Detection of toxic cypermethrin pesticides in drinking water by simple graphitic electrode modified with Kraft lignin@Ni@g-C₃N₄ nano-composite†

Shumaila Razzaque^a, Muhammad Abubakar^b, Muhammad Asim Farid^c, Rehana Zia^d, Shahid Nazir^d, Humaira Razzaque^e, Abid Ali^f, Zulfiqar Ali^d, Asif Mahmood^g, Waheed Al-Masry^g, Toheed Akhter*^h and Sadaf Ul Hassan*^d
aInstitute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka, 44/51, 01-224, Warszawa, Poland
^bDepartment of Chemistry, University of Management and Technology, C-II, Johar Town, Lahore 54770, Pakistan
^cDepartment of Chemistry, Division of Science and Technology, University of Education Lahore, Lahore 54770, Pakistan
^dDepartment of Chemistry, COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, Lahore 54000, Pakistan. E-mail: sadafulhassan@cuilahore.edu.pk
^eDepartment of Chemistry, University of Wah, Wah Cantt, Pakistan
^fDepartment of Chemistry, The University of Lahore, 1-km Defense road, Lahore, 54000, Pakistan
^gDepartment of Chemical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
^hDepartment of Chemical and Biological Engineering, Gachon University, Seongnam, 13120, Republic of Korea. E-mail: toheed@gachon.ac.kr

First published on 13th August 2024

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1. Introduction

The steady increase in the global population demands higher food production. To boost crop yields by eliminating pests, pesticides are commonly sprayed on crops, and their usage has increased over time.¹ Exporting fruits and vegetables within 3–4 days of pesticide application has become a common practice worldwide. However, this practice come with the several potential health risks to consumers.² For instance, pesticides interact with acetylcholinesterase, leading to the suppression of the immune system, cancer, genetic damage, and teratogenic diseases.³ Farmers exposed to pesticides during their work commonly experience health issues such as a chronic cough, headaches, and red eyes.⁴ In short, the excessive use of pesticides has not only resulted in accumulation in the food chain but also leaching into soil and water bodies, thereby affecting terrestrial and aquatic life.⁵ Therefore, accurate measurement and quantification of pesticide residues are highly important.⁶

To date, various conventional methods have been employed for sensing pesticides. For instance, high-performance liquid chromatography/thin-layer chromatography, gas chromatography–mass spectrometry, fluorescence spectrophotometry, and capillary electrophoresis. However, these traditional techniques require expensive instruments, sample pre-treatment, large sample volumes, and skilled personnel.⁷ Therefore, developing rapid and reliable methods for the sensitive detection of pesticide residues is a crucial focus in modern research aimed at ensuring food safety.⁸ To date, various electrochemical sensing methods have been employed to detect pesticide residues in agricultural products and the environment.^7–9 These electrochemical techniques are favored over traditional methods because they offer several advantages, such as rapid response, on-site analysis, affordability, selectivity, and high sensitivity.¹⁰

CYP is a pyrethroid insecticide with potential benefits for agronomy. It is widely used in agriculture because of its effectiveness in killing pests by disrupting their nervous systems.¹¹ Annually, approximately 140 tons of CYP are sprayed on crops and fruits, with an additional 100 tons used indoors each year to control pests. While CYP provides benefits in agriculture, it also poses detrimental effects on human health. The maximum allowable usage limits for CYP are determined by various regulatory agencies and can differ based on the type of crop and region. For example, in fruits and vegetables, the allowable limit typically ranges from 0.5 mg kg⁻¹ to 1 mg kg⁻¹. In cereal grains, the limit is generally set at 2 mg kg⁻¹, while for leafy vegetables, it can be up to 6 mg kg⁻¹.^12,13 Consequently, early detection of CYP in drinking water and agricultural products is crucial. This highlights the urgent need for a device that can detect CYP in these environments, allowing for the implementation of protective measures to prevent contamination and minimize associated health risks.¹⁴

