# Molecularly imprinted electrochemical sensor for fast and direct determination of caffeic acid in wine

Marie Elhachem, Philippe Cayot, Adrien Lerbret, Igor Bezverkhyy, Thomas Karbowiak, Maher Abboud, Richard Maroun, Elias Bou-Maroun

<figure><img src="image_1.png" /></figure>

## To cite this version:

Marie Elhachem, Philippe Cayot, Adrien Lerbret, Igor Bezverkhyy, Thomas Karbowiak, et al., Molecularly imprinted electrochemical sensor for fast and direct determination of caffeic acid in wine. Microchemical Journal, 2025, 217, pp.114924. 10.1016/j.microc.2025.114924. hal-05244386

HAL Id: hal-05244386

https://institut-agro-dijon.hal.science/hal-05244386v1

Submitted on 8 Sep 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Molecularly imprinted electrochemical sensor for fast and direct determination of caffeic acid in wine

Marie Elhachem<sup>a,b</sup>, Philippe Cayot<sup>a</sup>, Adrien Lerbret<sup>a</sup>, Igor Bezverkhyy<sup>c</sup>, Thomas Karbowiak<sup>a</sup>, Maher Abboud<sup>d</sup>, Richard G. Maroun<sup>b</sup>, Elias Bou-Maroun<sup>a,*</sup>

## ARTICLE INFO

### Keywords:

Electropolymerization

MIP-sensor

Differential pulse voltammetry

Molecular simulation

Wine

Screen printed electrode

## ABSTRACT

Caffeic acid (CA) is widely distributed in the plant kingdom. As an antioxidant, it helps protect food and provides health benefits. Therefore, the determination of this compound in many food and beverage matrices is of particular interest. This study aimed to develop an electrochemical sensor based on a molecularly imprinted polymer (MIP) as a sensitive material for the quantitative determination of caffeic acid in wine. The functional monomer was selected based on molecular simulation. The MIP was polymerized on the surface of a screen-printed carbon electrode (SPCE) using 4-aminobenzoic acid (4-ABA) as a functional monomer and CA as a template. The electropolymerization was performed by cyclic voltammetry (CV) and was optimised to enhance the sensor's selectivity. CA detection by differential pulse voltammetry (DPV) showed a linear response between 0 and 40 µM (R² = 0.9992), with a limit of detection of 0.68 µM (approximatively 1 mg·L⁻¹). The MIP sensor demonstrated selectivity for CA over other interferents, as well as good repeatability and stability. It was successfully applied to wine samples, and its response was compared to that of the HPLC-UV quantification method. Beyond its application in the complex wine matrix, this new MIP-SPCE sensor could also be used to detect CA in various other food matrices.

## 1. Introduction

Caffeic acid (CA) is a naturally occurring phenolic compound extensively found in various food matrices, including fruits, vegetables, and beverages like coffee and wine. Known for its potent antioxidant properties, caffeic acid plays a crucial role in protecting cells from oxidative damage, thus contributing to the prevention of various chronic diseases [1]. In wine, caffeic acid not only influences the organoleptic properties but also enhances the wine's health benefits, making its accurate quantification in such matrices highly important for both quality control and nutritional evaluation [2,3].

Classical chromatographic methods, such as high-performance liquid chromatography (HPLC) and gas chromatography (GC), have long been the gold standard for the quantification of antioxidants like caffeic acid [4,5,6]. Despite their high sensitivity and specificity, these methods often involve complex sample preparation, long analysis times, and the need for expensive and sophisticated instrumentation. Furthermore, the

presence of various interfering substances in wine can hinder the accurate quantification of caffeic acid, highlighting the need for more efficient analytical techniques.

Electrochemical sensors offer a promising alternative for the determination of phenolic compounds due to their high sensitivity, rapid response, and cost-effectiveness [7,8]. These sensors can be miniaturized and are suitable for on-site analysis, making them particularly advantageous for real-time monitoring in diverse environments. However, one of the major challenges associated with electrochemical sensors is their limited selectivity, particularly in complex matrices like wine, where numerous phenolic compounds coexist and may interfere with caffeic acid detection.

Analytical and electroanalytical methods play a crucial role in the detection and quantification of various compounds in biological and environmental matrices. Recent studies highlight the growing importance of electrochemical sensors in diverse analytical applications, from biomedical diagnostics to environmental monitoring and energy

<footnote><sup>a</sup> UMR PAM, Institut Agro, Université Bourgogne Europe, INRAE, F-21000 Dijon, France</footnote>

<footnote><sup>b</sup> Centre d'Analyses et de Recherche, Laboratoire CTA, UR TVA, Faculty of Sciences, Saint Joseph University, Beirut 1104 2020, Lebanon</footnote>

<footnote><sup>c</sup> Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université Bourgogne Europe, 9 Avenue Alain Savary, BP 47870, 21078 Dijon, Cedex, France</footnote>

<footnote><sup>d</sup> UEGP Unité Environnement, Génomique et Protéomique, Faculty of Sciences, Saint Joseph University, BP 17-5208 Mar Mikhael, Beirut 1104 2020, Lebanon</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: elias.bou-maroun@institut-agro.fr (E. Bou-Maroun).</footnote>
conversion [9,10,11,12,13,14]. These advancements underline the
critical role of electrochemical methods in modern analytical science,
offering cost-effective and high-performance solutions for real-world
challenges.

To overcome selectivity issue, molecularly imprinted polymers (MIPs) have emerged as a powerful strategy [15]. MIPs are synthetic materials designed with molecular recognition sites that are highly specific to the target molecule, in this case, to caffeic acid. The principle of MIPs relies on the formation of polymer networks with cavities that are complementary in shape, size, and functional groups to the target molecule. This is typically achieved by polymerizing monomers in the presence of the target molecule, which serves as a template. After polymerization, the template is removed, leaving behind specific binding sites that can selectively rebind the target molecule. Incorporating MIPs into electrochemical sensors can significantly enhance their selectivity and sensitivity [16]. Screen-printed carbon electrodes (SPCEs) serve as an excellent support for MIPs due to their small sample volume requirements, cost-effectiveness, and ease of use. SPCEs also facilitate the integration of MIPs into portable and user-friendly sensor platforms.

Several methods can be used to deposit MIPs onto the surface of screen-printed electrodes, including electropolymerization, drop casting, and chemical grafting [17]. Electropolymerization stands out among these techniques due to its simplicity, ability to form a thin and uniform polymer layer, and the covalent attachment of the functional monomer to the electrode surface. This method not only ensures strong polymer adhesion but also enables precise control over the film thickness and morphology [18]. Additionally, electropolymerization is highly compatible with commercial-scale production, making it an attractive option for developing market-ready sensor devices. Several studies have been conducted in order to develop electrochemical sensor based on the electropolymerization of a MIP on the surface of a glassy carbon electrode [19,20,21,22].

Previous studies have reported the development of electrochemical sensors for the detection of caffeic acid in wine [23,24,25,26,27,28,29,8,30]. Traditional electrochemical sensors for caffeic acid detection often suffer from limited selectivity due to interference with structurally similar phenolic compounds present in complex matrices like wine. Many reported sensors rely on nanomaterial modifications to enhance sensitivity; however, these approaches can face drawbacks such as high fabrication costs the use of conventional electrodes, and susceptibility to matrix effects. To overcome these drawbacks, authors have used the standard addition method and sometimes have modified the wine pH.

To the best of our knowledge, this work presents, for the first time,
the integration of MIPs onto SPCEs via electropolymerization as a
promising approach for the selective and sensitive determination of
caffeic acid in complex matrices such as wine. This approach combines
the high selectivity of MIPs with the practicality and cost-effectiveness
of SPCEs, paving the way for the development of robust and reliable
analytical tools for the direct monitoring of CA in wine.

