CuO nanoparticle-enhanced electrochemical sensing of bromfenac in rabbit aqueous humor: a green analytical approach

Loubna M. Elsharkawy^ab, Mariam Gamaleldin^b, Amr M. Mahmoud^c, Samah S. Abbas^c and Nermine S. Ghoniem*^c
aPostgraduate Program, Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Cairo University, Egypt
^bSchool of Biotechnology, Nile University, Egypt
^cPharmaceutical Analytical Chemistry, Faculty of Pharmacy, Cairo University, Egypt. E-mail: nermine.ghoniem@pharma.cu.edu.eg

First published on 8th August 2025

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Introduction

Intraocular surgery, a common procedure for various ocular conditions, can lead to post-operative inflammation. This inflammation arises from the disruption of the blood-aqueous barrier, allowing the influx of inflammatory mediators, such as proteins, cytokines, and growth factors, into the anterior chamber. To manage this inflammatory response, anti-inflammatory eye drops are often prescribed.¹ Non-steroidal anti-inflammatory drugs (NSAIDs) have emerged as a preferred choice for post-operative ocular inflammation due to their effectiveness and reduced risk of side effects compared to corticosteroids. While corticosteroids can be effective in reducing inflammation, their prolonged use can lead to serious complications like increased intraocular pressure and susceptibility to infection. Alternatively, NSAIDs provide targeted relief for inflammation without these associated risks. By understanding the mechanisms of post-operative inflammation and the benefits of NSAID-based eye drops, clinicians can make informed decisions to optimize patient outcomes and minimize complications following intraocular surgery.^2,3

Bromfenac (BRM) (Fig. S1) is chemically known as 2-[2-amino-3-(4-bromobenzoyl) phenyl] acetic acid. Its molecular formula is C₁₅H₁₂BrNO₃.⁴ It is an NSAID for ophthalmic use and one of the most potent ophthalmic NSAIDs inhibiting COX-2.⁵ Its penetration into ocular tissue is rapid and extensive. After being administered to rabbit eyes, bromophenac's concentration peaks in the aqueous humor at 2 hours and is still present throughout all eye tissues, including the retina, after 24 hours. In humans, its peak concentration occurs after 150–180 minutes, and its effective concentration remains in the eye for 12 hours.^5,6

Different clinical and pharmacological studies have been reported on the promising efficacy of BRM in the management of postoperative inflammation following cataract surgery^5,7 However, a review of the literature reveals few methods have been reported for the determination of BRM, either alone or in combination with other drugs. These methods include spectrophotometric methods^8–11 and high-performance liquid chromatography (HPLC) methods for BRM in eye drops^12–14 and in plasma.^15,16 According to previous literature reviews, no methods have been reported for detection and determination of BRM using voltammetric techniques in rabbit aqueous humor.

Unlike traditional analytical methods, such as high-performance liquid chromatography (HPLC), which are often time-consuming and require sophisticated instrumentation, electrochemical techniques offer a rapid, sensitive, and cost-effective alternative. They have emerged as powerful tools for the quantification of pharmaceutical drugs. Techniques such as voltammetry, potentiometry, amperometry, and conductometry offer significant advantages in drug analysis due to their high accuracy, specific identification, and affordability.¹⁷ Voltammetric techniques use a potential applied to an electrode to cause electrochemical oxidation or reduction of electroactive species at the electrode surface, allowing the monitoring of the current passing through the electrochemical cell.

Since 1958, the electrochemical study of medicines has made use of carbon paste electrodes (CPEs), one of the most widely utilized working electrodes in electrochemistry.¹⁸ CPEs are commonly prepared by mixing electroconductive carbon particles and the most stable carbon particles are graphene particles with a suitable nonconductive, water immiscible liquid to form a paste, such as paraffin oil. Moreover, CPEs are simple to prepare and have a surface that conducts electricity, and they also feature a broad positive and negative potential range that allows extremely precise and accurate analysis. Additionally, to improve the peaks produced and increase the sensitivity of the analysis, CPEs can be further modified with electroactive nanoparticles such as copper oxide nanoparticles (CuO-NPs), zinc sulfide nanoparticles (ZnS-NPs), and zinc oxide nanoparticles (ZnO-NPs),^18–20 which are known to improve the sensitivity by increasing the kinetics and enhancing the attraction between the adsorbent and adsorbate.²¹