Electrochemical sensors that use nanomaterials are employed to detect pesticide pollutants in water.¹⁵ Metal–organic frameworks (MOFs),^16,17 carbon materials, and covalent organic frameworks are commonly used in electrochemical sensor devices.¹⁸ Despite their good performance, synthetic methods can be tedious, which limits their applications. In this context, natural polymers present a promising alternative. Kraft lignin (KL), an aromatic biopolymer, is readily available as a major byproduct of the paper and pulp industry.¹⁹ KL has gained significant attention as an ionophore for pesticide sensing due to its aromatic structure and various functional groups, including hydroxyl, carbonyl, and carboxyl groups. These groups enable lignin to form complexes with a wide range of compounds, from metals to pesticides.²⁰ KL is typically an electrical insulator, which limits its use as an electrode material. To enhance the conductivity of such insulating polymers, conductive fillers such as nickel,²¹ iron,²² black carbon, or conductive polymers²³ can be added. The percolation effect, characterized by a sudden increase in the electrical conductivity of a composite by several orders of magnitude, can be achieved by selecting the appropriate filler. Percolation theory describes the transition from insulator to conductor in a two-component composite, where one component creates a conductive pathway through the insulating polymers.²⁴ Carbon-based nanomaterials are attracting significant attention in the field of electrochemical sensors due to their enhanced electrocatalytic capabilities, and may replace current state-of-the-art components and improve practical applications. Graphitic carbon nitride (g-C₃N₄) is a 2D metal-free semiconductor that stands out in electrochemistry due to its large surface area, biocompatibility, thermal stability, and low cost.¹⁷ It is used in various applications, including water splitting, bioimaging, lithium-ion batteries, pollutant degradation, and electrochemical sensors. The presence of sp²-hybridized nitrogen in g-C₃N₄ enhances its electrochemical and photochemical performance by facilitating electron transfer through free electron pairs.^16,25 The incorporation of both nonmetallic and metallic elements into g-C₃N₄ provides additional binding sites, which can modulate the redox band potentials and enhance its sensing capabilities.

In this study, KL was extracted from coconut husk and combined with nickel metal to form a conductive polymer. Subsequently, these KL@Ni nanoparticles were post-modified with g-C₃N₄ to enhance their structural robustness and stability in both basic and acidic environments. Compared to traditional materials, the developed nanohybrids offer significant structural advantages, including the ability to adsorb organic compounds such as pesticides through interactions with the aromatic moieties of KL via dispersion forces. The synthesized KL@Ni@g-C₃N₄ nanohybrids were then used to modify the surface of a graphitic electrode for sensing CYP pesticides. These nanohybrids demonstrated a high capacity for accommodating guest molecules, which enhanced their electrochemical performance. The large surface area of g-C₃N₄ enhances redox activity and allows for detection at very low limits. This study aims to explore the potential of Kraft lignin-based polymers in electrochemical sensors.

2. Experimental

2.1. Chemicals

Sodium hydroxide (NaOH), concentrated sulfuric acid (H₂SO₄), nickel chloride hexahydrate (NiCl₂·6H₂O), hydrazine (N₂H₄), urea, Nafion, sodium dihydrogen phosphate, and sodium hydrogen phosphate were purchased from Sigma-Aldrich and used without further purification. The cypermethrin (CYP) used in this study was sourced from Jullundure (Pvt) Limited, Pakistan. It is a technical grade pyrethroid insecticide with a chemical purity of ≥95%. The specific product used was a cypermethrin spray with a concentration of 100 g L⁻¹ (11.20% w/w).