In this work, molecular simulation was employed to select the most
suitable functional monomer among nine candidates. This strategy is
similar to the one followed by the group of Delerue-Matos, who used
Density Functional Theory (DFT) calculations and molecular dynamics
(MD) simulations to predict the optimal functional monomers and syn-
thesis conditions for the design of MIPs [31–34]. The chosen monomer
was then electropolymerized onto the surface of a SPCE using CV in the
presence of CA, which acted as the template molecule. Optimization of
the electropolymerization parameters, including the number of cycles
and the template-to-functional monomer (T/FM) molar ratio, was sub-
sequently conducted to enhance the performance of the MIP. The
rebinding time of CA by the MIP-SPCE was also optimised to ensure
efficient and rapid detection. Additionally, the thickness of the sensor
layer was estimated to understand its influence on the sensor's perfor-
mance. A calibration curve was established using the newly developed

sensor to determine its sensitivity and detection limits. The effect of
potential interfering compounds commonly found in wine was investi-
gated to assess the sensor's selectivity. The practical applicability of the
sensor was demonstrated by testing it on real wine samples, and its
signal was compared to CA quantification results obtained using a
standard HPLC-UV method. Finally, the repeatability and stability of the
sensor were evaluated to confirm its reliability for routine analysis. The
main advantage of this work lies in the simplicity of the MIP deposition
method via electropolymerization and the use of disposable SPCE.
However, the primary drawback is the time-consuming process required
for the development and optimization of the sensor.

## 2. Material and methods

2.1. Reagents

Caffeic acid (≥ 98 %, CAS number 331–39-5), 4-aminobenzoic acid (≥ 99 %, CAS number 150–13-0), *p*-coumaric acid (≥ 98 %, CAS number 501–98-4), gallic acid (97.5–102.5 % titration, CAS number 149–91-7), trans-ferulic acid (≥ 99 %, CAS number 537–98-4), sinapic acid (≥ 98 %, powder, 530–59-6), phosphate buffered saline (PBS tablets), ethanol (EtOH, absolute, CAS number 64–17-5), and acetic acid (glacial, CAS number 64–19-7) were purchased from Sigma Aldrich, France. Water used in all experiments was deionized and obtained from an Elga water purification system with PURELAB option.

2.2. Instrumentation

All the voltammetric measurements were carried out using a
portable µStat-i MultiX potentiostat (Metrohm France). The electrodes
used were Screen Printed Carbon Electrodes (SPCE, DropSens, DRP-110)
with a carbon working electrode (d = 4.0 mm) and silver reference
electrode. The analyses were performed using cyclic voltammetry for
polymerization and differential pulse voltammetry for detection. The
measured intensity (anodic peak intensity, I<sub>pa</sub>) is proportional to the
concentration of the detected compound in the medium.

## 2.3. Molecular simulation

The choice of the functional monomer (FM) was made using mo-
lecular dynamics (MD) simulation. Nine monomers, 4-aminobenzoic
acid (4-ABA), aniline (ANL), *o*-phenylenediamine (OPD), carbazole
(CBZ), mercaptobenzimidazole (MBI), *m*-phenylenediamine (MPD),
phenol (PHE), pyrrole (PYR) and thiophene (TPH) were studied as po-
tential functional monomers for CA-MIP film formation. These 9 func-
tional monomers were previously selected based on an examination of
their structure, suggesting that they were likely to interact favourably,
non-covalently, with caffeic acid. To conduct this study, the program
GROMACS [35,36] was used with the OPLS-AA force field parameters
[37,38] to represent caffeic acid, functional monomers, and ethanol.
These parameters were automatically generated by the LigParGen server
[39], available at http://zarbi.chem.yale.edu/ligpargen/. No further
optimization of these parameters was performed. Water molecules were
represented by the TIP4P/2005 model [40]. The LINCS algorithm [41]
was used to fix the length of all bonds involving a hydrogen atom and the
equations of motion were integrated with the Verlet leapfrog algorithm
using a time step of 2 fs.

Periodic boundary conditions were applied, and cubic simulation
boxes were used. Van der Waals interactions were force-switched to zero
between 10 and 12 Å and Lorentz-Berthelot mixing rules were used for
cross-interaction terms. The short-range electrostatic interactions were
cut off at 12 Å and long-range electrostatic interactions were handled by
the Particle Mesh Ewald (PME) method with a 4th-order spline inter-
polation and the fast-Fourier grid spacing was set to 1 Å [42]. The Verlet
neighbour list scheme was used for short-range interactions. Long range
dispersion corrections for energy and pressure were applied.
An ethanol/water mixture (10/90, v/v) was used as the solvent for the simulations of pre-polymerization mixtures, which were prepared with the PACKMOL program [43] (https://m3g.github.io/packmol/). Each mixture consisted of 1 molecule of caffeic acid, 4 molecules of functional monomer, 113 molecules of ethanol, and 3327 molecules of water in a cubic simulation box with an initial side length of 48 Å, which corresponds to a caffeic acid concentration of approximately 15 mM. For each mixture, 3 independent random configurations were generated.

The energy of these initial configurations was minimized using the steepest descent algorithm as follows: (i) three successive short mini-
mizations (20 steps) with high harmonic restraints on all the atoms of
caffeic acid, monomers, and ethanol molecules (force constant of 10⁶ kJ·
mol⁻¹·nm⁻²); (ii) then, two successive minimizations of 2000 steps with
harmonic restraints on the heavy atoms of caffeic acid, monomers, and
ethanol molecules (force constants of 10⁶ kJ·mol⁻¹·nm⁻² and of 10³ kJ·
mol⁻¹·nm⁻², respectively); (iii) minimization with no harmonic re-
straints for 10,000 steps, with a force tolerance of 1 kJ·mol⁻¹·nm⁻¹. A
200 ps molecular dynamics simulation in the canonical ensemble (N, V,
T) was then performed to heat the system from 10 K to 298 K. Tem-
perature was controlled with the Nose-Hoover chain thermostat with a
time constant for coupling τ<sub>T</sub> of 0.5 ps [44]. Finally, a 200 ns molecular
dynamics simulation in the isothermal-isobaric ensemble (N, p, T) with
T = 298 K and p = 1 atm was performed and atomic coordinates were
saved every 2 ps. Pressure was controlled with the Parrinello-Rahman
pressure coupling scheme, with a time constant for coupling τ<sub>P</sub> of 2.5
ps and a compressibility of 4.5 10<sup>-5</sup>·bar<sup>-1</sup> [45,46].

The interactions between caffeic acid and the functional monomers were analysed over the second half of each simulation (t = [100 ns–200 ns]), by determining the average values of the following three parameters: (i) the number of functional monomers in contact with caffeic acid (coordination number), (ii) the number of hydrogen bonds formed between caffeic acid and functional monomers, and (iii) the interaction energy between caffeic acid and functional monomers.

A given functional monomer was considered to belong to the first coordination shell of caffeic acid if at least one of its heavy atom was within 5 Å of a heavy atom of caffeic acid (this cut-off distance corresponds approximately to the position of the first minimum of the probability distribution function of the minimal distance between monomers and caffeic acid). Moreover, a hydrogen bond between caffeic acid and a functional monomer was considered to exist if (i) the donor-acceptor distance was less than 3.5 Å and (ii) the H-donor-acceptor angle was lower than 30° [47]. Finally, the interaction energy between caffeic acid and functional monomers was calculated as the sum of the short-range van der Waals and electrostatic energies using the energy module of Gromacs and the same cutoff distance (12 Å) as the one employed to perform the MD simulations. Caffeic acid and functional monomers were defined as two groups and the interaction energy between these groups was computed with the energygrps keyword.

