Fuel 357 (2024) 129654
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Fuel
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Full Length Article
Tribological characterisation of graphene hybrid nanolubricants in
biofuel engines
Ching-Ming Lai a, Heoy Geok How b, Yeoh Jun Jie Jason b, Yew Heng Teoh c, *, Haseeb Yaqoob d,
Shengfu Zhang e, f, Mohammad Rafe Hatshan g, Farooq Sher h, *
a
Department of Electrical Engineering, National Chung Hsing University (NCHU), 145 Xing Da Road, South District, Taichung 402, Taiwan
Department of Engineering, School of Engineering, Computing and Built Environment, UOW Malaysia KDU Penang University College, 32, Jalan Anson, 10400
Georgetown, Penang, Malaysia
c
School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Seri Ampangan, Nibong Tebal, Pulau Pinang 14300, Malaysia
d
Mechanical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
e
College of Materials Science & Engineering, Chongqing University, Chongqing 400044, China
f
Chongqing Key Laboratory of Vanadium-Titanium Metallurgy & Advanced Materials, Chongqing University, Chongqing 400044, China
g
Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
h
Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, United Kingdom
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Biofuels
Response surface methodology
Graphene nanoplatelets
Tribology
Friction
Renewable energy
The rapid development of nanotechnology allows further enhancement of the tribological performance of lu­
bricants by utilizing nano-additives. This study used a four-ball tribometer to examine the tribological properties
of three different oils, namely 15 W-40 mineral oil, 5 W-30 completely synthetic polyalphaolefin with ester oil
and pongamia oil with additional graphene nanoplatelets. The experiment model was constructed using a
mathematical technique known as Response Surface Methodology (RSM), with the experimental design devel­
oped by using Optimal Custom Design. The extent of the influence to which various operating parameters,
including load, speed and concentration of nanoparticles, were assessed by analysis of variance (ANOVA) and
regression analysis. The simulation results are used for optimization purposes to determine the optimum con­
centration of nanoparticles that provides excellent tribological properties. The surface morphology was analysed
using scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy to explore the
mechanisms that improve the tribological performance. The optimum concentration of graphene nanoplatelets
(GNP) was determined to be 0.126 wt%, 0.15 wt%, and 0.096 wt% for mineral oil, synthetic polyalphaolefin with
ester oil and pongamia oil respectively. The optimization of graphene nanoplatelets (GNP) concentration on
mineral oil (MO), synthetic oil (SO) and Pongamia oil (PO) exhibits 5.78, 15.63 and 6.82% friction reduction
respectively and 17.68, 29.46 and 97.32% wear reduction respectively compared to base oils. The dispersion
stability results show that GNP is more stable in MO and SO than PO in the absence of surfactant. Finally, the
improvement on worn surface was observed with optimization of concentration due to the significant polishing
effect of GNP.
1. Introduction
The crucial challenge in automobile engines is improving tribolog­
ical performance to extend the durability of engine components from
friction. The friction phenomenon in the engine causes heat and then
promotes wear and colossal energy losses. At the same time, lubrication
is the most efficient way and process of reducing friction and wear be­
tween the interacting surfaces [1]. So far, the frictional losses in engines
caused the total power to be reduced by 17–19% [2] and the combi­
nation of friction and wear caused a total energy loss of 30% in engines
[3]. Moreover, commercial lubricants are composed of 80–90% hydro­
carbon molecules and 10–20% of additives that impart performance [4].
Those additives can be anti-wear additives, extreme pressure additives,
corrosion and oxidation inhibitors, etc. Therefore, researchers are
searching for an alternative source to replace the environmentally
harmful additives (Zinc dialkyl-dithiophosphate) that contain sulphur,
* Corresponding authors.
E-mail addresses: yewhengteoh@usm.my (Y.H. Teoh), Farooq.Sher@ntu.ac.uk (F. Sher).
https://doi.org/10.1016/j.fuel.2023.129654
Received 19 November 2022; Received in revised form 2 August 2023; Accepted 26 August 2023
Available online 4 September 2023
0016-2361/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C.-M. Lai et al.
Fuel 357 (2024) 129654
Table 1
Comparison of various parameters from bio-based nanolubricants with concentrations.
No.
Base oil
Nanoparticles
Concentration (wt%)
Optimum concentration (wt%)
References
1.
2.
3.
4
5
6
7
8
9
10
Rapeseed oil, soybean oil
Pongamia oil
Palm oil TMP ester
Palm oil TMP ester + Polyalphaolefin (PAO)
Rice bran oil
Palm oil
Coconut oil
Coconut oil, mustard oil
Coconut oil
Coconut oil
Cu, CNTs, Cu/CNTs,Cu/CNTs/PDA
GNPs
TiO2/SiO2
GNPs
TiO2/Graphene
Cu, MoS2
MoS2
CuO
CeO2
Ce/Zr
0.05–0.5
0.01–0.2
0.25–1
0.01–3
0.5, 1
1
0.25–1
0.1–0.4
0.1–0.7
0.5, 1
0.2
0.05
0.75
0.05
1
1
0.53
0.3
0.5
0.62
[27]
[28]
[30]
[51]
[44]
[65]
[14]
[66]
[67]
[31]
Notes:
Cu: Copper nanoparticles, CNTs: carbon nanotubes, PDA: polydopamine, TiO2: Titanium dioxide, SiO2: Silicon dioxide, GNPs: graphene nanoplatelets, MoS2: Mo­
lybdenum disulfide, CuO: copper oxide, CeO2: Cerium (IV) oxide, Ce/Zr: Ceria-zirconia.
chlorine and phosphorus without losing the tribological performance
and eco-friendly additives such as nanoparticles and ionic liquids [5].
In recent years, the rapid progression of nanoparticles as lubricant
additives has been developed in the tribology field. To date, the nano­
particles that have been documented in tribology studies include metals,
metal oxides, metal sulphides, carbon materials, nanocomposites and
rare earth compounds. Most of these studies reported nanoparticles
exhibiting impressive tribological performance [6]. Still, graphene is the
most highlighted of all the nanoparticles due to its unique properties,
including excellent mechanical, physical and electrical properties.
Further, graphene has been used in various applications and is
frequently named a “supermaterial” or “all-in-one material” in the world
of material science [7]. Besides, nanoparticles have a nanometer size,
which allows them to fill in the contact surface to form a tribofilm. They
also function as anti-wear (AW), extreme pressure (EP) additives and
friction modifiers, have excellent thermal stability and can react with
the surface without an induction period during lubrication [8]. Addi­
tionally, graphene nanoparticles have been applied in a variety of fields.