2.2. Extraction of KL

First, the coconut husk was dried in an oven at 80 °C for 5 hours. After drying, the husk was cut into small pieces of approximately 0.5 cm in size. A 2% NaOH solution was prepared by dissolving 4.3 g of NaOH in 215 mL of distilled water. Then, 5 g of husk powder was mixed with the NaOH solution in a round-bottom flask fitted with a reflux setup, and the extraction was carried out for 4 hours. Following the extraction, the solution was filtered, and the filtrate was acidified with sulfuric acid to a pH of 1. The acidified mixture was then heated to 100 °C, diluted to a 1:5 volume ratio, and allowed to stand overnight. Finally, the precipitates were filtered out and dried in an oven at 105 °C.²⁶

2.3. Synthesis of KL@Ni nanoparticles

To prepare KL@Ni nanoparticles, 0.01 M (0.24 g) of nickel chloride hexahydrate (NiCl₂·6H₂O) was dissolved in 100 mL of distilled water containing 0.5 g of KL. The solution was stirred for 20 minutes, during which the green color of the Ni(II) solution disappeared, and a brown solution appeared, indicating the coordination of Ni(II) ions with KL functional groups. The resulting solution was then stirred for an additional 30 minutes while hydrazine monohydrate (∼2 mL) was added dropwise to control the particle size. Following this, the solution was sonicated for 1 hour. During sonication, the brown color of the solution gradually changed to black, indicating the reduction of Ni(II) ions to Ni(0) nanoparticles. The black precipitates were then separated by centrifugation and washed several times with acetone. The precipitates were dried first in open air and then in an oven at 70 °C for 24 hours. The resulting KL@Ni nanoparticles were obtained as a black powder.²⁷

2.4. Synthesis of g-C₃N₄

g-C₃N₄ was synthesized using a direct low-temperature thermal condensation process from urea. Specifically, 20 g of urea was placed in an alumina crucible, which was then placed in a muffle furnace. The urea was calcined at 560 °C for 4 hours with a heating rate of 5 °C min⁻¹. After the calcination, the product was allowed to cool and then ground into a fine powder.²⁸

2.5. Synthesis of the KL@Ni@g-C₃N₄ hybrid nanocomposite

The synthesis of the KL@Ni@g-C₃N₄ nanocomposite was carried out via the wet-impregnation method, which involves several key steps that result in a well-dispersed and integrated hybrid material.²⁹

2.6. Fabrication of the KL@Ni@g-C₃N₄ modified graphitic electrode (GE)

The GE electrode (surface area 0.16 cm²) was modified with the KL@Ni@g-C₃N₄ hybrid to create a working electrode for sensing CYP pesticide. Before fabrication, the GE was cleaned with distilled water and dried under a light bulb.

To prepare the suspension, 1 mg of the KL@Ni@g-C₃N₄ hybrid composite and 1 μL of Nafion were added to a sample vial, which was then sonicated for 15 minutes to form a slurry. A drop of this slurry was then cast onto the GE. The modified electrode was allowed to dry under a light bulb for 2 hours.

2.7. Characterizations

The synthesized compounds were characterized using several techniques. Fourier Transform Infrared Spectroscopy (FTIR) was employed to examine the surface functional groups of the compounds, utilizing an FTIR spectrometer (Thermo Scientific Nicolet 6700) over a frequency range of 4000–400 cm⁻¹ with a resolution of 4 cm⁻¹. The absorption properties of the samples were analyzed via UV-Visible spectroscopy, using a UV-Vis spectrophotometer (LAMBDA 25) within the wavelength range of 200–800 nm. The phase purity of the prepared compounds was assessed by X-ray Diffraction (XRD) with a SmartLab 3 diffractometer. The morphologies of the prepared materials were investigated using Scanning Electron Microscopy (SEM) with a TESCAN VEGA LMU microscope, while the morphology details were further examined using Transmission Electron Microscopy (TEM) with a JEOL JEM-1230 microscope. Additionally, cyclic voltammetry and electrochemical impedance spectroscopy experiments were performed using an Interface 1010E potentiostat (Gamry Instruments). The KL@Ni@g-C₃N₄-modified GE electrode (KL@Ni@g-C₃N₄@GE) was used for impedance and cyclic voltammetry measurements. All electrochemical measurements were conducted using an Autolab Potentiostat/Galvanostat with Nova software. A three-electrode system was employed, consisting of an Ag/AgCl reference electrode (saturated with KCl), KL@Ni@g-C₃N₄@GE as the working electrode, and platinum wire as the counter electrode. For sensing CYP pesticide in drinking water, a phosphate buffer solution with pH 7 was used. Cyclic voltammetry measurements were performed by varying the pesticide concentrations and scan rates.³⁰