2.4. MIP sensor development

Under the optimised conditions, the electropolymerization of the MIP-sensor was performed on the SPCE surface using cyclic voltammetry (CV) in the potential range from –0.2 to 1 V, at a scan rate of 100 mV. s⁻¹ for 10 consecutive cycles. A 50 µL drop of the pre-polymerization mixture, consisting of 0.05 mM caffeic acid and 0.5 mM 4-ABA as a functional monomer in a 0.05 M PBS (pH 3)/ethanol (90/10, v/v) solution, served as the polymerization medium. pH 3 was selected based on the distribution of species of caffeic acid as a function of pH. At pH 3, caffeic acid is mainly in its protonated form, which enhances hydrogen bond interactions with the functional monomer. Moreover, pH 3 is close to the pH of real wines. A non-imprinted polymer (NIP) electrode was prepared using the same experimental conditions, but without adding CA into the polymerization solution. The electrodes were then rinsed with ultrapure water and immersed in a washing solution of ethanol/ acetic acid (90/10, v/v) for 10 min to remove the CA, followed by

immersion in ethanol for an additional 10 min. The electrodes were air-
dried before applying 50 µL of 0.05 M PBS (pH 3) onto the surface of the
MIP-SPCE. Differential Pulse Voltammetry (DPV) was then performed
over a potential range of 0.1 to 0.8 V, E<sub>step</sub>: 0.004 V, E<sub>pulse</sub>: 0.08 V, t<sub>pulse</sub>:
200 ms and S<sub>rate</sub>: 0.04 V for the detection. After the removal of CA during
the washing step, the SPCE was immersed for 20 min in a CA solution
(2–40 µM) prepared in 0.05 M PBS (pH 3)/ethanol (90/10, v/v). The
SPCE was then rinsed, air dried, and reconnected to the potentiostat
with 50 µL of PBS on its surface. DPV was applied to evaluate CA
rebinding.

Several key parameters influencing the performance of the MIP
sensor were optimised, including the template-to-functional monomer
molar ratio, the number of cycles during electropolymerization, and the
rebinding time.

2.5. XPS characterization

The X-ray photoelectron spectra (XPS) were recorded on PHI 5000 Versaprobe apparatus using Al Kα radiation. The powdered samples were pressed onto a sheet of indium. The XPS spectra were analysed using CasaXPS software [48]. The QUASES software, based on TPP-2 M equation, was used to calculate the mean free path of the photoelectrons in the deposited polymer film [49]. The length of the 4-aminobenzoic acid (4-ABA) molecule was estimated as a distance between the H atoms of -NH₂ and -CO₂H groups in the crystalline PABA using the Mercury software (CCDC code of the crystalline PABA is AMBNAC04).

2.6. Interference study

The performance of the MIP-sensor was evaluated in the presence of interfering compounds with chemical structures similar to that of caffeic acid: gallic acid (GA), trans-ferulic acid (FA), p-coumaric acid (p-CA), and sinapic acid (SA) by preparing 30 µM solutions of each compound separately and using them as incubation solutions for the sensor. Rebinding tests were conducted as described in section 2.4.

2.7. Application of the sensor in real wine sample

Five samples of a red Burgundy wine (Pinot noir, 2017) were pre-
pared. The wine was diluted 10-fold and spiked with five concentrations
of caffeic acid, ranging from 11 to 167 µM. The MIP-sensor was used as
described previously (see rebinding procedure in Section 2.4.), and the
same samples were analysed using HPLC-UV to compare the results and
validate the sensor’s response.

2.8. HPLC-UV quantification of CA in wine samples

The quantification of caffeic acid in diluted wine samples was per-
formed using a Shimadzu HPLC system (LC-20 AT) pump equipped with
a UV-Visible detector (SPD-20 A). The chromatographic separation was
achieved on a C18 column (150 mm × 4.6 mm, 5 µm particle size;
Pinacle II from Restek). The mobile phase consisted of a mixture of
solvent A (water with 0.1 % acetic acid) and solvent B (acetonitrile with
0.1 % acetic acid). The gradient elution program was as follows: initial
conditions (92.5 % A and 7.5 % B), 0–8 min: linear gradient to (5 % A
and 95 % B), 8–11 min: (5 % A and 95 % B), 11–13 min: linear gradient
to (92.5 % A and 7.5 % B), 13–15 min: re-equilibration (92.5 % A and
7.5 % B). The flow rate was set to 1.0 mL.min⁻¹, and the injection
volume was 20 µL. The detection wavelength was set to 325 nm, which
corresponds to the maximum absorbance of caffeic acid. The calibration
curve, constructed with eight standards ranging from 2 to 200 mg.L⁻¹,
was linear (R² = 0.9999).
# 3. Results and discussion

## 3.1. Choice of the functional monomer based on molecular simulation

The selection of a suitable functional monomer with favourable interactions with the template prior to polymerization can lead to a better imprinting effect. For this purpose, molecular simulation was employed as a valuable pre-screening strategy to select the monomer with the highest affinity for the template, based on coordination number (CN), total interaction energy ($E_{int}$, kJ·mol⁻¹) and number of hydrogen bonds (nHB) (Table 1).

These results indicate that 4-ABA is expected to exhibit the highest affinity for CA, as it shows the largest coordination number (CN = 0.51 ± 0.06), the most favourable interaction energy ($E_{int}$ = -9.0 ± 1.4 kJ·mol⁻¹), and the highest number of hydrogen bonds (nHB = 0.031 ± 0.007). This enhanced interaction with CA may be attributed to the presence of a carboxylic and an amine group on the benzene ring of 4-ABA, which may form hydrogen bonds with the carboxylic and the hydroxyl groups of CA, in addition to the π-π stacking interactions between the benzene rings of 4-ABA and CA. An example of a 4-ABA-CA complex sampled in MD simulations is shown in Fig. S1 in the Supplementary Material.

Based on these theoretical results, 4-ABA should present the highest selectivity for the CA template. Therefore, this monomer was chosen for the molecular imprinting of CA.

## 3.2. Electropolymerization of 4-ABA at the surface of the SPCE and design of the sensor

The fabrication of the MIP electrochemical sensor involves three main steps, as presented in Scheme 1. The first step is the electropolymerization of 4-ABA monomer through 10 cycles of CV, in the presence of the CA template. The second step consists of solvent extraction in an acidic medium to remove the template and free the imprinted cavities. The third step is the selective rebinding of CA from a complex medium, such as wine. The electropolymerization of 4-ABA has been previously described in the literature, which suggests polymerization of the monomer in acidic medium via the amine group at the surface of a graphite-based electrode [50]. In this case, the carboxyl groups of 4-ABA will interact specifically with the COOH functions of CA. Also π-π interactions between the functional monomer and the template cannot be ruled out. Fig. 1A and 1B represent the CV signals of the MIP-sensor and the corresponding NIP at the end of the polymerization, just before the template removal. The CA peak at 0.45 V is prominent in the first polymerization cycles of the MIP-sensor, while the 4-ABA oxidation peak is recorded at 0.37 V. An increase of the oxidation

**Table 1**

Coordination number (CN), total interaction energy ($E_{int}$) and number of hydrogen bonds (nHB) between CA and monomers. A given monomer was considered to belong to the coordination shell of caffeic acid if at least one pair of heavy atoms from the monomer and caffeic acid was within a cutoff distance, $d_{cutoff}$, of 5 Å. A hydrogen bond between caffeic acid and a functional monomer was considered to exist if (i) the donor-acceptor distance was less than 3.5 Å and (ii) the H-donor-acceptor angle was lower than 30° [47]. The reported values are averages and standard deviations determined from three independent simulations.

<table><thead><tr><th>Complex</th><th>CN</th><th>E<sub>int</sub> (kJ·mol<sup>-1</sup>)</th><th>nHB</th></tr></thead><tbody><tr><td>CA-ABA</td><td>0.51 ± 0.06</td><td>-9.0 ± 1.4</td><td>0.031 ± 0.007</td></tr><tr><td>CA-ANL</td><td>0.18 ± 0.02</td><td>-2.1 ± 0.2</td><td>0.013 ± 0.003</td></tr><tr><td>CA-CBZ</td><td>0.33 ± 0.10</td><td>-5.6 ± 2.2</td><td>0.008 ± 0.001</td></tr><tr><td>CA-MBI</td><td>0.41 ± 0.17</td><td>-7.5 ± 4.0</td><td>0.015 ± 0.002</td></tr><tr><td>CA-MPD</td><td>0.20 ± 0.01</td><td>-2.7 ± 0.3</td><td>0.027 ± 0.006</td></tr><tr><td>CA-OPD</td><td>0.18 ± 0.02</td><td>-2.4 ± 0.3</td><td>0.020 ± 0.005</td></tr><tr><td>CA-PHE</td><td>0.22 ± 0.04</td><td>-2.5 ± 0.7</td><td>0.006 ± 0.001</td></tr><tr><td>CA-PYR</td><td>0.09 ± 0.01</td><td>-0.7 ± 0.2</td><td>0.004 ± 0.001</td></tr><tr><td>CA-TPH</td><td>0.17 ± 0.02</td><td>-1.6 ± 0.2</td><td>0.000 ± 0.000</td></tr></tbody></table>

current indicates film growth on the surface of the electrode.