It has been utilised to develop effective electrochemical impedance
sensing platforms; utilising graphene nanoparticles was a simpler and
more environmentally friendly method [9].
The tribological enhancement by nanoparticles is attributed to their
lubrication mechanism [10]: rolling effect [11], protective film forma­
tion [12], mending [13] and polishing [14]. The rolling effect and the
formation of protective films are direct actions of nanoparticles on
lubrication enhancement, whereas mending and polishing are surface
enhancements attributed to nanoparticles [15]. Raina and Anand [16]
investigated the tribological performance of nearly spherical diamonds
as additives in synthetic oil and they reported the enhancement is
associated with the rolling mechanism. Wang et al. [17] reported that a
protective film formed by copper nanoparticles has an energetic effect
on the friction part’s lifetime. The deposition of nanoparticles on the
friction surfaces by compensating for the mass losses is known as
mending [18], while polishing is when nanoparticles polish the friction
surface by reducing the surface roughness and abrasion [15]. Moreover,
four parameters affect the tribological characteristics of nanolubricants,
such as the size and shape of nanoparticles, dispersion stability and the
amount of concentration added, with the latter being the most influen­
tial [19].
Despite advancements in research, most tribological studies have
focused on nanoparticles as additives in commercial lubricants, with
only a few studies devoted to vegetable oils. Still, the investigation into
bio-based nanolubricants is indispensable because vegetable oils are a
promising alternative source that is renewable, non-toxic, biodegrad­
able and has excellent physicochemical properties that fulfil lubricant
requirements [20,21]. However, vegetable oil has some drawbacks such
as poor oxidation stability and low thermal stability [22,23]. Still, those
drawbacks can be overcome by using chemical modification [24].
Furthermore, the use of biolubricants delays the rapid depletion of fossil
fuels and reduces mineral oil disposal, which pollutes the environment
[25,26]. Wang et al. [27] investigated the tribological properties of Cu,
CNTs, Cu/CNTs and Cu/CNTs/PDA as additives in rapeseed oil and
soybean oil. They reported that the addition of nanoparticles improves
tribological properties. The greatest improvement is exhibited by Cu/
CNTs/PDA which forms a low-shear strength tribofilm on substrates and
has a self-lubricating ability.
Furthermore, Jason et al. [28] studied the friction and wear char­
acteristics of the four-ball interaction using graphene nanoplatelets
(GNPs) in Pongamia oil. The test results showed that the COF and WSD
were reduced by 17.5% and 11.96% respectively, utilizing a 0.05 wt%
concentration. Singh et al. [29] studied the tribological properties of
Pongamia oil with different concentrations of TiO2 nanoparticle con­
centrations under variable sliding speed conditions. The result showed
that 0.1% concentration exhibited minimum friction coefficient and
wear among all concentrations (0.1, 0.2 and 0.3%). The wear surface for
0.1% concentration of TiO2 nanoparticles was smoother than neat
pongamia oil. Gulzar et al. [30] investigated the tribological effects of
TiO2/SiO2 nanoparticles (50 nm) on piston ring-cylinder contact and
four-ball interaction in palm TMP ester. The results demonstrated TiO2/
SiO2 nanolubricants improved load carrying capacity, anti-wear, and
anti-friction behaviour. Table 1 shows the previous investigation of
nanoparticles as additives in vegetable oil for lubricant application.
The response surface methodology (RSM) technique is a mathemat­
ical and statistical method that is widely used to solve several technical
challenges. For example, RSM is used to evaluate the optimum con­
centration of nanoparticles as additives in base oil [31], optimize bio­
diesel production [32] and predict the optimum fuel blends for
compression ignition (CI) engines [33]. Besides, RSM can be considered
the most cost-effective and efficient technique for evaluating the effects
of numerous factors and their interactions on single or multiple response
variables [34]. Tan et al. [35] compared the Taguchi and RSM approach
when applying both to waste cooking oil transesterification. The reac­
tion temperature was said to be the parameter that affected biodiesel
output the most, according to both the RSM and Taguchi methods,
however, the RSM approach is more practically useful than the Taguchi
method. The author also concluded that RSM is more accurate in pre­
dicting the nonlinear relationship between process factors and response.
In RSM, several mathematical models are available for data to fit into,
such as linear, two-factor interaction, quadratic, cubic, quartic, fifth and
sixth models. The established equations to define the case study are in
accordance with the selected models. Moreover, Satake et al. [36]
demonstrated approximately a 54% man-hour testing reduction by using
RSM compared to conventional testing.
In summary, the nanoparticles improved the tribological properties
when used as lubricant additives, especially the amount of concentra­
tion added. Therefore, the objective of the present study was to study the
tribological enhancement of graphene-based nanolubricants using RSM.
The four-ball tribometer was performed to study the friction and wear
2
C.-M. Lai et al.
Fuel 357 (2024) 129654
speed, load and concentration of nanoparticles on the maximized
tribological enhancement and predict the optimum concentration of
GNPs in various base oils. Other than the tribological studies, dispersion
analyses for the nanolubricants and surface morphology analyses have
also been performed.
Table 2
Comparison of various properties of base oils.
Base oils
Properties
Units
Methods
MO
SO
PO
Density @ 15 ◦ C
g/cm3
D4052/D1298
0.87
0.85
Kinematic Viscosity @
40 ◦ C
Kinematic Viscosity @
100 ◦ C
Viscosity index
Pour point
Flash point
mm2/s
D442
105
65.05
0.94
(D1298)
38.80
mm2/s
D442
13.90
11.63
–
–
C
◦
C
D2270
D97
D93
130
− 36
203
173
− 45
232
–
− 4
212
◦
2. Experimental details and procedure
2.1. Base oil selection
In this research, 15 W-40 mineral oil (MO), 5 W-30 fully synthetic
PAO + ester oil (SO) and Pongamia oil (PO) were selected as base oils
without modification. MO and SO acted as comparison studies with PO.
PO is non-edible vegetable oil with an annual yield of Pongamia seeds
ranging from 20 to 80 kg per tree. It can be found easily and grown on
marginal terrain, reducing agricultural conflicts [37] and above all, it
does not conflict with the food supply. PO extracted through a coldpressing process was purchased from Ramamoorthy Exports, India.