3. Results and discussion

3.1. FT-IR analysis

FTIR is used to confirm the chemical structure of the synthesized nanocomposite. The FT-IR spectra of NiCl₂·6H₂O, g-C₃N₄, KL, KL@Ni nanoparticles, and KL@Ni@g-C₃N₄ hybrid composite are shown in Fig. 1. The FT-IR spectrum of g-C₃N₄ shows a peak at 1607 cm⁻¹, attributed to the CN stretching vibration modes, while peaks at 1241 cm⁻¹, 1325 cm⁻¹, and 1404 cm⁻¹ are due to aromatic C–N stretching. The s-triazine ring modes exhibit a peak at 798 cm⁻¹. The pure KL shows peaks at 3345 cm⁻¹ (broad, ν-OH stretching), 2920 cm⁻¹ (ν-C–H stretching), 1680 cm⁻¹ (ν-CO stretching from carbonyl groups), 1590 cm⁻¹ (ν-CC stretching from aromatic skeletal vibrations), 1412 cm⁻¹ (ν-C–H bending from CH₂ and CH₃ groups), and 1107 cm⁻¹ and 1030 cm⁻¹ (ν-C–O stretching).³¹ The FT-IR spectra of KL@Ni nanoparticles and KL@Ni@g-C₃N₄ hybrid composite clearly preserve all the characteristic peaks of lignin, except for the CO peak, which shifts from 1591 cm⁻¹ to 1531 cm⁻¹ due to ν-C–O–Ni bond stretching.³² The shift in the peak indicates an interaction between the CO groups of lignin and the nickel nanoparticles.³³

Moreover, the FT-IR spectrum of the KL@Ni@g-C₃N₄ hybrid composite shows additional peaks at 1632 cm⁻¹ and 805 cm⁻¹, corresponding to ν-C–N stretching in amide groups and s-triazine ring stretching in the heterocyclic ring, respectively.³⁴ The FT-IR spectrum of pure NiCl₂·6H₂O shows a peak at 791 cm⁻¹, attributed to ν-Ni–O stretching.³⁵ The vibrational peaks of Ni are not prominent in the KL@Ni@g-C₃N₄ hybrid composite, indicating that Ni is embedded within the porous network formed by the host lignin and g-C₃N₄.

3.2. UV-Visible analysis

The UV-visible spectra of NiCl₂·H₂O, g-C₃N₄, KL, KL@Ni NPs, and the KL@Ni@g-C₃N₄ nanocomposite are shown in Fig. 2. NiCl₂·6H₂O exhibits a peak at 430 nm, attributed to the d–d transition in hydrated Ni(II) chloride.²⁷ g-C₃N₄ shows a strong absorbance peak at 320 nm, primarily due to the n–π* transition of the lone pairs of electrons. The KL spectrum exhibits a peak at 276 nm, attributed to the extended conjugation of the benzene rings within the lignin structure.³⁵ In the case of KL@Ni, the peak shifts to 295 nm, indicating the formation of KL-mediated Ni nanoparticles and a redshift in the absorption wavelength.³⁶ In the spectrum of the KL@Ni@g-C₃N₄ nanocomposite, the peak originally observed for KL@Ni nanoparticles is further redshifted to 328 nm due to the interaction with g-C₃N₄. Additionally, g-C₃N₄ itself exhibits a peak at 390 nm.³⁷