As the field of green analytical chemistry has expanded, the various tools designed to assess the environmental impact of analytical techniques have also advanced. These tools aim to evaluate factors like energy consumption, waste generation, and the use of hazardous substances. In recent years, Analytical Eco-Scale (AES) and the Green Analytical Procedure Index (GAPI) have emerged as two widely used methods for assessing the greenness of analytical procedures.^22,23

This work aims to significantly enhance the sensitivity of BRM analysis through the development of a novel voltammetric sensor based on hydrothermally synthesized CuO-NPs. The performance of this sensor is evaluated in pharmaceutical and biological samples, and the environmental sustainability of the entire analytical procedure is assessed using two greenness assessment tools.

Experimental

Instrumentation and software

A potentiostat [Metrohm Autolab Potentiostat/galvanostat PGSTAT204 (Netherlands)] connected to the Nova software version 2.1.6 was utilized for electrochemical analysis. The analysis was done using 3 electrodes including a working electrode (a carbon paste electrode; CPE), an auxiliary platinum electrode, and a reference electrode (Ag/AgCl, 3 M KCl). The adjustment of the pH was carried out using a Jenway pH-meter 3310 (Dunmow, Essex, United Kingdom). Centrifugation of the samples was done using a Centurion K241R (United Kingdom). Hydrothermal synthesis was performed in a Parr 4748 Hydrothermal Autoclave Reactor with a stainless steel (SS-304) outer body and a Teflon (PTFE) liner with a volume of 100 mL. SigmaPlot software was employed for advanced graphical representation and statistical analysis.

Chemicals and reagents

BRM (≥99% purity), carbon nanotubes (≥98% purity), graphene (<20 μm particle size) and copper sulfate were obtained from Sigma-Aldrich (Darmstadt, Germany), hexamethylenetetramine (HMTA) from El Nasr Pharmaceutical Chemicals (Cairo, Egypt), boric acid and phosphoric acid were purchased from Adwick (Cairo, Egypt) and acetic acid was obtained from Biochem (Cairo, Egypt). Britton–Robinson (B–R) buffer 0.04 M was prepared by mixing 2.3 mL acetic acid, 2.1 mL phosphoric acid and 2.47 g boric acid in volume of 1000 mL distilled water.²⁴ The adjustment of pH was done by using 0.5 M NaOH to reach the desired pH (4.0). The pharmaceutical dosage form (BROMOFLAM) used for the experiment was manufactured by Eva Pharma (Cairo, Egypt). The aqueous humor was obtained from the Animal House of the Faculty of Pharmacy (Cairo, Egypt).

Stock and working standard solution preparation

The BRM stock solution (1.00 × 10⁻³ M) was prepared by accurately weighing 8.35 mg of pure BRM standard powder and dissolving it in 0.04 M B–R buffer (pH 4) in a 25.0 mL volumetric flask, and then it was completed with B–R buffer.

Working solution preparation

The working solution (1.00 × 10⁻⁴ M) was prepared by transferring 2.5 mL of the stock solution to a 25.0 mL volumetric flask, and then diluting it using B–R buffer (pH 4.0).

Nanoparticle synthesis

The CuO-NPs were synthesized via an in situ hydrothermal method, as follows: 100.00 mg of CNTs was added to 20.0 mL deionized pure water, and then sonicated for 30 min to get a homogeneous dispersion of CNT. After that, 20.0 mL of CuSO₄·5H₂O solution (which was prepared by weighing 80.00 mg of CuSO₄ and adding it to 20.0 mL deionized pure water) was added to the homogenous dispersion of CNTs that had been produced, while being constantly stirred. 140.00 mg of hexamethylenetetramine (HMTA) was added while being continuously stirred. The homogenous solution was moved to a 50.0 mL Teflon-lined stainless autoclave and heated for 12 hours at 170 °C. Following that, the autoclave was left to cool to ambient temperature. The black precipitate was gathered by centrifugation and repeatedly cleaned with ethanol and ultrapure water. Finally, the successful fabrication of CuO-NPs was achieved, and it was stored for the next electrode fabrication.

Electrochemical sensor preparation

CuO-NPs were added to a carbon paste electrode to increase the electrochemical sensitivity of the electrode. The study of the particle size and composition is carried out using scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analysis. The SEM image shows copper oxide nanoparticles (CuO) and the EDX spectrum confirms that the nanoparticles are mainly composed of copper and oxygen.