The properties of the base oils used in this study are represented in
Table 2.
Table 3
Physical properties of nanoparticles.
Properties
Description
Appearance
Form
Relative density (g/cm3)
Bulk density (g/cm3)
Thickness (nm)
Particle size (µm)
Surface area (m2/g)
Black
Powder
2.0–2.25
0.2–0.4
Few
<2
500
2.2. Nanoparticles selection
Since the advent of nanotechnology, the use of nanoparticles as
lubricant additives to achieve superior tribological qualities has drawn a
lot of interest. In this study, graphene nanoplatelets (GNPs) were
selected as an additive to formulate the nano lubricants without modi­
fication. Similar GNPs were also employed by another research study
[38]. These nanoparticles (particle size < 2 µm) were purchased from
Sigma-Aldrich (M) Sdn. Bhd, Malaysia and the properties of GNPs pro­
vided by the supplier are listed in Table 3. The morphology and nano­
structure of GNPs are further verified by authors using scanning electron
microscopes (SEM, HITACHI S-3400N) equipped with energy-dispersive
spectroscopy (EDX, EDAX) and high-resolution transmission electron
microscopy (HRTEM, TECNAI G2 20 S-TWIN).
Table 4
The test conditions of experiments.
Test condition
Unit
Value
Load
Speed
Temperature
Test duration
Concentrations of GNPs
kg
rpm
◦
C
min
wt%
20, 40, 60
900, 1200, 1500
75
60
0.01, 0.05, 0.1, 0.2
properties of formulated nanolubricants. The obtained experimental
results were then evaluated using Design Expert software. The novelty of
this study presenting the RSM model was developed using Optimal
Custom Design to determine the influence of test parameters such as
2.3. Nanolubricant preparation
GNPs will be dispersed in MO, SO and PO at different concentrations
to formulate the associated nanolubricants based on a weight percentage
Fig. 1. Schematic representation of four-ball tribometer.
3
C.-M. Lai et al.
Fuel 357 (2024) 129654
Table 5
Experimental design and results using different bio-oils.
Run order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Factors/Test parameter
15 W-40
Mineral engine oil
5 W-30
Synthetic oil
Pongamia oil
FactorA: Concentration
(wt%)
Factor B: Speed (rpm)
Factor C: Load (kg)
COF
WSD (μm)
COF
WSD (μm)
COF
WSD (μm)
0.2
0.2
0.1
0.2
0.01
0.1
0.05
0.2
0.01
0.1
0.1
0.01
0.2
0.01
0.01
0.05
0.2
0.1
0.2
0.1
900
1200
1500
1500
1200
1200
900
900
1500
1200
1500
900
900
1500
1200
900
1500
1200
1200
1500
20
20
40
40
40
20
20
60
60
60
40
60
40
20
40
20
60
60
40
40
0.076
0.050
0.048
0.044
0.045
0.068
0.063
0.046
0.054
0.046
0.045
0.049
0.053
0.047
0.045
0.060
0.039
0.044
0.052
0.050
531.9
547.3
554.3
800.7
768.5
559.9
561.3
540.3
827.2
573.5
503.9
842.6
664.9
533.3
768.5
564.8
579.5
564.1
722.8
508.1
0.057
0.057
0.059
0.067
0.064
0.058
0.07
0.077
0.061
0.060
0.060
0.073
0.074
0.061
0.064
0.073
0.062
0.064
0.057
0.060
389.1
586.5
694.3
754.5
590.2
530.5
557.1
935.0
762.9
701.3
727.9
676.1
722.3
628.5
590.2
543.1
755.9
760.1
527.7
704.1
0.040
0.050
0.033
0.038
0.051
0.043
0.036
0.063
0.048
0.053
0.032
0.045
0.044
0.032
0.051
0.031
0.049
0.056
0.056
0.039
487.1
601.9
530.5
961.6
922.4
394.7
506.7
1196.7
1189.7
1111.3
492.7
999.4
506.7
1561.5
904.2
459.1
1631.9
1047.0
1028.8
594.2
Table 6
ANOVA analysis table for COF of nanolubricants.
Source
Sum of square
Oil type
Model
A- Conc.
MO
0.0013
1.664e07
0.0002
SO
0.0006
0.0000
PO
0.0015
0.0001
MO
9
1
SO
9
1
PO
9
1
0.0003
0.0001
1
1
0.0003
0.0001
0.0001
0.0001
0.0001
0.0000
0.0001
0.0000
1
1
1
1
A2
0.0001
B2
C2
0.0000
0.0000
9.025e06
0.0001
0.0000
0.0006
0.0000
0.0000
3.001e07
0.0001
0.0006
0.0000
B-
Speed
C- Load
AB
AC
BC
Degree of
freedom
Mean square
F-value
p-value
SO
0.0001
0.0000
PO
0.0002
0.0001
MO
5.11
0.0588
SO
5.42
1.72
PO
14.87
6.19
MO
0.0089
0.8133
SO
0.0072
0.2187
PO
0.0001
0.0321
1
MO
0.0001
1.664e07
0.0002
0.0003
0.0001
6.01
25.82
4.57
0.0341
0.0005
0.0582
1
1
1
1
1
1
1
1
0.0003
0.0001
0.0001
0.0001
0.0001
0.0000
0.0001
0.0000
12.22
2.41
4.01
3.31
4.82
1.42
5.16
1.00
50.76
3.89
1.76
0.0266
0.0058
0.1513
0.0730
0.0990
0.0528
0.2615
0.0465
0.3398
<0.0001
0.0768
0.2137
0.8736
1
1
1
0.0001
1.90
0.7038
8.38
0.1984
0.4211
0.0160
1
1
1
1
1
1
0.0000
0.0000
9.025e06
0.0001
0.0000
0.0006
0.0000
0.0000
3.001e07
0.0001
0.0006
0.0000
0.9422
1.15
8.72
1.12
51.96
1.27
0.3546
0.3096
0.0145
0.3153
<0.0001
0.2867
2.4. Design of experiment
Table 7a
Modelling statistics for COF of nanolubricants.
The tribological studies were performed using RSM and set up with a
Randomized Optimal Custom Design (ROCD) with 20 runs for each base
oil type. The concentration of nanoparticles (wt%), speed (rpm) and
load (kg) are the three factors, while two responses, coefficient of fric­
tion (COF) and wear scar diameter (WSD) are considered for this study.