3.3. XRD analysis

The phase purity, crystalline size, and crystallinity of NiCl₂·6H₂O, KL, g-C₃N₄, KL@Ni nanoparticles, and the KL@Ni@g-C₃N₄ hybrid composite were characterized by XRD analysis (Fig. 3). The XRD pattern of NiCl₂·6H₂O shows an intense diffraction peak at 42°, which corresponds to the lattice plane (111)³⁸ (JCPDS card no. 03-1051).³⁹ A distinct peak in the XRD pattern of g-C₃N₄ at 27.4° (2θ), corresponding to the diffraction plane (002), confirms the successful synthesis of g-C₃N₄. The diffractogram of KL shows a broad, low-intensity peak at 20.22°, indicating the amorphous nature of the material.⁴⁰ The XRD pattern of KL@Ni nanoparticles exhibits behavior similar to KL, with a 2θ value of 20.22°. It does not show a distinct peak for Ni nanoparticles, which is attributed to the amorphous nature of KL. Similarly, the XRD patterns of KL@Ni and KL@Ni@g-C₃N₄ also do not reveal any peaks corresponding to Ni nanoparticles. This lack of distinct peaks is due to the incorporation of nanoparticles into the extended framework, which typically does not alter the crystal structure of the host material and preserves its integrity and dominance.⁴¹ The lignin and g-C₃N₄ have extended porous networks, which prevents the peaks of Ni nanoparticles from being observed in the obtained XRD spectra. A similar effect was noted in the FT-IR analysis of these samples, as discussed earlier. The characteristic diffraction peaks of the incorporated nanoparticles are obscured by the overlapping signals from lignin and g-C₃N₄. The SEM and EDX study of the KL@Ni NPs and KL@NI@g-C₃N₄ nanocomposite is explained in the ESI† (Fig. S1 and S2 and Tables S1 and S2, respectively). The mean size of the ordered domain affects the incorporation processes of controlled-release agents into lignin. Scherrer's equation was used to determine the average size of the ordered domain (crystallite):

where B is the dimensionless shape factor (typically 0.9), d is the average size of the crystallite, β is the full width at half maximum (FWHM) of the diffraction peak, and λ is the X-ray wavelength (0.154 nm for Cu Kα radiation).^41,42 Table 1 shows the calculated mean size of the ordered domain for lignin is 0.35 nm.

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3.4. TEM study

The TEM images of KL@Ni, as shown in Fig. 4(A) and (B), clearly indicate the uniform distribution of Ni nanoparticles within the 3D network of KL. The images reveal that the Ni nanoparticles possess both spherical and hexagonal morphologies. The average particle size of the Ni nanoparticles ranges from 15 to 20 nm. Additionally, the TEM images of g-C₃N₄ (Fig. 4(C)) demonstrate that the material primarily consists of an irregular layered morphology.⁴³

The TEM images of KL (Fig. 4(D)) demonstrate hollow aggregated spherical particles. To further validate the structure of the synthesized composite material, STEM-EDX elemental mapping was conducted. For the KL@Ni nanoparticles (Fig. 4(E)), the mapping confirms the existence of C, O, Ni, and Na elements. These findings from XRD, TEM, and STEM-EDX studies confirm that Ni nanoparticles are successfully doped into the KL.

From the TEM images of the KL@Ni@g-C₃N₄ nanocomposite (Fig. 5), it is evident that KL@Ni nanoparticles are entangled within the sheets of g-C₃N₄. The STEM-EDX elemental mapping of the KL@Ni@g-C₃N₄ nanocomposite (Fig. 5(D)) confirms the existence of C, O, N, S, Ni, and Na elements.

3.5. Cyclic voltammetry (CV)

Cyclic voltammetry (CV) is a widely employed technique to investigate redox behaviors, electrode processes and reaction mechanisms. In the current study, the fabricated GE has been used to examine the redox property of (K₃[Fe (CN)₆]). Fe(CN)₆ has quasi-reversible redox behavior on the KL@Ni@g-C₃N₄@GE and bare GE (BGE) as shown in Fig. 6(A). The increase in peak current for the fabricated KL@Ni@g-C₃N₄@GE compared to a bare electrode proposed the facile electron transfer across the fabricated electrode. Moreover, the electroactive surface area of the fabricated electrode was calculated via Randles–Sevcik equations (ESI†). It noted that the electroactive surface area of the fabricated KL@Ni@g-C₃N₄@GE is 56.52 mm² and is higher than the bare electrode surface area of 31.52 mm².