Electrochemical measurements

The methods of CV and DPV were carried out at room temperature. In the DPV determinations, the step potential was 0.006 V, while the scanning potential range was 0.000 V to 1.500 V. The modulation time was adjusted to 0.070 s and the modulation amplitude was adjusted to 0.075. The scan rate was set at 0.0125 mV and the interval time was adjusted to 0.40 s. The scanning potential in the CV study was between 0.10 V and 1.50 V, and the scan rate range was 10.0 mV to 100.0 mV.

Calibration curve range

To build the calibration curve, aliquots of different concentrations were withdrawn from 1.00 × 10⁻⁴ M BRM working solutions, then transferred to 25 mL volumetric flasks and diluted with 0.04 M B–R buffer to get five different concentrations for each of the calibration curves. The calibration curve range was from 2.00 × 10⁻⁷ M to 2.00 × 10⁻⁶ M and 2.00 × 10⁻⁶ M to 2.00 × 10⁻⁵ M.

Preparation of spiked aqueous humor samples

Different working BRM solutions were prepared from 1.00 × 10⁻³ M BRM stock solution to prepare spiked aqueous humor samples with a concentration in the range of 2.00 × 10⁻⁶ M–1.00 × 10⁻⁵ M. The resulting samples were made up to 25.0 mL using B–R buffer pH 4.0.

Preparation of market dosage form

To prepare the market dosage form, various working solutions were made from 1.00 × 10⁻³ M BRM dosage form (BROMOFLAM), resulting in a mixture of 2.00 × 10⁻⁶ M–1.00 × 10⁻⁵ M. The samples obtained were made up to 25.0 mL using B–R buffer at pH 4.0.

Results and discussion

Characterization of synthesized CuO-NPs

The morphological analysis presented in Fig. 1(A), obtained using SEM, points out the efficient synthesis of CuO-NPs, highlighting their characteristic nanoscale dimensions and overall structural characteristics. This visual evidence is supplemented by the EDX spectrum presented in Fig. S2, which is crucial. EDX spectroscopy provides excellent elemental confirmation, indicating that the synthesized material is predominantly composed of Cu and O atoms. The presence of prominent peaks for Cu and O is observed in the EDX spectrum, while there are no prominent impurity peaks, which confirms not only the intended composition of the CuO-NPs but also attests to the purity of the synthesized product. This well-defined nanoscale morphology and high purity are crucial, as they directly contribute to the enhanced surface-to-volume ratio and catalytic activity, which are key to their superior performance in electrochemical sensing applications.²⁵ Collectively, these EDX and SEM findings provide an exhaustive initial characterization, confirming the nanoscale morphology and elemental composition of the resultant copper oxide material, which are critical characteristics required for the interpretation of its possible uses and properties.

Electrochemical analysis methods

Two voltammetric methods are used for the initial electrochemical analysis of BRM, DPV and CV. Both approaches are used to examine the drug in the potential range of 0.0 V to 1.5 V, with CuO-NP-modified CPE as the working electrode and Ag/AgCl as the reference electrode. The parameters studied were the effect of pH and scan rate. The results obtained were utilized to optimize the conditions for drug detection by DPV. DPV is widely applied for the quantitative determination of drugs due to its intrinsic advantages in electroanalysis. DPV enhances the sensitivity and resolution compared to other voltammetric techniques by superimposing a series of small amplitude pulses onto a linear potential sweep. Moreover, DPV can distinguish between analytes with closely related redox potentials, thus improving the selectivity for the quantitative analysis of pharmaceuticals in formulations or biological fluids. Several trials were conducted using carbon-based electrodes modified with various metal nanoparticles to enhance the sensitivity and selectivity of the detection system.