The test conditions of the present study are listed in Table 4. The
response variables COF and WSD will be obtained through the experi­
ment for statistical accuracy and to construct an equation for the
response model. The experimental test was performed under the ASTM
standards D4172 [41]. Besides, the results will be analysed using
ANOVA. Through these 20 runs, the optimized concentration of GNPs
for the lubricant will be predicted by Design Expert software with the
desirability function by selecting the desired target for each factor and
response within the data range. Moreover, the experiment test will be
conducted to prove the actual value of the results from the prediction.
Regression analysis on COF
Oil type
MO
SO
PO
R-square, R2
Adjusted R2
Adequate precision
0.8213
0.6604
8.8671
0.8299
0.6768
8.5525
0.9305
0.8679
10.8607
(wt%) basis, such as 0.01, 0.05, 0.1 and 0.2 wt%. Corresponding
nanolubricants were prepared on the day of tests and 50 mL of the base
oils were used to prepare each nanolubricants. Furthermore, the use of
surfactants in nanolubricants will be excluded because it limits the
catalytic activities against the formation of tribofilm on the surface [39].
Then, the agitation procedure is done by using a TYFSF SH-4C digital hot
plate with a magnetic stirrer and this formulation method was proposed
by previous researchers for stable suspension [40]. The agitation pro­
cedure will be completed at a hot plate temperature of 60 ◦ C for 30 min
and the agitation speed is between 350 and 500 rpm.
2.5. Tribology experimental test
The anti-wear properties of base oils and nanolubricants were
investigated using a DUCOM TR30L-LAS four-ball tribometer (Ducom
4
C.-M. Lai et al.
Fuel 357 (2024) 129654
Fig. 2. Influence of concentration and speed on COF for (a) MO (b) SO and (c) PO.
Instruments, India). Fig. 1 shows the schematic diagram of the four-ball
tribometer. The experimental test was performed under the ASTM
standards D4172 and carried out by following the run order designed by
the Design Expert [41]. Therefore, the tests will not be repeated. The
diameter of 12.7 mm steel ball made of AISI 52100 alloy steel with G25
and 64–66 HRC hardness are used in the tests. The steel balls and ball
pot surfaces were thoroughly cleaned with acetone before and after each
test. After the test, the stationary balls were taken out from the ball pot
for WSD measurement by using Eq. (1) an image acquisition system
(optical microscope), which is connected to the computer equipped with
image capture software to measure the captured image of the wear scar.
The frictional torque was obtained after the test utilizing Winducom
2010 software, and the COF was calculated using Eq. (1). Where W is
applied load (kg), T is frictional torque (kg/mm) and r is the distance
between the centre of the contact surfaces of the lowers ball and the
rotation axis is 3.67 mm.
√̅̅̅
T× 6
Coefficient of friction (μ) =
(1)
3 × W ×r
During observation, a photograph of the sample will be taken once there
are any changes in appearance. Besides, any relocation or disturbance of
the sample will be avoided to ensure the analysis is done well.
2.7. Surface morphology analysis
The surface morphology of the tested steel balls was analysed using
Hitachi S-3400 N and Hitachi S-3700 N scanning electron microscopes
equipped with energy-dispersive spectroscopy (EDX), and a 15 kV
electron-accelerating voltage was adopted. Besides, EDX analysis was
conducted to determine the elements existing on the worn surfaces.
Tests comply with International Standards ASTM E1508-12a (Standard
Guide for Quantitative Analysis by Energy-Dispersive Spectroscopy).
The company (Quasi) performed all the SEM/ EDX analyses. A method
for assessing the size of wear particles in test oils was employed. After
the tests, the oil underwent paper filtration, enabling metal debris
collection for SEM/EDX analysis. The analysis revealed the presence of
Fe, F, C and O at various locations on the filter paper, each exhibiting
distinct element weight percentages [42].
2.6. Dispersion stability investigation
The dispersion ability of nanolubricants was evaluated by an
observation stability test. Only observation is involved in determining
the dispersion ability of nanolubricants in this observation stability test.
5
C.-M. Lai et al.
Fuel 357 (2024) 129654
Fig. 3. Influence of concentration and load on COF for (a) MO (b) SO and (c) PO.
F-value for the MO, SO and PO is 5.11, 5.42 and 14.87 respectively.
Therefore, the model F-value implies the model is significant, and the pvalue is lesser than 0.05. As a result, the MO model has a 0.89% (p-value
= 0.0089), the SO model has a 0.72% (p-value = 0.0072) and the PO
model has a 0.01% (p-value = 0.0001), which may occur noise. Further
examination of the F value and p-value shows that factor C (load) has the
most significant effect on COF of MO, followed by factor B (speed). For
COF of SO, factor B (speed) has the most significant effect. Besides, it is
worth mentioning that factor A (Cncentration) is less significant in COF
of MO and SO, but this can be concerned with excluding the COF base
oils in this statistical method and the existing additives in the base oils of
MO and SO. For PO, factor C (load) has the most significant effect on
COF, followed by factor A and then factor B.
Tests on statistical accuracy are essential to ensure the predictive
strength of the model. Therefore, Table 7(a) represents the modelling
statistics of the coefficient of friction for all types of nanolubricants. The
R2 value represents how much of the total variation in the response
(COF) can be explained by independent variables (Concentration, speed
and load). The MO series test sample on COF is 82.13% (0.8213 ×
100%), the SO series test sample on COF is 82.99% (0.8299 × 100%),
and the PO series test sample on COF is 93.05% (0.9305 × 100%) which
independent variables can explain and they mean very big. This in­
dicates that the entire relationships among the various experimental
factors for the model are adequately represented. Adjusted R2 is a
variant of R2 that takes into consideration factors in a regression model
Table 7b
Modelling statistics for WSD of nanolubricants.
Regression Analysis on wear scar diameter
Oil type
MO
SO
PO
R-square, R2
Adjusted R2
Adequate precision
0.8323
0.6814
8.2797
0.8071
0.6335
7.6024
0.9315
0.8698
13.7870
3. Results and discussion
3.1. Tribological study
The tribo tests were carried out using the four-ball tribometer
following the design matrix (varying input parameters) designed by
Design-Expert software and the obtained corresponding results are
shown in Table 5 in the supplementary section and were analysed using
ANOVA.