3.5. Electron impedance spectroscopy (EIS)

Fig. 6(B) indicates the Randle equivalent circuit model applied to fit EIS data. The charge transfer resistance (R_ct) of the modified or bare electrode corresponds with the diameter of the semicircle in the Nyquist plot. The fabricated electrode (KL@Ni@g-C₃N₄@GE) shows a semicircle of different diameter than BGE indicating different R_ct. The R_ct value of KL@Ni@g-C₃N₄@GE is lower in comparison to BGE, which suggests that the charge transfer resistance of KL@Ni@g-C₃N₄ is less than that of BGE. Hence, the KL@Ni@g-C₃N₄ nanocomposite increases the electron conductivity properties at the electrode surface. The EIS results confirmed that KL@Ni@g-C₃N₄@GE has a higher electron conductivity with a low R_ct compared with that of BGE.⁴⁴ As presented in the circuit (Fig. 6(B) inset), R_s indicates the resistance of the solution, C_dl is the double layer capacitance, R_ct is the charge transfer capacitance, and Zw is the Warburg resistance. The cause of Zw is ascribed to the diffusion controlled process across the electrode–electrolyte, due to the presence of a porous structure in the electrode material.

3.7. Electrochemical detection of CYP pesticide

Due to its electrochemical features, KL@Ni@g-C₃N₄@GE was used to assess its ability to sense CYP pesticide in drinking water. Cyclic voltammetry was recorded in a three-electrode system within the potential window of 0.0–0.6 V to detect pesticide in drinking water with 0.1 M phosphate buffer. At a scan rate of 100 mV s⁻¹, voltammograms were recorded with both BGE and KL@Ni@g-C₃N₄@GE in the absence and presence of CYP pesticides.

In the absence of CYP, the BGE showed no peak (anodic and cathodic) current, whereas KL@Ni@g-C₃N₄@GE displayed an anodic peak current with an I_p value of 19.6 μA. As shown in Fig. 7(A), in the presence of CYP, KL@Ni@g-C₃N₄@GE demonstrated a more sensitive response, with the peak current increasing to an I_p value of 27.4 μA, highlighting the applicability and sensitivity of the fabricated electrode. KL@Ni@g-C₃N₄@GE has a porous structure with numerous functional groups, such as hydroxyl and carbonyl groups, on its surface. These functional moieties can coordinate with CYP pesticide, forming stable complexes that are sensed electrochemically at a positive potential, thus reducing the interference of dissolved O₂. Combining properties such as surface-active functionalities, porous structure, and a high surface-to-volume ratio may enhance the capability of KL@Ni@g-C₃N₄@GE to sense CYP.

Similarly, we recorded the electrochemical responses of g-C₃N₄, KL, and Ni nanoparticles using CV. The observed peak current values were 12.3 μA for g-C₃N₄ (entry 3 in Fig. 7(B)), no current peak for KL (entry 5 in Fig. 7(B)), and 15.7 μA for Ni nanoparticles (entry 6 in Fig. 7(B)). This data demonstrates that these materials cannot efficiently sense CYP when used individually.

Additionally, we investigated the ability of KL@Ni@g-C₃N₄@GE to selectively sense CYP in the presence of other interferents, including methomyl, pirimicarb, carbofuran, chlorturon, and mesotrione, which are widely used pesticides. The peak current values for these pesticides were found to be 23.1 μA, 20.4 μA, 22.99 μA, 19.4 μA, and 16.7 μA, respectively (entry 8–12 in Fig. 7(B)). The decreased current values for these pesticides, compared to the current value for CYP, clearly confirm that KL@Ni@g-C₃N₄ is more efficient in sensing CYP than other molecules.