Optimized electrode composition and performance

The electrode synthesis outcomes, in particular the finding that the composite containing 99% graphene and 1% CuO-NPs has the greatest response, is shown in Fig. S3. The dominance of graphene in the electrode composition emphasizes the contribution of its intrinsic properties such as high electrical conductivity and large surface area, to the facilitation of efficient electrochemical processes. The extensive π-electron cloud of graphene enables the rapid transfer of electrons, which is fundamental to attaining a sensitive electrochemical response. The inclusion of a small amount of 1% CuO-NPs appears to enhance the performance of the electrode compared to the pure graphene electrode. Numerous factors are responsible for this. CuO is a transition metal oxide known commonly for its electrocatalytic activity in a variety of electrochemical reactions. The presence of CuO-NPs on the graphene matrix has the potential to include catalytic sites that facilitate the redox reactions of the target analyte, thereby enhancing the signal, and thus the response. The other compositions such as 100% graphene, those with different ratios of graphene to other carbon-based materials (CNTs and graphene) or other metal/metal oxide nanoparticles (copper and zinc oxide) did not yield the same high response. This suggests a specific and positive interaction between the graphene matrix and the 1% loading of CuO-NPs, as shown in Fig. S3. This optimal loading likely provides enough catalytic sites without influencing the overall conductivity and surface area of graphene. Additional loading of CuO-NPs can lead to aggregation, decrease the conductivity of the composite, or block the surface of graphene, thus decreasing the response. The comparison with electrodes modified with other materials (CNTs, graphene, Cu, and ZnO) highlights the unique electrocatalytic properties of CuO for the target analyte or reaction being studied. The synergistic effect of the high conductivity of graphene and the specific catalytic activity of CuO at this concentration appears to be the key to achieving the noted high response.

It was observed that the 99%:1% graphene: CuO-NP ratio generated the best response, with higher CuO-NP concentrations (2% and 5%) resulting in a decrease in the intensity of the BRM peaks and lower sensitivity, as illustrated in Fig. S4. This suggests that while the incorporation of CuO-NPs enhances the performance of the electrode, there is a maximum load, above which will result in a reduction in gains possibly through undesirable effects on the overall properties of the electrode. The improved performance of the 99%:1% composite would likely be due to the optimized combination of the high surface area and conductivity of the dominant graphene phase and the electrocatalytic activity introduced by the small quantity of CuO-NPs. At this low loading, the CuO-NPs would likely be evenly distributed on the graphene sheets to provide sufficient catalytic sites for the target analyte without significantly hindering the electron transport ability of the graphene network. The intimate contact of the CuO-NPs with the conductive graphene matrix facilitates efficient charge transfer, leading to a good electrochemical signal and high sensitivity.

Electrochemical behavior of BRM

The DPV of measurement of 1.00 × 10⁻⁴ M BRM (pH 4.0) at the anodic direction showed oxidation at 0.7 V, which means the reaction is irreversible. It is found that the oxidation peak height employing the modified CPE electrode is higher than that of the bare CPE electrode. As shown in Fig. 2, peak height is 9.3621 × 10⁻³ and the peak potential is 0.7 V.

Effect of pH

The effect of pH was studied in the range of 4.0–10.0, as represented in the voltammogram in Fig. 3. The highest oxidation peak is obtained at pH 4, as presented in Fig. 3(A), which illustrates that pH affects the peak height. All the readings are an average of three replicates, with the standard deviation (SD) ranging from 0.14 to 1.24. Conversely, the peak height drastically decreased in the pH range of 5.0–10.0. The peak oxidation potential decreased with an increase in pH. This implies that protons play a part in the oxidation process of BRM, and that the oxidation process is pH dependent. A linear relationship between pH and peak potential can be seen in Fig. 3(B). The regression equation is determined to be E(V) = −0.035 pH + 0.8914, and the regression coefficient (R²) = 0.999. The slope value is 0.035, which is nearer to 0.059/2, indicating an equal number of protons and electrons participating in the reaction.

Effect of scan rate

CV of 1.00 × 10⁻⁴ M of BRM was studied at various scan rates. The scan rate was studied in the range of 10 mV s⁻¹ to 100 mV s⁻¹, as illustrated in the voltammograms. All readings were an average of three replicates, with standard deviations (SD) ranging from 0.01 to 0.05. As the scan rate increased, the irreversible anodic oxidation peaks of BRM exhibited positive potential shifting and an increase in peak current. Fig. 4(A) represents the linear plot of peak current ι(υA) against square root of the scan rate υ with the regression equation: i (μA) = 0.0346 v^1/2 + 0.0922 and regression coefficient (R²) equal 0.9993, confirming that the diffusion-controlled irreversible oxidation behavior of BRM occurs on the surface of the electrode. The logarithmic plot of scan rate (υ) vs. peak current (ι) in Fig. 4(B) demonstrates a linear relationship, for which the regression equation is log(i) = 0.5528 log(V) + 2.6566 and R² = 0.9992. Based on the given equation, the slope of the equation is 0.5, which indicates that a diffusion-controlled process mediates the electro–oxidation reaction. Laviron's eqn (1) is used to investigate the number of electrons involved in the reaction. It contains the following information: E is the initial potential; K° (s⁻¹) is the standard heterogeneous rate constant of the reaction; α is the transfer coefficient, which equals 0.5; T is the temperature (298 K); R is the universal gas constant, which equals 8.314 J mol⁻¹; F is Faraday's constant, which equals 96485C mol⁻¹; v is the scan rate; and n is the number of electrons involved in the reaction.²⁶ 2 electrons are estimated using the slope of 0.035 produced by the linear plot of Epa against log v.