The oxidation mechanism of CA is presented in the following reaction:

The curve (a) in Fig. 1C shows the differential pulse voltammograms (DPV) of a PBS solution on MIP-SPCE before the removal of the template and directly after electropolymerization. The curve (b) in Fig. 1C shows the differential pulse voltammograms (DPV) of a PBS solution on MIP-SPCE after the removal of the template. No signal was observed, indicating the successful removal of the template molecules using a washing solution of ethanol/acetic acid (90/10, v/v). The curves c and d in Fig. 1C correspond to the DPV of a PBS solution after rebinding of a 0.5 mM CA solution for 20 min on the surface of MIP-SPCE and NIP-SPCE respectively. The oxidation peak of CA is clearly identified and is higher for MIP-SPCE than for NIP-SPCE. This indicates that the molecular imprinting of CA was successful, enabling its direct determination without the need for an electrochemical probe, as CA is inherently electrochemically active.

## 3.3. Optimisation of the electropolymerization parameters

After demonstrating the feasibility of an electrochemical sensor for CA determination through the electropolymerization of 4-ABA, optimization was performed on the template-to-functional monomer molar ratio, the number of CV cycles, and the CA rebinding time. Fig. 2 shows the results of the optimised parameters of the electropolymerization after rebinding of a 50 µM CA solution. Several template-to-functional monomer molar ratios were tested, and it was observed that the electrochemical performance of the prepared MIP is highly dependent on this parameter. Fig. 2A shows that the CA peak current increased to 9.865 µA for (1/10; T/FM) ratio and decreased with higher concentrations of functional monomer. The ratio 1/10 was retained for the following experiments.

The main advantage of electropolymerization is that the polymer thickness can be controlled by adjusting the number of potential cycles. Different CV cycles were applied, ranging from 5 to 40. As shown in Fig. 2B, the highest peak current intensity (26.7 µA) of the MIP-sensor was obtained with 10 cycles, followed by a continuous decrease. This decrease can be attributed to an increase in the thickness of the polymer, which hinders the removal of the imprinted CA molecules located near the electrode surface. Based on Fig. 2B, 10 electropolymerization cycles were considered for the subsequent tests.

The rebinding time refers to the duration for which the MIP-sensor is immersed in the CA rebinding solution before DPV evaluation. Rebinding times ranging from 1 to 60 min were tested. The highest current was observed after 20 min of immersion, after which the response reached a plateau (Fig. 2C). Therefore, 20 min of rebinding were chosen for the subsequent tests.

## 3.4. Estimation of the polymer film thickness

According to the XPS data, the SPCE surface layer contains carbon, oxygen, and chlorine (see table S1 and fig. S2 of the Supplementary material section). The C1s spectrum can be represented as a sum of four contributions corresponding to different types of carbon atoms. The first contribution (BE = 284.6 eV) can be attributed to the aromatic carbon, the two peaks at higher energies (285.6 and 286.6 eV) correspond to carbon atoms bonded to oxygen and/or chlorine atoms [51]. The broad peak located at 288 eV is due to a plasmon peak systematically observed in aromatic systems. The O1s peak is a sum of two contributions, which are due to different oxygen-containing surface groups present in the support. The Cl2p spectrum corresponds to two doublets (2p3/2 and 2p1/2 levels) whose positions (2p3/2 BE = 200.1 and 201.2 eV) are characteristic of the chlorine atoms bonded to carbon atoms (Justin [52]).

After the deposition of the polymer on the support, nitrogen appears in the spectrum (2.8 at.%) and the content of other elements varies. The appearance of nitrogen with a binding energy (BE) of 400.0 eV is typical
<figure><img src="image_4.png" /><figcaption>**Scheme 1.** Principle of electropolymerization and fabrication of the SPCE-MIP.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** A. Electropolymerization of MIP. B. Electropolymerization of NIP. 10 CV cycles were applied from –0.2 to 1 V (scan rate: 100 mV/s) on the surface of a SPCE. The polymerization mixture contained [CA] = 0.05 mM and [4-ABA] = 0.5 mM in a 0.05 M PBS (pH 3)/ethanol (90/10, v/v) solution. C. DPV measurements: (a) 0.05 M PBS (pH 3) on the MIP-SPCE before the removal of CA template, within the potential range of 0.1 to 0.8 V; (b) 0.05 M PBS (pH 3) on the MIP-SPCE after the removal of CA template, within the same potential range; (c) 0.05 M PBS (pH 3) on the MIP-SPCE after rebinding of 0.5 mM CA for 20 min, within the same potential range; (d) 0.05 M PBS (pH 3) on NIP-SPCE after rebinding of 0.5 mM CA for 20 min, within the same potential range.</figcaption></figure>

for the amine group (Justin [52]) and the increase of the oxygen content reflects the presence of these atoms in the deposited polymer film. It can also be noticed that the chlorine content decreases after polymer deposition. This effect can be explained by absorption of a fraction of the emitted Cl2p photoelectrons by the deposited polymer layer.

The observed attenuation of the Cl 2p signal allows for the estimation of the thickness (t) of the deposited polymer film. To do this the following equation was used [53]:
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 2. Optimization of the electropolymerization parameters of the MIP-SPCE. All measurements were performed by DPV after rebinding of a 50 µM CA solution: (A) Peak current as a function of the ratio of the template (T) to functional monomer (FM) molar concentrations, with the rebinding time set to 20 min, and the number of electropolymerization cycles was set to 10; (B) Peak current as a function of the number of polymerization cycles, the T/M ratio was set to 1/10, and the rebinding time was set to 20 min; (C) Peak current as a function of the rebinding time, the T/M ratio was set to 1/10, the number of electropolymerization cycles was set to 10, the line is a guide for the eyes. Experiments were performed in triplicate.</figcaption></figure>

$$t = - \lambda \cos \alpha \ln \left( \frac{I(t)}{I_0} \right) \qquad (1)$$

where λ is the mean free path of the electrons in the layer, α is the angle between the vertical and the detector axis (45° for our apparatus), I(t) is the signal intensity for the sample with the layer and I₀ is the signal intensity without the layer. The mean free path of the electrons was calculated using QUASES software. For this calculation one needs the number of valence electrons in the molecule and the layer density. If the typical density of aromatic polymers (1.3 g.cm⁻³) is used as a film density, the value of 36 Å for λ is obtained. By inputting the intensities of Cl2p peaks and the other parameters into eq. (1), the polymer film thickness was estimated to be 8.2 Å. It should be noted that, given the approximations used, the thickness is an estimate rather than an exact value.

A second approach, based on Faraday's Law, was used to estimate the polymer thickness. This law states that the amount of substance deposited during electrolysis is proportional to the total charge passed through the electrolyte [54]:

$$Q = n.F.N \qquad (2)$$

where Q is the total charge in Coulomb, n is the number of electrons involved in the electrochemical reaction, F is Faraday's constant (96,485C.mol⁻¹), and N is the number of moles of the deposited substance. Eq. (2) allows for the calculation of N, considering that the total charge was determined experimentally during the electropolymerization (35.413 µC), and that the number of electrons was 2.

Next, the polymer volume was estimated using the following equation:

$$V = \frac{N.MW}{\rho} \qquad (3)$$

where MW is the molecular weight of the monomer (137.14 g.mol⁻¹) and ρ is the polymer density. The density was estimated based on the average density of aromatic polymers (polystyrene, poly-ethylene terephthalate and polyamides) at 1.3 g.cm⁻³.