Our fabricated material, KL@Ni@g-C₃N₄@GE, features a porous structure, large surface-to-volume ratio, and multiple functionalities (OH, CO), enhancing its capability for facilitated electron transfer even in a non-conductive environment.⁴⁵ Both the CYP pesticide and the KL@Ni@g-C₃N₄ contain aromatic rings, enabling π–π stacking (non-covalent interaction) between them, allowing for easy detection of the analyte in drinking water.⁴⁶

3.8. Effect of scan rate

Fig. 7(C) shows the effect of the scan rate on the KL@Ni@g-C₃N₄@GE fabricated electrode at a different scan (0.5–0.4 V s⁻¹) in 1 μg mL⁻¹ CYP pesticide containing 0.1 M phosphate buffer solution (pH 7.0). The oxidation peak current of CYP pesticide increased linearly with different scan rates.⁴⁷ The linearity confirmed the diffusion-controlled process in the plot of the square root of the scan rate and oxidation peak current, as shown in Fig. 7(D).⁴⁸

3.9. Calibration curve and detection limits

The CYP pesticide solution was prepared at different concentrations to determine the detection limits. Fig. 8(A) explains the cyclic voltammograms for different concentrations of pesticides. It confirmed a linear increase in the oxidation current peak with the increase in analyte concentration (Fig. 8(B)). The detection limit of the fabricated electrode is determined as follows: LOD = 3S.D_b/m. Here 3S.D_b is the standard deviation of the blank, and m is the slope of a calibration curve. The fabricated electrode presented the smallest detection limit of 0.026 μg mL⁻¹. Table 2 provides information of the electrode materials reported so far, employed for the detection of several pesticides using the electrochemical sensing approach. From the tabulated data, it is quite evident that our fabricated electrode presents very good performance and is comparable with the reported materials. From the tabulated data, it is clear that our fabricated electrode, with a detection limit of 0.026 μg mL⁻¹ for CYP, exhibits superior performance compared to other reported materials. For instance, the CPM-PMMA/PCB electrode detects CYP within a range of 1–100 μg mL⁻¹ with a higher LOD of 0.34 μg mL⁻¹, indicating that our electrode is more sensitive and suitable for detecting lower concentrations of CYP. Similarly, the anatase TiO₂/CPE electrode, with a LOD of 0.1 μg mL⁻¹, shows less sensitivity compared to our electrode.

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Moreover, while the PMMA/AuNPs/APTAMER electrode detects malathion with a LOD of 3.3 μg mL⁻¹, and the dsCT/DNAPPy/PVS electrode detects malathion with a LOD of 0.17 μg mL⁻¹, the performance of our material in detecting CYP is notably more sensitive. The DMMIP-Ag-N@ZnO/CHAC electrode, despite having an exceptionally low LOD of 6.7 × 10⁻¹⁴ M, demonstrates a more complex fabrication process compared to our KL@Ni@g-C₃N₄ nanocomposite, which offers a practical and efficient approach for CYP detection.

These comparisons emphasize the effectiveness of our fabricated electrode in terms of sensitivity and practicality. The incorporation of KL, Ni nanoparticles, and g-C₃N₄ into the nanocomposite significantly enhances its electrochemical properties, making it a promising candidate for pesticide detection in environmental monitoring and food safety applications.

3.10. Electrochemical sensing mechanism of CYP using the KL@Ni@g-C₃N₄ nanocomposite

CYP is a pyrethroid insecticide characterized by the chemical structure C₂₂H₁₉C_l2NO₃. A key functional group in CYP is the nitro group (NO₂) attached to the benzene ring. This nitro group is susceptible to electrochemical reduction, which is pivotal for its detection. CYP molecules adsorb onto the surface of the KL@Ni@g-C₃N₄ nanocomposite. Upon the application of a potential, electrons are transferred from the electrode to the nitro group and the benzene ring of CYP through the conductive network of g-C₃N₄. The nitro group undergoes stepwise reduction, initially forming a nitroso group (NO), followed by hydroxylamine (NHOH), and eventually an amine (NH₂) under specific conditions. The reduction steps can be represented as follows:

R-NO2 + 4H+4e− → R-NHOH + H2O → R-NH2 + 2H2O

The benzene ring can undergo oxidation resulting in the formation of phenolic compounds or quinones. The oxidation reaction can be simplified as:

C6H5-R → C6H4-OH + 2H+ + 2e−

The electron transfer during these redox reactions generates a measurable current. This current is directly proportional to the concentration of CYP in the solution. The synergy between KL, Ni nanoparticles, and g-C₃N₄ within the nanocomposite enhances the adsorption and electron transfer processes, thereby improving the sensor's sensitivity and specificity.^14,52–54