	(1)

Proposed oxidation mechanism for BRM

Fig. 6 shows the oxidation peak formed due to the oxidation of BRM, which is consistent with the literature.²⁷ Thus, the number of electrons (n) involved in the electrooxidation of BRM is assumed to be ∼2, as presented in Fig. 5.

Method validation

The method validation was carried out according to the International Council for Harmonization (ICH) guidelines including linearity, specificity, accuracy, precision, limit of detection (LOD), and limit of quantification (LOQ).²⁴

Linearity, LOD & LOQ

It is found that the method is linear for two ranges of 2.00 × 10⁻⁷ M–2.00 × 10⁻⁶ M and 2.00 × 10⁻⁶ M–2.00 × 10⁻⁵ M. DPV was used for the construction of the calibration curve as it has higher sensitivity and lower background noise.²⁸ The DPV voltammograms of BRM in the chosen ranges are shown in Fig. S5. Fig. S6A presents the calibration curve for the first range. The regression equation for the first range of 2.00 × 10⁻⁷ M–2.00 × 10⁻⁶ M is y = 0.3454x + 6 × 10⁻⁸ with R² = 0.9991. Fig. S6B presents the calibration curve for the second range. The regression equation for the second curve is y = 0.0202x + 7 × 10⁻⁷ with R² = 0.9991. Moreover, the LOD is 1.36 × 10⁻⁷ and the LOQ is 1.4 × 10⁻⁷, as shown in Table 1.

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Specificity

The method successfully detected BRM without interference from endogenous components present in aqueous humor, such as amino acids, electrolytes like chloride, sodium, and potassium, as well as ascorbic acid, glutathione, and immunoglobulins.²⁹ This demonstrates the selectivity of this method and its potential for accurate BRM quantification in complex biological matrices, as shown in Fig. 6.

Precision

The intraday precision was measured by analyzing three different concentrations three times on the same day, while interday precision was measured by analyzing the same selected concentrations on three different days. The method is found to be precise with % RSD ranging from 0.28 to 0.76 and 0.13 to 1.03 for interday and intraday precision, respectively, as shown in Table 1.

Accuracy

Three different concentrations were analyzed six times. The accuracy ranged from 99.05% to 102.00%, as shown in Table 1.

Application to pharmaceutical dosage form (BROMOFLAM)

The application of the developed voltammetric method to the commercially available ophthalmic solution BROMOFLAM yielded satisfactory results. The determined recoveries were in the range of 98.30% to 101.00%, as shown in Table 2. These results highlight the capability of this method for the accurate and precise quantification of BRM in pharmaceutical formulations. This consistency in recovery values underscores the robustness of this method and its potential for routine quality control analysis of BRM-containing eye drops.

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Spiked rabbit aqueous humor analysis

The developed voltammetric method demonstrated an excellent performance in quantifying BRM in spiked rabbit aqueous humor samples. The recoveries obtained, ranging from 98.40% to 101.00% indicate its high accuracy and minimal matrix effects, as shown in Table 2. This narrow range of recovery values signifies the reliability and suitability of this method for the determination of BRM in complex biological matrices like aqueous humor, suggesting its potential for pharmacokinetic and bioequivalence studies.