Finally, the polymer thickness t was calculated based on the following equation:

$$t = \frac{V}{A} \qquad (4)$$

where A is the surface area of the SPCE, determined by the supplier to be 0.11 cm². The calculated thickness based on Faraday's Law is 17.6 Å, which is about two times higher than that obtained from XPS spectra. However, it should be noted that the methods used are quite different, and given the approximations, the agreement between the obtained values can be considered fairly good.

The comparison of the estimated thickness values with the length of the 4-ABA molecule obtained from its crystalline structure (7.7 Å) suggests that the resulting film consists of one or two molecular layers, assuming a vertical orientation on the surface. However, it cannot not be excluded that the polymer film has a more complex structure. For example, it could consist of multiple layers of 4-ABA molecules oriented horizontally on the surface.
Considering the very low thickness of the deposited 4-ABA film, characterizations performed using scanning electron microscopy, X-ray diffraction and atomic force microscopy did not show significant differences between the electrodes before and after the film deposition.

### 3.5. Determination of caffeic acid using the MIP-SPCE sensor

Under the optimised experimental conditions described above, CA determination at various concentrations was evaluated using DPV. After washing and template extraction, the sensors were immersed in CA solutions of varying concentrations, prepared in 0.05 M PBS (pH 3)/ ethanol (90/10, v/v) for 20 min. Fig. 3A illustrates the DPV responses of the MIP sensor at various concentration of CA in the range between 2 and 40 µM. Fig. 3 B shows a linear relationship (R² = 0.9992) between the oxidation peak current (Iₚₐ) and the CA concentration. Iₚₐ values were obtained from DPV of the MIP-sensor. The MIP-sensor generated a signal approximately five times higher than that of the NIP, indicating its specificity for caffeic acid. The limits of detection (LOD) and quantification (LOQ), calculated using the equations LOD = 3 s/m and LOQ = 10s/m, where “s” is the standard deviation of the intercept and “m” is the slope of the calibration curve, were determined to be 0.68 and 2.05 µM, respectively.

The standard deviation of the intercept was calculated using the following equation:

<figure><img src="image_8.png" /><figcaption>Fig. 3. (A) DPV responses of MIP-SPCE, under the optimised conditions (T/FM molar ratio of 1/10, 10 electropolymerization cycles, 20 min rebinding time), after the rebinding of various concentration of CA ranging from 2 to 40 µM and prepared in 0.05 M PBS (pH 3)/ethanol (90/10, v/v). DPV parameters: from 0.1 to 0.8 V, Estep = 0.004 V, Epulse = 0.08 V, tpulse = 200 ms, and Srate = 0.04 V. (B) Peak current from DPV on MIP-SPCE and NIP-SPCE after rebinding of CA from 2 to 40 µM. Experiments were performed in triplicate.</figcaption></figure>

$$s = \frac{s_y}{\sqrt{n}} \sqrt{\frac{1}{n} + \left( \frac{x_{mean}^2}{\sum (x_i - x_{mean})^2} \right)}$$

where:

s_y is the standard deviation of the residual errors from the regression.

n is the number of data points. Data from the 3 regression lines were considered.

x mean is the mean of the x-values (concentrations) of the calibration curves.

x_i represents each individual x-value in the calibration data.

In addition, the performance of the MIP-sensor was compared with other electroanalytical methods for caffeic acid detection in wine samples (Table 2). To the best of our knowledge, this is the first time a carbon screen-printed electrode has been functionalized with a MIP using electropolymerization to create a highly sensitive sensor for the determination of caffeic acid. The LOD obtained in this study is comparable to that reported by Leite et al. [26] (0.15 µM). However, these authors used a conventional electrode, which is more expensive and more difficult to functionalise and use.

### 3.6. Interference study

The performance of the MIP-sensor was evaluated in the presence of interferents. Four structurally similar compounds: GA, FA, SA, and p-CA, were selected, and their solutions were prepared at a concentration of 30 µM. Both MIP and NIP were used to analyse the solutions of each individual compound, including CA. The results, presented in Fig. 4, are expressed as the percentage of rebinding, calculated from the peak current of the MIP or the NIP-sensor and divided by the initial concentration of each antioxidant. As expected, the response of the MIP-sensor

<table><caption>**Table 2**
Comparison of the present MIP-sensor with other electroanalytical methods for caffeic acid detection in wine samples.</caption><thead><tr><th>Modified electrode</th><th>Technique</th><th>Linear range (µM)</th><th>Limit of detection (µM)</th><th>References</th></tr></thead><tbody><tr><td>RGO/PDA</td><td>DPV</td><td>0.05-450</td><td>0.001</td><td>[28]</td></tr><tr><td>Au/MIS</td><td>DPV</td><td>0.5-60</td><td>0.15</td><td>[26]</td></tr><tr><td>Pt/PEDOT</td><td>CV</td><td>0.01-6500</td><td>0.003</td><td>[24]</td></tr><tr><td>GCE/F-GO</td><td>DPV</td><td>0.5-100</td><td>0.018</td><td>[27]</td></tr><tr><td>SPCE/RGO</td><td>DPV</td><td>0.2-2100</td><td>0.064</td><td>[8]</td></tr><tr><td>Au/PdNPS-GRF</td><td>DPV</td><td>0.03-939</td><td>0.006</td><td>[29]</td></tr><tr><td>GCE/SnO<sub>2</sub>-RGO</td><td>DPV</td><td>0.15-35</td><td>0.08</td><td>[30]</td></tr><tr><td>GCE/Ag/PTh</td><td>DPV</td><td>0.01-4.83</td><td>0.005</td><td>[25]</td></tr><tr><td>PE/CdO/SWCNTs/PE</td><td>LSV</td><td>0.02-200</td><td>0.009</td><td>[23]</td></tr><tr><td>SPCE/MIP</td><td>DPV</td><td>2-40</td><td>0.68</td><td>This work</td></tr></tbody></table>

RGO/PDA, reduce graphene oxide/polydopamine.

Au/MIS, gold electrode functionalized with a molecularly imprinted siloxane.

Pt/PEDOT, platinum electrode modified with poly(3,4-ethylenedioxy).

GCE/F-GO, glassy carbon electrode modified with fluorine-doped graphene oxide.

SPCE/RGO, screen printed carbon electrode modified with reduced graphene oxide.

Au/PdNPS-GRF, gold and palladium nanoparticles deposited on graphene flakes.

GCE/SnO₂-RGO, glassy carbon electrode modified with SnO₂ decorated graphene.

GCE/Ag/PTh, glassy carbon electrode modified with silver nanoparticle decorated poly(thiophene).

PE/CdO/SWCNTs/PE, paste electrode modified with cadmium oxide decorated on single wall carbon nanotubes.

SPCE/MIP, screen printed electrode modified with a molecularly imprinted polymer.

CV, Cyclic voltammetry; DPV, differential pulse voltammetry, LSV, linear sweep volammetry.
<figure><img src="image_9.png" /><figcaption>Fig. 4. The response of the MIP-sensor and NIP, represented by % of rebinding in the presence of CA and other interferents: trans-ferulic acid (FA), sinapic acid (SA) gallic acid (GA), and p-coumaric acid (p-CA). The concentration of each interferent compounds was 30 µM. Experiments were performed in triplicate.</figcaption></figure>

in the presence of CA was higher than that of the other interferents, indicating the effective imprinting process and greater selectivity for caffeic acid. FA and SA showed lower peaks, corresponding to 23 and 33 % of the CA peak, respectively. GA and p-CA did not show any response.

The high selectivity towards CA could be attributed to the size and arrangement of functional groups in the MIP's specific cavities, that perfectly match with CA. An explanatory diagram illustrating the interactions between caffeic acid and the interferent compounds on one side, and the functional monomer on the other, is provided in the supplementary data section (Fig. S3). It highlights the role of the orthodiphenol group, present in the CA molecule, in explaining the superior selectivity of the MIP for CA compared to other antioxidants.

