal’s surface. Because of their high
EHOMO, ∆N, and low ∆E values, TSC2, TSC3 and TSC5 are
projected to have the best inhibition efficiency, allowing for ef-
ficient electron transfer and hence a higher performance as cor-
rosion inhibitors. This theoretical work has an excellent cor-
relation with experimental corrosion inhibition efficiency, in-
dicating that the approach used in this study is reliable. The
additional NO2 and CN functional groups present in TSC3 and
TSC4 respectively may give insight into the preferred moieties
to look for when designing corrosion inhibitors while the cor-
relations and model developed might aid in the design of novel
thiosemicarbazide inhibitors with appropriate substituents ca-
pable of donating electrons to a metal’s surface.

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