3.1.1. ANOVA analysis for coefficient of friction
Table 6 shows the ANOVA analysis for the COF results against the
mineral oil, synthetic oil and Pongamia oil-based-nano lubricants. It
summarises each independent variable (concentration, speed and load)
influences and their interactions fit into a quadratic model for COF.
Besides, the model is developed with a 95% confidence level. The model
6
C.-M. Lai et al.
Fuel 357 (2024) 129654
Fig. 4. Influence of concentration and speed on WSD for (a) MO (b) SO and (c) PO.
that are not significant. When the new term enhances the model more
than would be predicted by chance, the adjusted R2 rises. When a pre­
dictor enhances the model by less than anticipated, it falls. For PO
model, the adjusted R2 was higher than MO and SO model which in­
dicates that the independent variable is adding more value to the model
as compared to MO and SO model. Adequate precision measures the
signal-to-noise ratio (S/N ratio) and compares the range of the predicted
design values with the average prediction error. If the ratio of adequate
precision is greater than 4, it indicates adequate model discrimination.
The adequate precision value is 8.8671, 8.5525 and 10.8607 respec­
tively for MO, SO and PO as shown in Table 7. Thus, the model is
desirable and has a signal sufficient to be used for optimization. Influ­
ence of concentration and speed on COF for MO, SO and PO.
Fig. 2 shows the 3D surface plot as a response to the concentration
and speed on COF at 40 kg. It can be observed the effect of concentration
is not very significant for MO-series and SO-series nanolubricants as
shown in Fig. 2 (a) and Fig. 2 (b), even though there is a slight increment
of COF value at 0.1 wt% concentration and then declined at 0.2 wt%
concentration under 1500 rpm for MO-series nanolubricants. The least
increment of COF by increasing the concentration of GNPs in SO-series
nanolubricants. For the effect of speed for MO-series nanolubricants, the
increase in speed resulted in COF decreasing at 0.2 wt% concentration.
Hence, a friction reduction of 1.9% and 17% have been observed for
speeds under 1200 rpm and 1500 rpm compared to 900 rpm respec­
tively. Besides, increasing the speed leads to a decrease in friction, which
has also been observed on SO-series nanolubricants for all concentra­
tions. This can be related to the real contact area decreasing when speed
increases due to the thermal activation on the friction surface, resulting
in COF declination [43]. Fig. 2(c) shows the effects of concentration and
speed on COF for PO-series nanolubricants. It can be observed that the
increase in the concentration, increased the friction, while there was a
rapid increase in COF from the speed of 900 to 1200 rpm and then
declined from 1200 to 1500 rpm. It is expected the sliding process is at
the boundary lubrication regime. At low sliding speed, unstable and thin
film is formed on the rubbing surfaces, which causes high friction [44].
In contrast, friction declined at high sliding speed, and it still can be
attributed to the thermal activation on the friction surfaces mentioned
before.
3.1.1.1. Influence of concentration and load on COF for MO, SO and PO.
Fig. 3 shows the 3D surface plot as a response to concentration and load
on COF at 1200 rpm. The effect of concentration and load on COF for
MO-series nanolubricants has similar trends to the effect of concentra­
tion and speed, as shown in Fig. 3(a). The COF decreased as the load
increased, and there was a friction reduction of 30.26% and 39.47% at
40 kg and 60 kg loads respectively, compared to a 20 kg load at 0.2%
concentration under 900 rpm. This trend can be suggested when load
increases, which causes surface frictional heating and forms a molten
layer on the surface asperities, thus reducing friction [45]. Besides, this
is also be attributed to the presence of GNPs and their mechanism, which
7
C.-M. Lai et al.
0.0001
0.2676
0.0005
0.0004
0.9966
0.0011
0.3494
<0.0001
0.0992
0.0011
0.0124
0.3449
0.3289
0.0009
0.5711
0.1723
0.1327
0.5078
0.1060
0.8316
SO
MO
0.0067
0.0132
0.5659
0.0361
0.0763
0.0713
0.6186
0.0011
0.5501
0.0458
15.11
1.38
25.17
27.71
0.000
20.57
0.9638
41.28
3.30
20.48
4.65
0.9826
1.05
21.96
0.3430
2.16
2.68
0.4717
3.16
0.0477
5.51
9.03
0.3525
5.86
3.91
4.07
0.2639
20.16
0.3825
5.20
2.710e + 05
24735.77
4.515e + 05
4.971e + 05
0.3441
3.691e + 05
17292.22
7.407e + 05
59253.45
3.675e + 05
24732.57
5227.77
5603.96
1.168e + 05
1824.66
11499.51
14250.62
2509.34
16791.93
253.52
23618.68
38694.19
1509.89
25083.81
16730.21
17434.88
1130.39
88280.41
1638.36
22265.37
9
1
1
1
1
1
1
1
1
1
9
1
1
1
1
1
1
1
1
1
9
1
1
1
1
1
1
1
1
1
MO
PO
SO
PO
Sum of square
MO
2.126e + 05
38694.19
1509.89
25083.81
16730.21
17434.88
1130.39
88280.41
1638.36
22265.37
Model
A- Conc.
B- Speed
C- Load
AB
AC
BC
A2
B2
C2
SO
2.439e + 06
24745.77
4.515e + 05
4.971e + 05
0.3441
3.691e + 05
17292.22
7.407e + 05
59253.45
3.675e + 05
p-value
PO
SO
MO
F-value
PO
8
Oil Type
3.1.2. ANOVA analysis for wear scar diameter
In tribology studies, the wear resistance on the rubbing surface is also
a critical parameter. The wear scar diameter is analysed by adopting the
average value of wear scar diameter on three rubbing steel balls after the
four-ball tribo-test. The ANOVA analysis of the WSD in this study adopts
the same design procedure and settings that analyse for the COF anal­
ysis. The ANOVA analysis summary for WSD is tabulated in Table 8,
including the correlation of independent variables, F and p-value. The
model is developed with a 95% confidence level and the model is sig­
nificant. An intriguing prediction shows that factor C (load) is the
Source
Table 8
ANOVA analysis table for WSD of the nanolubricants.