### 3.7. Determination of CA in red wine samples using the MIP-SPCE sensor and comparison with HPLC-UV

The determination of CA using the new MIP-SPCE sensor was performed in red wine samples, which were spiked with five different concentrations of caffeic acid: 11, 33, 44, 56, and 167 µM. Then, 50 µL of each sample were deposited on the surface of the MIP-sensors, and the DPV signal was measured. To compare the results with a conventional method, the same wine samples were analysed using HPLC-UV. Two types of recovery were calculated, recovery 1 (eq. 5) and recovery 2 (eq. 6). Recovery 2 compares the results obtained with DPV and HPLC, and they were in the range of 98 to 114 % (Table 3).

<caption>**Table 3**</caption>

Determination of CA concentration and recovery values in spiked wine samples using the MIP-SPCE sensor and HPLC-UV methods. Values with different letters (a,b) in each row indicate a significant difference among concentration at *p* < 0.05.

<table><thead><tr><th rowspan="2">Added CA (µM)</th><th colspan="3">MIP-SPCE sensor</th><th colspan="3">HPLC</th></tr><tr><th>Found CA (µM)</th><th>RSD (%)</th><th>Recovery 1 (%)</th><th>Found CA (µM)</th><th>RSD (%)</th><th>Recovery 2 (%)</th></tr></thead><tbody><tr><td>11</td><td>12.0 ± 0.3 <sup>a</sup></td><td>2.5</td><td>109</td><td>12.0 ± 0.6 <sup>a</sup></td><td>5.0</td><td>100</td></tr><tr><td>33</td><td>33.0 ± 0.9 <sup>a</sup></td><td>2.7</td><td>100</td><td>29.0 ± 0.6 <sup>b</sup></td><td>2.1</td><td>114</td></tr><tr><td>44</td><td>43.0 ± 1.8 <sup>a</sup></td><td>4.2</td><td>98</td><td>39.0 ± 0.3 <sup>a</sup></td><td>3.3</td><td>110</td></tr><tr><td>56</td><td>53.0 ± 2.3 <sup>a</sup></td><td>4.3</td><td>95</td><td>54.0 ± 0.9 <sup>a</sup></td><td>1.7</td><td>98</td></tr><tr><td>167</td><td>178.0 ± 5.0 <sup>a</sup></td><td>2.8</td><td>107</td><td>169.0 ± 6.0 <sup>a</sup></td><td>3.6</td><td>105</td></tr></tbody></table>

$$ \text{Recovery 1} = \frac{[\text{CA}]_{\text{found in DPV}}}{[\text{CA}]_{\text{added}}} \qquad (5) $$

$$ \text{Recovery 2} = \frac{[\text{CA}]_{\text{found in DPV}}}{[\text{CA}]_{\text{found in HPLC}}} \qquad (6) $$

A t-test was conducted, and no significant difference was observed between the results of the two methods, except at one concentration. All recovery values fell within the acceptable range of 80–120 %. These

<figure><img src="image_10.png" /><figcaption>Fig. 5. Repeatability and stability of MIP-SPCE sensor. (A) DPV responses of MIP-SPCE, under the optimised conditions (T/FM molar ratio of 1/10, 10 electropolymerization cycles, 20 min rebinding time), after the rebinding of 50 µM CA in 0.05 M PBS (pH 3)/ethanol (90/10, v/v). DPV parameters: from 0.1 to 0.8 V, Estep = 0.004 V, Epulse = 0.08 V, tpulse = 200 ms, and Srate = 0.04 V. Signals are shown after the 1st, the 15th and the 23rd trial. (Sensor A) DPV responses of MIP-SPCE after storage of 7, 12 and 15 days at room temperature. (Sensor B) DPV responses of MIP-SPCE after storage of 7, 12 and 15 days in 0.05 M PBS (pH 3).</figcaption></figure>
results show that the proposed MIP-sensor could be used as an accurate device for fast CA determination in wine samples, without the need for sample pre-treatment or preparation. The recovery values close to 100 % indicate that the wine matrix does not affect the MIP-SPCE signal over the concentration range of 11 to 167 µM.

### 3.8. Repeatability and stability of the sensor

The repeatability (intra-day precision) of the MIP-sensor was also evaluated, and the DPV responses of MIP-SPCE are presented in Fig. 5. Repeatability (Fig. 5 A) was estimated over 23 consecutive repetitions at 50 µM of caffeic acid and expressed as relative standard deviation (RSD) and percentage decrease of the peak current. The RSD was found to be 4.6 % at the 15th trial, with a 2.6 % decrease in $I_{pa}$. By the end of the 23 repetitions, the RSD increased to 10.4 %, with a 37 % of decrease in $I_{pa}$. These results indicate that the sensor can be used reliably with satisfactory precision for at least 15 consecutive measurements.

Moreover, the stability of the MIP-sensor was evaluated under two different storage conditions. One sensor (sensor A) was stored dry at room temperature, while the other (sensor B) was stored in a 0.05 M PBS solution at pH 3. DPV responses of sensor A and sensor B are presented in Figs. 5. Their responses were evaluated on days 7, 12, and 15. It was found that the peak current of sensor A decreased by 6, 10, and 26 % on days 7, 12 and 15, respectively, while for sensor B, the decrease was 2, 7 and 11 % over the same period. These results indicate that the MIP-sensor demonstrates improved stability when stored in a 0.05 M PBS solution at pH 3.

## 4. Conclusions

In this work, a selective MIP electrochemical sensor for the determination of caffeic acid in hydro-alcoholic solutions and wine samples was developed using electropolymerization of 4-ABA on the surface of SPCE. The functional monomer (4-ABA) was selected based on molecular simulation. The proposed sensor showed a good linear relationship ($R^2 = 0.9992$) in the concentration range of 0 to 40 µM, with a LOD of 0.68 µM. This demonstrates that the method is both fast and sensitive enough for direct application to real samples without the need for sample preparation. The MIP-SPCE sensor showed good selectivity towards CA when compared to other interferents with similar chemical structures. Furthermore, the MIP-sensor was successfully applied to wine samples, and its signal was compared with and validated by HPLC-UV, showing recovery values ranging from 98 to 114 %. These results, combined with the simplicity and the rapidity of sensor preparation, indicate that the developed MIP-sensor could be a promising green technique for future commercial applications, such as quantification of CA or other antioxidants.

### CRediT authorship contribution statement

**Marie Elhachem:** Writing – review & editing, Writing – original draft, Visualization, Validation, Formal analysis, Conceptualization.

**Philippe Cayot:** Writing – review & editing, Validation, Supervision, Investigation, Formal analysis. **Adrien Lerbret:** Writing – review & editing, Writing – original draft, Formal analysis. **Igor Bezverkhy:** Writing – review & editing, Writing – original draft, Formal analysis.

**Thomas Karbowiak:** Writing – review & editing, Formal analysis.

**Maher Abboud:** Writing – review & editing, Validation, Supervision,

Formal analysis. **Richard G. Maroun:** Writing – review & editing, Validation, Project administration, Investigation, Funding acquisition,

Formal analysis, Conceptualization. **Elias Bou-Maroun:** Writing – review & editing, Validation, Supervision, Investigation, Funding acquisition,

Formal analysis, Conceptualization.

## Funding

This research was funded by L'Institut Agro Dijon and the Research council of Saint-Joseph University of Beirut, Grant Number FS140.

## 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.

## Acknowledgement

The MD simulations were performed using HPC resources from DNUM-CCUB (Université de Bourgogne).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.microc.2025.114924.

## Data availability

Data will be made available on request.

## References

[1] A. Birková, B. Hubková, B. Bolerázska, M. Mareková, B. Čizmárová, Caffeic acid: a brief overview of its presence, metabolism, and bioactivity, Bioactive Compounds Health Dis. 3 (2020) 74–81, https://doi.org/10.31989/bchd.v3i4.692.

[2] J. Darias-Martín, B. Martín-Luis, M. Carrillo-López, R. Lamuela-Raventós, C. Díaz-Romero, R. Boulton, Effect of Caffeic acid on the color of red wine, J. Agric. Food Chem. 50 (2002) 2062–2067, https://doi.org/10.1021/jf010931+.