Greenness assessment

A successful analytical method is one that is not only accomplishable in terms of its set targets but also takes environmental protection into consideration at the same time. The inception of the idea of ‘green analytical chemistry’ is in fact a consequence of the realization that the overuse of dangerous chemicals and solvents could harm the environment, and it came into the picture in the 2000s for the purpose of promoting safer analytical practices. Over the course of time, assessment instruments have become available for the environmental aspects of methods. In research, the Analytical Eco-Scale (AES) and the Green Analytical Procedure Index (GAPI) are utilized to rate the sustainability of a method that is established as suitable in a methodology study.^22,23 Firstly, AES provides a semi-quantitative way to scan the eco-friendliness of a method. This gives some penalty points of different factors such as reagent use, energy consumption, worker safety, and waste generation. The number of points penalizing the greenness of the method is measured by deducting the total penalty points from 100. The basic guidelines mentioned in AES indicate a method as very green if its score is 75 or above, whereas less than 50 declares non-eco-friendliness.²³ In the envisaged DPV method, the penalty points were calculated and a result of 89 points obtained, and thus it is green. The lost point is because of the potential inhalation risk of solid chemicals during handling and the energy use of hydrothermal synthesis. The developed DPV method, which has greater environmental acceptability than the reported HPLC method,³⁰ also had a greater penalty of 71 points. The cause for the greater penalty in the HPLC method is the utilization of highly hazardous solvents such as acetonitrile, triethylamine, and phosphoric acid. The second green method is GAPI, a recently introduced tool for assessing the environmental impact of analytical procedures. It utilizes a pictogram that visually represents the greenness of each stage, using a color-coding system including red for high impact, yellow for medium impact, and green for low impact. The pictogram is divided into five sections, each representing a different category, as follows: sample handling, which encompasses collection, preservation, transport, and storage. Sample preparation, which includes all steps involved in preparing the sample for analysis. Reagents and solvents, which consider the environmental impact of chemicals used. Instrumentation, which evaluates energy consumption and waste generation by analytical instruments. Method type, which assesses the overall environmental impact of the analytical method. By analyzing these categories, GAPI not only quantifies the overall greenness of a method but also highlights areas where improvements can be made to reduce its environmental footprint.²² The reduced greenness of the reported HPLC method³⁰ is due to the employment of less environmentally friendly solvents and the employment of massive amounts of solvents such as acetonitrile, triethylamine, and phosphoric acid, which lead to the generation of humongous waste, and thus the maximum number of red fields in its GAPI pictogram, as represented in Table 3. These results prove that the developed DPV method used a greener solvent than the reported method.

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Statistical comparison

A statistical comparison of the results from the DPV method used to determine BRM showed no significant difference compared to a previously reported HPLC method,³⁰ and then both were applied to pharmaceutical dosage forms, as shown in Table 4.

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Conclusion

In this study, CuO-NPs were used for the sensitive and selective determination of BRM in pure form, pharmaceutical formulations, and biological fluids. The novelty of CuO-NPs in the electrode material allowed the sensor to exhibit an excellent perform by maximizing the electron transfer kinetics and the effective surface area. The resulting experimental conditions, which are pH 4.0 and DPV technique, resulted in a very good analytical performance. The method shows an extended linear range, low detection and quantification limits, high accuracy, and good precision, which are confirmed according to ICH guidelines. The suggested electrochemical sensor is a prompt, facile, and cost-effective substitute for the traditional analytical methods for the detection of BRM. It can also be considered as a method that quality control laboratories, pharmaceutical industries, and clinical settings will use in routine analysis and monitoring of drug levels. The AES and GAPI tools are employed to show the environmental sustainability of the presented technique, which has been labeled as green. This is the first study in the scientific literature that has employed a greenness assessment for this procedure.

Author contributions

Loubna M. Elsharkawy: formal analysis, methodology and writing the original draft, Mariam Gamaleldin: supervision, methodology, review & editing, Amr M. Mahmoud: supervision, methodology, review & editing, Samah S. Abbas: supervision, methodology, writing, review & editing, and Nermine S. Ghoniem: supervision, methodology, review & editing. All authors approved the final manuscript.

Conflicts of 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

The authors declare that the data supporting the findings of the study are available within the paper and its SI. Should any raw data files be needed in another format, they are available from the corresponding author upon reasonable request.

Chemical structure of BRM (Fig. S1). EDX spectrum (Fig. S2). DPV voltammograms (Fig. S3–S5). See DOI: https://doi.org/10.1039/d5ay01107h.

Acknowledgements

The authors would like to extend their appreciation for the Faculty of Pharmacy, Cairo University for providing the required facilities to perform this work. The study was conducted in accordance with the guidelines authorized by the Research Ethics Committee of the Faculty of Pharmacy, Cairo University, which granted ethics permission (No. AC(3443)).

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