Degree of freedom
form a stable tribofilm, reducing the shear strength at the contact surface
and resulting in friction reduction and enhancing the load capacity. In
contrast, a different trend has been observed for SO and PO-series
nanolubricants as shown in Fig. 3(b) and Fig. 3(c). For SO-series
nanolubricants, COF was increased by increasing the load in the range
of 20 to 60 kg for all concentrations. At low load, there was a slight
decrement of COF with the increase in concentration, but at high load,
there was an occurrence of a slight increment of COF when concentra­
tion increased. For PO-series nanolubricants, the uptrend in COF was
displayed by increasing the loads. Besides, it can be seen that the COF is
minimum at around 0.1% concentration and then elevated at 0.2%
concentration. There was an increment of 10% and 57.5% friction when
the load increased from 20 to 40 kg and 60 kg at 0.2 wt% concentration
and under 900 rpm respectively. As the results show, the COF increases
gradually as the applied load increases for SO and PO-series nano­
lubricants. This is because contact pressure increases as load increases,
which causes the gaps between contact surfaces to become smaller and
the lubrication is inadequate at the interface, thus the friction increase
[46].
MO
Mean square
SO
Fig. 5. Influence of concentration and speed on WSD for (a) MO (b) SO and
(c) PO.
2.226e + 05
5227.77
5603.96
1.168e + 05
1824.66
11499.51
14250.62
2509.34
16791.93
253.52
PO
Fuel 357 (2024) 129654
C.-M. Lai et al.
Fuel 357 (2024) 129654
Table 9a
Model validation of MO.
Concentration (wt%)
0.126
Coefficient of friction
Wear scar diameter (μm)
Desirability
Predicted
Experimental
Error (%)
Predicted
Experimental
Error (%)
0.050
0.052
3.85
583.2
634.1
8.03
0.858
Table 9b
Model validation of SO.
Concentration (wt%)
0.15
Coefficient of friction
Wear scar diameter (μm)
Desirability
Predicted
Experimental
Error (%)
Predicted
Experimental
Error
(%)
0.061
0.064
4.69
633.6
579.5
9.34
0.817
Table 9c
Model validation of PO.
Concentration (wt%)
0.096
Coefficient of friction
Wear scar diameter (μm)
Desirability
Predicted
Experimental
Error (%)
Predicted
Experimental
Error (%)
0.048
0.044
9.09
471.4
471.7
0.06
prominence factor on the wear scar diameter lubricated by all types of
nanolubricants. This is because a higher load will have a high flash
temperature and increase WSD [47]. Factor A (concentration) is a sig­
nificant factor in MO. In contrast, concentration has a less significant
effect on SO and PO. This may be due to the increase in concentrations
doesn’t affect much on WSD. Besides, factor B (speed) is not significant
for MO and SO, but it is significant for the PO. This is because the
increased speed doesn’t result in a massive variation of WSD for MO and
SO. Still, the ANOVA analysis is limited to these 20 runs. The R2 value of
the designed model for the wear scar diameter is 0.8323, 0.8071, and
0.9315 for MO, SO and PO respectively as shown in Table 7(b). These R2
values are entirely satisfactory because at least 80% can be explained by
the independent variable. Moreover, the adequate precision for MO, SO
and PO is 8.2797, 7.6024 and 13.7870 respectively. This evidence shows
that the model is desirable and has a sufficient signal for optimization.
0.82
addition, the minimum WSD can be observed in the range between 0.09
wt% and 0.12 wt%, which can be suggested in the range of these con­
centrations, low WSD could be achieved at a speed between 900 and
1500 rpm.
3.1.2.2. Influence of concentration and load on WSD for MO, SO and PO.
Fig. 5 shows the 3D surface plot with the variation of concentration and
load on WSD at 1200 rpm. For MO-series nanolubricants, minimum
WSD has been found at concentrations between 0.1 wt% and 0.15 wt%
(Fig. 5(a)) and it shows similar results that are mentioned in the effect of
speed and concentration. The variation of WSD with loads for SO-series
nanolubricants shows a linear change by achieving the highest WSD at
high loads, as shown in Fig. 5(b). Moreover, it can be observed that there
are obvious changes in WSD at high loads when concentration increases,
but in other load conditions, there are insignificant changes. Fig. 5(c)
demonstrates the variation of WSD with concentration and loads for POseries nanolurbricants. It can be seen that the increase in loads will result
in WSD rising for all concentrations. This is because load increases cause
the flash temperature to increase and lead to failure of shearing the
surface asperity, thus the wear increases [47]. At 0.2 wt% concentra­
tions and under 900 rpm, there was an increase of 4.02% and 145.68%
WSD when the applied load was at 40 kg and 60 kg compared to 20 kg
respectively. For the effect of concentration, it can be observed that
there is a similar trend at low load and high load on WSD. The lowest
WSD has been found between 0.09 wt% and 0.12 wt% concentration.
The further increment of concentrations resulted in a rapid increase of
WSD up to 1600 μm (approximately) at 60 kg loads, while at 20 kg loads,
there was just a slight increment of WSD.
3.1.2.1. Influence of concentration and speed on WSD for MO, SO, and
PO. Fig. 4 shows the 3D surface plot as a response of concentration and
speed on WSD at 40 kg load. It can be observed that the WSD is mini­
mum between the concentration of 0.1 to 0.15 wt% for MO-series
nanolubricants at all speeds, as shown in Fig. 4(a). This can be attrib­
uted to the presence of GNPs in a sufficient amount which is able to
minimize the wear. At concentration before 0.1 wt%, it depicts WSD
downtrends and can be identified as the result of the presence of GNPs.
For the effect of speed, it can be seen that there is not much increment of
WSD from 900 to 1500 rpm at 0.2 wt% concentration and the WSD value
is in the range of 600 to 700 μm. For SO-series nanolubricants, it can be
observed the effect of speed and concentration shows insignificant ef­
fects on the wear resistance since there are not many changes in WSD,
which matches the ANOVA analysis, as shown in Fig. 4(b). Fig. 4(c)
illustrates the variation of WSD with the concentration and speed for POseries nanolubricants. It can be observed that there is a huge variation in
WSD (increase trends) with the change in speed from 900 to 1500 rpm.
This trend can be attributed to the increase in rotational speed. Thus, the
sliding speed upon the contact surfaces increases, which causes the
contact to traverse longer distances [48] and the WSD to become bigger.