[3] M. Migliori, V. Cantaluppi, C. Mannari, A.A.E. Bertelli, D. Medica, A.D. Quercia, V. Navarro, A. Scatena, L. Giovannini, L. Biancone, V. Panichi, Caffeic acid, a phenol found in white wine, modulates endothelial nitric oxide production and protects from oxidative stress-associated endothelial cell injury, PLoS One 10 (2015) e0117530, https://doi.org/10.1371/journal.pone.0117530.

[4] L. Minuti, R.M. Pellegrino, I. Tesei, Simple extraction method and gas chromatography-mass spectrometry in the selective ion monitoring mode for the determination of phenols in wine, J. Chromatogr. A 1114 (2) (2006) 263–268, https://doi.org/10.1016/j.chroma.2006.02.068.

[5] H. Wang, G.J. Provan, K. Helliwell, Determination of rosmarinic acid and caffeic acid in aromatic herbs by HPLC, Food Chem. 87 (2004) 307–311, https://doi.org/10.1016/j.foodchem.2003.12.029.

[6] P. Yang, H. Li, H. Wang, F. Han, S. Jing, C. Yuan, A. Guo, Y. Zhang, Z. Xu, Dispersive liquid-liquid microextraction method for HPLC determination of phenolic compounds in wine, Food Anal. Methods 10 (2017) 2383–2397, https://doi.org/10.1007/s12161-016-0781-2.

[7] R. Nehru, Y.-F. Hsu, S.-F. Wang, Electrochemical determination of caffeic acid in antioxidant beverages samples via a facile synthesis of carbon/iron-based active electrocatalyst, Anal. Chim. Acta 1122 (2020) 76–88, https://doi.org/10.1016/j.aca.2020.05.001.

[8] M. Velmurugan, P. Balasubramanian, S.-M. Chen, Determination of Caffeic acid in wine samples based on the electrochemical reduction of graphene oxide modified screen printed carbon electrode, Int. J. Electrochem. Sci. 12 (2017) 4173–4182, https://doi.org/10.20964/2017.05.01.

[9] Z. Dourandish, F. Garkani Nejad, R. Zaimbashi, S. Tajik, M.B. Askari, P. Salarizadeh, S.Z. Mohammadi, H. Oloumi, F. Mousazadeh, M. Baghayeri, H. Beitollai, Recent advances in electrochemical sensing of anticancer drug doxorubicin: a Mini-review, Chem. Methodol. 8 (2024) 293–315, https://doi.org/10.48309/chemm.2024.441220.1761.

[10] P.M. Jahani, S.Z. Mohammadi, A. Khodabakhshzadeh, J.H. Cha, M.S. Asl, M. Shokouhimehr, K. Zhang, Q. Van Le, W. Peng, Simultaneous detection of morphine and diclofenac using graphene nanoribbon modified screen-printed electrode, Int. J. Electrochem. Sci. 15 (2020) 9037–9048, https://doi.org/10.20964/2020.09.14.

[11] S.Z. Mohammadi, H. Beitollahi, M. Kaykhaii, N. Mohammadzadeh, S. Tajik, R. Hosseinzadeh, Simultaneous determination of droxidopa and carbidopa by carbon paste electrode functionalized with NiFe2O4 nanoparticle and 2-(4-ferrocenyl-[1,2,3]triazol-1-yl)-1-(naphthalen-2-yl) ethanone, Measurement 155 (2020) 107522, https://doi.org/10.1016/j.measurement.2020.107522.

[12] P. Mohammadzadeh Jahani, M.R. Aflatoonian, R. Abbasi Rayeni, A. Di Bartolomeo, S.Z. Mohammadi, Graphite carbon nitride-modified screen-printed electrode as a highly sensitive and selective sensor for detection of amaranth, Food Chem. Toxicol. 163 (2022) 112962, https://doi.org/10.1016/j.fct.2022.112962.

[13] T. Rohani, S.Z. Mohammadi, N. Gholamhosein Zadeh, M.B. Askari, A novel carbon ceramic electrode modified by Fe3O4 magnetic nanoparticles coated with aptamer-
immobilized polydopamine: an effective label-free aptasensor for sensitive detection of diclofenac, Microchem. J. 166 (2021) 106274, https://doi.org/10.1016/j.microc.2021.106274.

[14] P. Salarizadeh, M.B. Askari, A. Beheshti-Marnani, M. seifi, S.M. Rozati, T. Rohani, N. Askari, N. Salarizadeh, S.Z. Mohammadi, Synthesis and characterization of (co, Fe, Ni)9 S8 nanocomposite supported on reduced graphene oxide as an efficient and stable electrocatalyst for methanol electrooxidation toward DMFC, J. Mater. Sci. Mater. Electron. 30 (2019) 3521-3529, https://doi.org/10.1007/s10854-018-00629-7.

[15] L. Carballido, T. Karbowiak, P. Cayot, M. Gerometta, N. Sok, E. Bou-Maroun, Applications of molecularly imprinted polymers and perspectives for their use as food quality trackers, Chem 8 (2022) 2330-2341, https://doi.org/10.1016/j.chempr.2022.05.021.

[16] E. Bou-Maroun, Carbon electrode modified with molecularly imprinted polymers for the development of electrochemical sensor: application to pharmacy, food safety, environmental monitoring, and biomedical analysis, Chemosensors 11 (2023) 548, https://doi.org/10.3390/chemsensors11110548.

[17] T. Sikolenko, E. Bou-Maroun, T. Karbowiak, Deposition routes of molecularly imprinted silica for the development of highly specific electrochemical "in-field" sensors, Curr. Opin. Electrochem. 36 (2022) 101151, https://doi.org/10.1016/j.coelec.2022.101151.

[18] A.G. Ayankojo, J. Reut, A. Öpik, V. Syritski, Sulfamethizole-imprinted polymer on screen-printed electrodes: towards the design of a portable environmental sensor, Sensors Actuators B Chem. 320 (2020) 128600, https://doi.org/10.1016/j.snb.2020.128600.

[19] Z.M. Karazan, M. Roushani, A new method for electrochemical determination of Hippuric acid based on molecularly imprinted copolymer, Talanta 246 (2022) 123491, https://doi.org/10.1016/j.talanta.2022.123491.

[20] Z.M. Karazan, M. Roushani, A novel electrochemical sensor for the determination of histidine based on a molecularly imprinted copolymer, Anal. Methods 13 (2021) 4904-4910, https://doi.org/10.1039/D1AY01492G.

[21] N. Mahdi, M. Roushani, Z.M. Karazan, Electrochemical sensor based on molecularly imprinted copolymer for selective and simultaneous determination of riboflavin, dopamine, and L-tryptophan, J. Mol. Recognit. 36 (2023) e3053, https://doi.org/10.1002/jmr.3053.

[22] M. Roushani, Z.M. Karazan, Novel electrochemical sensor based on Electropolymerized dopamine molecularly imprinted polymer for selective detection of pantoprazole, IEEE Sensors J. 22 (2022) 6263-6269, https://doi.org/10.1109/JSEN.2022.3150222.

[23] Z. Arab, S. Jafarian, H. Karimi-Maleh, L.R. Nasiraie, M. Ahmadi, Electrochemical sensing of caffeic acid antioxidant in wine samples using carbon paste electrode amplified with CdO/SWCNTS: original scientific paper, J. Electrochem. Sci. Eng. 14 (2024) 75-82, https://doi.org/10.5599/jese.1701.

[24] C. Bianchini, A. Curulli, M. Pasquali, D. Zane, Determination of caffeic acid in wine using PEDOT film modified electrode, Food Chem. 156 (2014) 81-86, https://doi.org/10.1016/j.foodchem.2014.01.074.

[25] Ş.U. Karabiberoglu, E.M. Ayan, Z. Dursun, Electroanalysis of Caffeic acid in red wine and investigation of thermodynamic parameters using an ag nanoparticles modified poly(Thiophene) film glassy carbon electrode, Electroanalysis 25 (2013) 1933-1945, https://doi.org/10.1002/elan.201300091.

[26] F.R.F. Leite, W. de J.R. Santos, L.T. Kubota, Selective determination of caffeic acid in wines with electrochemical sensor based on molecularly imprinted siloxanes, Sensors Actuators B Chem. 193 (2014) 238-246, https://doi.org/10.1016/j.snb.2013.11.028.