Moreover, Prabhu [49] reported that wear loss increases with the in­
crease in sliding speed caused by the frictional temperature rise. In
3.1.3. Optimization of graphene nanoplatelets concentration in base oils
The coefficient of friction and wear resistance are the two major
characteristics of tribology and the analysis of these responses is detailed
above. As to the cost-effectiveness, efficiency and settlement trend of
nanoparticles in lubricants, an optimum concentration estimate is un­
avoidable for favourable responses. Thus, the numerical optimization
has been constructed and carried out in Design Expert software with
desirability. The optimization procedure has been conducted by
following the parameters of the ASTM D4172 standard. These
9
C.-M. Lai et al.
Fuel 357 (2024) 129654
Fig. 6. Comparison of the optimised concentration in base oils against bare oils; (a) Coefficient of friction and (b) Wear scar diameter.
parameters have been selected for optimization because this standard
can determine lubricants’ relative anti-wear properties in sliding contact
under prescribed test conditions [41]. Besides, the test condition of
75 ◦ C is nearest to the internal combustion engine’s average operating
temperature, which is also a reason for selecting this standard as an
optimization procedure.
The optimization of the concentration of GNPs for MO adapted from
20 test runs is shown in Table 9(a). The estimation of the optimum
concentration of GNPs for MO is 0.126 wt% (Desirability: 0.858) and the
predicted COF and WSD are 0.05 and 583.2 μm respectively, while the
experimental results of COF and WSD are 0.052 and 634.1 μm, respec­
tively. The actual value was higher than the predicted value and the
error differences between the predicted. The actual values were 3.85%
(COF) and 8.03% (WSD). Table 9(b) shows the estimation of the opti­
mum concentration of GNPs for SO is 0.15 wt% (Desirability: 0.817).
The predicted values for COF and WSD are 0.061 and 633.6 μm. In
contrast, the actual values of COF and WSD obtained through the
experiment are 0.064 and 579.5 μm respectively. The error differences
between those values are 4.69% and 9.34% respectively. The estimated
optimum concentration of PO is 0.096 wt% (Desirability: 0.814), as
stated in Table 9(c). The predicted results for COF and WSD in PO are
0.048 and 471.4 μm, while the actual results are 0.044 and 471.7 μm.
The errors in percentages for COF and WSD are 9.09% and 0.06% for PO
respectively. Overall, the experimental results are slightly higher than
predicted results and the percentage error values for all lubricants are
within 10%. Thus, these acceptable error values clearly demonstrate the
model’s robustness in terms of the parameters mentioned above.
Additionally, the comparison of the COF and WSD of base oils against
the optimum concentration suggested by Design Expert software with
satisfactory desirability, is shown in Fig. 6. It can be observed that the
optimum concentration of GNPs that are added to base oil improves the
tribological characteristics of base oils. As a result, the addition of 0.126,
0.15 and 0.096 wt% in MO, SO and PO respectively, exhibits 5.78, 15.63
and 6.82% friction reduction and 17.68, 29.46 and 97.32% wear
reduction compared to base oils. The price for graphene nanoplaletes
from Sigma Aldrich will be around US$0.705/g in 2022. Additionally,
according to researchers, adding 0.2 mg/L graphene to base oil as an
addition will only cost US$0.0005, making graphene one of the least
expensive additives on the market [50]. Costs are expected to drop even
more sharply when bulk manufacturing methods for graphene become
more effective.
3.2. Investigating dispersion stability of nanolubricants
The dispersion stability of nanoparticles is strongly desirable for
reliable lubrication performance. Thus, for the consideration of
10
Fuel 357 (2024) 129654
C.-M. Lai et al.
Fig. 7. Observation of the suspension of GNPs at optimum concentrations in various base oils over a period of time; (a) PO with 0.096% GNPs, (b) MO with 0.126 wt
% GNPs and (c) SO with 0.15 wt%.
Fig. 8. SEM micrographs of steel balls worn in various base oils and optimised nanolubricants. TPO; (a) MO (2 k×), (b) MO + 0.126% GNP (2 k×), (c) SO (2 k×), (d)
SO + 0.15% GNP (2 k×), (e) PO (2 k×) and (f) PO + 0.096% GNP (2 k×).
nanolubricants for long-term applications without losing their tribo­
logical improvement ability, the stable suspension of nanolubricants is
essential. Fig. 7 shows the observation stability test on the suspension of
GNPs in nanolubricants to investigate the stability of GNPs in terms of
the period in the nanolubricants after magnetic stirring. For the addition
of GNPs in PO, it can be seen that the sedimentation of GNPs occurs on
day 7 with obvious appearance changes. Further increasing the period,
the solutions were fully sedimented on day 20. A similar finding has
been reported by previous studies[51], which examined the dispersion
stability of GNPs concentration in the range of 0.01 to 3 wt% in TMP +
PAO and reported the nanolubricants with 3 wt% GNPs fully precipi­
tated first, followed by 0.01 wt% and the rest between 19 and 21 days.
For the addition of GNPs in MO and SO, the strong stability of GNPs in
Table 10
Element details of worn surfaces through EDX analysis.
Lubricants
MO
MO + 0.126
SO
SO + 0.15
PO
PO + 0.096
Element
(wt%)
Fe
F
O
C
84.00
79.79
84.52
90.92
96.22
96.23
0.00
0.00
0.00
0.00
3.78
0.00
7.52
6.08
9.31
4.13
0.00
0.00
8.47
14.13
6.17
4.94
0.00
3.77
11
C.-M. Lai et al.
Fuel 357 (2024) 129654
Fig. 9. Schematic diagram of the lubrication mechanism.
the nanolubricants can be observed through the photograph of the
observation stability test. Until day 120, the appearance of MO and SO
based nanolubricants showed insignificant changes. However, there was
slight sedimentation that could be seen. Additionally, the dispersion
stability of GNPs in PO is poor compared to MO and SO. This can be
attributed to the MO and SO containing dispersants, which improved the
dispersion stability and can also be related to the density of base oils
according to Stoke’s law (Sedimentation rate) [52]. Under the current
scenario of increasing fossil fuels emissions [53–55], there is an urgent
need to develop and apply eco-friendly technologies for environmental
pollution reduce [56,57].
to the base oil containing the undefined carbon-based additive package,
surface adhesion during the sliding process and there was an absence of
carbon on the worn surface lubricated with PO, as it is pure oil. The
highest carbon content (14.13 wt%) was found for MO + 0.126. It is
worth mentioning that the carbon content is not only attributed to the
deposition of GNPs since there is insignificant surface enhancement
compared to the MO. Besides, it can be related to the oil composition
assisting in surface adhesion [58], the element composition of steel balls
and oil film carbonization [59]. Furthermore, the worn surface lubri­
cated by SO + 0.015 and PO + 0.096 had a carbon content of 4.94 wt%
and 3.77 wt% respectively. This confirmed that the GNPs were deposited
or absorbed on the friction surface. The most convincing evidence is the
surface morphology shown by the SEM micrographs of SO + 0.15 and
PO + 0.096 compared to SO and PO respectively. Lubrication mecha­
nism of GNPs nanoparticles.