[27] V.S. Manikandan, B. Sidhureddy, A.R. Thiruppathi, A. Chen, Sensitive electrochemical detection of caffeic acid in wine based on fluorine-doped graphene oxide, Sensors 19 (2019) 1604, https://doi.org/10.3390/s19071604.

[28] K. Thangavelu, S. Palanisamy, S.-M. Chen, V. Velusamy, T.-W. Chen, S.K. Ramaraj, Electrochemical determination of Caffeic acid in wine samples using reduced graphene oxide/Polydopamine composite, J. Electrochem. Soc. 163 (2016) B726, https://doi.org/10.1149/2.1231614jes.

[29] K. Thangavelu, N. Raja, S.-M. Chen, W.-C. Liao, Nanomolar electrochemical detection of caffeic acid in fortified wine samples based on gold/palladium nanoparticles decorated graphene flakes, J. Colloid Interface Sci. 501 (2017) 77-85, https://doi.org/10.1016/j.jcis.2017.04.042.

[30] J.-W. Zhang, K.-P. Wang, X. Zhang, Fabrication of SnO2 decorated graphene composite material and its application in electrochemical detection of caffeic acid in red wine, Mater. Res. Bull. 126 (2020) 110820, https://doi.org/10.1016/j.materresbull.2020.110820.

[31] P. Rebelo, J.G. Pacheco, I.V. Voroshylova, M.N.D.S. Cordeiro, C. Delerue-Matos, Development of a molecular imprinted electrochemiluminescence sensor for amitriptyline detection: from MD simulations to experimental implementation, Electrochim. Acta 397 (2021) 139273, https://doi.org/10.1016/j.electacta.2021.139273.

[32] P. Rebelo, J.G. Pacheco, I.V. Voroshylova, A. Melo, M.N.D.S. Cordeiro, C. Delerue-Matos, A simple electrochemical detection of atorvastatin based on disposable screen-printed carbon electrodes modified by molecularly imprinted polymer: experiment and simulation, Anal. Chim. Acta 1194 (2022) 339410, https://doi.org/10.1016/j.aca.2021.339410.

[33] P. Rebelo, J.G. Pacheco, I.V. Voroshylova, A. Melo, M.N.D.S. Cordeiro, C. Delerue-Matos, Rational development of molecular imprinted carbon paste electrode for Furazolidone detection: theoretical and experimental approach, Sensors Actuators B Chem. 329 (2021) 129112, https://doi.org/10.1016/j.snb.2020.129112.

[34] P. Rebelo, J.G. Pacheco, I.V. Voroshylova, I. Seguro, M.N.D.S. Cordeiro, C. Delerue-Matos, Computational modelling and sustainable synthesis of a highly selective electrochemical MIP-based sensor for citalopram detection, Molecules 27 (2022) 3315, https://doi.org/10.3390/molecules27103315.

[35] M.J. Abraham, T. Murtola, R. Schulz, S. Päll, J.C. Smith, B. Hess, E. Lindahl, GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX 1 (2015) 19-25, https://doi.org/10.1016/j.softx.2015.06.001.

[36] D. Van Der Spoel, E. Lindahl, B. Hess, G. Groenhof, A.E. Mark, H.J.C. Berendsen, GROMACS: fast, flexible, and free, J. Comput. Chem. 26 (2005) 1701-1718, https://doi.org/10.1002/jcc.20291.

[37] L.S. Dodda, J.Z. Vilseck, J. Tirado-Rives, W.L. Jorgensen, 1.14*CM1A-LBCC: localized bond-charge corrected CM1A charges for condensed-phase simulations, J. Phys. Chem. B 121 (2017) 3864-3870, https://doi.org/10.1021/acs.jpcb.7b00272.

[38] W.L. Jorgensen, J. Tirado-Rives, Potential energy functions for atomic-level simulations of water and organic and biomolecular systems, Proc. Natl. Acad. Sci. 102 (2005) 6665-6670, https://doi.org/10.1073/pnas.0408037102.

[39] L.S. Dodda, I. Cabeza de Vaca, J. Tirado-Rives, W.L. Jorgensen, LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands, Nucleic Acids Res. 45 (2017) W331-W336, https://doi.org/10.1093/nar/gkx312.

[40] J.L.F. Abascal, C. Vega, A general purpose model for the condensed phases of water: TIP4P/2005, J. Chem. Phys. 123 (2005) 234505, https://doi.org/10.1063/1.2121687.

[41] B. Hess, P-LINCS: a parallel linear constraint solver for molecular simulation, J. Chem. Theory Comput. 4 (2008) 116-122, https://doi.org/10.1021/ct700200b.

[42] U. Essmann, L. Perera, M.L. Berkowitz, T. Darden, H. Lee, L.G. Pedersen, A smooth particle mesh Ewald method, J. Chem. Phys. 103 (1995) 8577-8593, https://doi.org/10.1063/1.4701117.

[43] L. Martínez, R. Andrade, E.G. Birgin, J.M. Martínez, PACKMOL: a package for building initial configurations for molecular dynamics simulations, J. Comput. Chem. 30 (2009) 2157-2164, https://doi.org/10.1002/jcc.21224.

[44] G.J. Martyna, M.L. Klein, M. Tuckerman, Nosé-hoover chains: the canonical ensemble via continuous dynamics, J. Chem. Phys. 97 (1992) 2635-2643, https://doi.org/10.1063/1.463940.

[45] S. Nosé, M.L. Klein, Constant pressure molecular dynamics for molecular systems, Mol. Phys. 50 (1983) 1055-1076, https://doi.org/10.1080/00268978300102851.

[46] M. Parrinello, A. Rahman, Polymorphic transitions in single crystals: a new molecular dynamics method, J. Appl. Phys. 52 (1981) 7182-7190, https://doi.org/10.1063/1.328693.

[47] A. Luzar, D. Chandler, Hydrogen-bond kinetics in liquid water, Nature 379 (1996) 55-57, https://doi.org/10.1038/379055a0.

[48] N. Fairley, V. Fernandez, M. Richard-Plouet, C. Guillot-Deudon, J. Walton, E. Smith, D. Flahaut, M. Greiner, M. Biesinger, S. Tougaard, D. Morgan, J. Baltrusaitis, Systematic and collaborative approach to problem solving using X-ray photoelectron spectroscopy, Appl Surf Sci Adv 5 (2021) 100112, https://doi.org/10.1016/j.apsadv.2021.100112.

[49] S. Tanuma, C.J. Powell, D.R. Penn, Calculations of electron inelastic mean free paths. V. Data for 14 organic compounds over the 50-2000 eV range, Surf. Interface Anal. 21 (1994) 165-176, https://doi.org/10.1002/sia.740210302.

[50] Y. Lei, L. dos S. Madalena, B.D. Ossonon, F.E.B. Junior, J. Chen, M.R.V. Lanza, A. C. Tavares, One-step synthesis of aminobenzoic acid functionalized graphene oxide by electrochemical exfoliation of graphite for oxygen reduction to hydrogen peroxide and supercapacitors, Molecules 27 (2022) 7629, https://doi.org/10.3390/molecules27217629.

[51] S. Biniak, G. Szymański, J. Siedlewski, A. Świątkowski, The characterization of activated carbons with oxygen and nitrogen surface groups, Carbon 35 (1997) 1799-1810, https://doi.org/10.1016/S0008-6223(97)00096-1.

[52] Justin Gorham, NIST X-ray Photoelectron Spectroscopy Database - SRD 20, 2012. Doi: 10.18434/T4T88K.

[53] A. Jablonski, C.J. Powell, Effective attenuation lengths for different quantitative applications of X-ray photoelectron spectroscopy, J. Phys. Chem. Ref. Data 49 (2020) 033102, https://doi.org/10.1063/5.0008576.

[54] S. Kumar, S. Pande, P. Verma, Factor affecting electro-deposition process, Int. J. Curr. Eng. Technol. 5 (2015) 700-703.