According to the findings of this study, the addition of GNPs as ad­
ditives in various base oils that improved the tribological properties is
attributed to the formation of the protective film between the rubbing
surface and the surface enhancement (polishing effect), as illustrated in
Fig. 9 [60,61]. Furthermore, Zhang et al. [62] reported that the primary
tribological mechanism of graphene oxide (GO) is attributed to the GO
being absorbed on the friction surface and forming the tribochemical
film. The tribo-film effectively reduces the shear strength of the rubbing
surface and leads to tribological improvement. Wang et al. [60]
demonstrated the deposition and absorption of graphene on the rubbing
surface reduced surface roughness and partially avoided the surface
directly rubbing, resulting in friction and wear reduction. The sugges­
tion from the previous studies is further confirmed in this study by EDX
analysis, with the presence of GNPs elements (C) on the worn surfaces
lubricated by nanolubricants. This evidences the deposition and ab­
sorption of GNPs on the surface, which results in tribological enhance­
ment. Besides, the lubrication mechanism proposed in this study is
consistent with the findings of Wu et al. [63] and Hou et al. [64].
3.3. SEM/EDX analysis
Fig. 8 shows the worn surface lubricated by base oils and nano­
lubricants that are enriched-optimum concentrations of GNPs. The worn
surfaces lubricated by MO + 0.126 showed delamination, cracks and
pitting and didn’t show any appreciable change compared to the worn
surface lubricated by bare MO. However, there was no appreciable
improvement in surface enhancement with the addition of 0.126 wt%
GNPs, but there was a reduction in terms of friction and wear. Besides,
compared to the worn surface after being lubricated by SO, the worn
surface was enhanced with the addition of 0.15 wt% GNPs in SO. The
severe surface defects such as the occurrence of cracks, pitting, and
delamination that were seen from SO were replaced by light abrasion
and cracks. This can be attributed to the presence of GNPs, which form a
protective film on the surface asperities and avoid direct surface rub­
bing. Thus, the surface was enhanced and became smoother compared
to SO. Deep grooves and abrasion were observed on the micrograph of
PO, while with the addition of optimum concentration, several surface
defects have been cured.
However, there was remaining light wear and light grooves, which is
evidence that GNPs provided surface enhancement. Therefore, this in­
dicates that GNPs play an important role in improving tribological
characteristics. The deposition of nanoparticles on the worn surface was
further investigated by EDX analysis. Table 10 illustrates the worn
surface’s elemental weight percentages that correspond to the SEM
micrographs. Iron is the main element of AISI 52100 steel balls, which
contributed the highest value in the analysis and the main element of
GNPs, carbon, was detected in the analysis. The detection of oxygen is
related to the formation of oxide layers during the sliding process.
Carbon was detected on MO and SO worn surfaces; this can be attributed
4. Conclusions
The tribological effect of GNPs in different base oils were investi­
gated in a four-ball tribometer. Based on the results presented above, the
following conclusions are provided as follows:
• ANOVA and regression analysis has established the model’s credi­
bility developed by RSM using ROCD. In addition, RSM is a useful
12
C.-M. Lai et al.
•
•
•
•
•
Fuel 357 (2024) 129654
technique to evaluate the significant factors and optimize the oper­
ating parameters.
From the optimization results, the optimized concentration of GNPs
nanoparticles for the lowest COF and WSD determined using desir­
ability function were 0.126, 0.15, and 0.096 wt% for MO, SO and PO
respectively.
The error percentage of the model validation for optimization was
less than 10%, which clearly demonstrates the model’s robustness.
Besides, the optimized concentration in MO, SO, and PO exhibits
5.78, 15.63 and 6.82% friction reduction and 17.68, 29.46 and
97.32% wear reduction respectively as compared to base oils.
It is noteworthy to report the dispersion stability of nanolubricants
for the consideration of long-term applications. The obvious
appearance changes of the PO-based nanolubricants happened after
7 days and were fully sedimented after 20 days. In contrast, stable
dispersion was observed for MO and SO-based nanolubricants, which
showed insignificant appearance changes after 120 days. Therefore,
a suggestion such as modifying the nanoparticles or base oils is
suggested to further improve the dispersion stability.
Surface analyses via SEM and EDX confirmed the lubrication mech­
anism of GNPs is attributed to the tribo-film formation and followed
by the polishing effect.
A possible extension of the current investigation to optimize GNPs in
other biodegradable oil can further establish the use of GNPs-based
nanolubricants.
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CRediT authorship contribution statement
Ching-Ming Lai: Conceptualization, Methodology, Software, Data
curation. Heoy Geok How: Visualization, Investigation, Project
adminstration, Funding acquisition, Software, Validation, Writing – re­
view & editing. Yeoh Jun Jie Jason: Methodology, Software, Data
curation, Writing – original draft. Yew Heng Teoh: Conceptualization,
Visualization, Resources, Supervision, Validation, Writing – review &
editing. Haseeb Yaqoob: Data curation, Software, Writing – review &
editing. Shengfu Zhang: Software, Data curation, Writing – original
draft. Farooq Sher: Funding acquisition, Resources, Supervision,
Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgement
This study was supported by Grant number MOST-112-2622-8-005001-TE1, the Ministry of Higher Education (MOHE) of Malaysia and
UOW Malaysia KDU Penang University College through Fundamental
Research Grant Scheme (FRGS Grant: FRGS/1/2019/TK03/KDUPG/03/
1) and Universiti Sains Malaysia Research University (RUI) Grant
Scheme, 1001. PMEKANIK.8014136. The authors are thankful for the
financial support from the International Society of Engineering Science
and Technology (ISEST) UK. The authors are also grateful to the Re­
searchers Supporting Project number (RSP2023R222), King Saud Uni­
versity, Riyadh, Saudi Arabia, for the financial support.
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