3.11. Real sample analysis of CYP for practical applicability

The feasibility of the designed electrocatalyst for sensing CYP was tested using tap water samples. The determination of CYP in the water sample was performed using the spiking method. The results, as shown in Table 3, demonstrate accuracy with recovery rates ranging from 89.0% to 98.1%. The relative standard deviation (RSD) values are below 8.94%, indicating the precision and reliability of the electrocatalyst for CYP determination. These results illustrate the practical applicability of the electrocatalyst in real-world water analysis scenarios.

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3.12. Reusability and stability of KL@Ni@g-C₃N₄@GE

The stability and reusability are key features of electrochemical sensors. In the current study, the stability of KL@Ni@g-C₃N₄@GE was assessed by detecting the CYP pesticide at 2 concentrations over 15 cycles. It can be seen from Fig. 9(A) that the intensity of the current remains almost constant. Furthermore, GE is stored for 14 days. After that, voltammograms were recorded, and the preserved GE showed almost the same current response and maintains 97% of the initial current, proposing the good stability of GE (Fig. 9(B)).

4. Conclusions

In conclusion, non-enzymatic electrochemical sensing of cypermethrin (CYP) pesticide has been successfully demonstrated using a KL@Ni@g-C₃N₄ nanocomposite-based electrode. The KL@Ni@g-C₃N₄ nanocomposite was synthesized via a wet-impregnation technique, and its physical, structural, and morphological properties were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction, and transmission electron microscopy. The KL@Ni@g-C₃N₄@GE electrode was employed for potentiometric sensing of CYP in drinking water and exhibited high sensitivity with a limit of detection (LOD) of 0.026 μg mL⁻¹. This study highlights the potential of conducting polymer composites based on KL for detecting pesticides at trace levels. Additionally, the developed sensor demonstrated reusability, stability, and repeatability. Therefore, it shows promise for future development into a portable device for pesticide detection in real-world settings.

Author contributions

Shumaila Razzaque, Toheed Akkhter, Muhammad Abubakar: writing – original draft, methodology, Sadaf-ul-Hassan: conceptualization and supervision, Muhammad Asim Farid, Rehana Zia, Humaira Razzaque, Abid Ali, Waheed Al-Masry, and Asif Mahmood: writing – review & editing, Shahid Nazir and Zulfiqar Ali: software and formal analysis.

Data availability

All data supporting the findings of this study are included within the manuscript and its ESI.†

Conflicts of interest

The authors declare that they have no conflicts of interest.

Acknowledgements

The authors are highly thankful for the support of COMSATS University Islamabad, Lahore Campus, and UMT, Lahore Pakistan. The authors would like to acknowledge the Researcher's Supporting project number (RSP2024R43), King Saud University, Riyadh, Saudi Arabia.

References

1. A. Zul, O. A. La and N. Muhammad, Orient. J. Chem., 2017, 33, 1164–1170 CrossRef .
2. M. K. Alam, World, 2013, 1, 30–35 Search PubMed .
3. A. P. Periasamy, Y. Umasankar and S.-M. Chen, Sensors, 2009, 9, 4034–4055 CrossRef CAS PubMed .
4. V. Dhananjayan and B. Ravichandran, Curr. Opin. Environ. Sci. Health, 2018, 4, 31–37 CrossRef .
5. R. Kaur and J. Singh, Nat. Environ. Pollut. Technol., 2021, 20, 1997–2005 CAS .
6. A. Wong, T. A. Silva, F. R. Caetano, M. F. Bergamini, L. H. Marcolino-Junior, O. Fatibello-Filho and B. C. Janegitz, C, 2017, 3, 8 Search PubMed .
7. R. Ramachandran, T.-W. Chen, S.-M. Chen, T. Baskar, R. Kannan, P. Elumalai, P. Raja, T. Jeyapragasam and K. Dinakaran, Inorg. Chem. Front., 2019, 6, 3418–3439 RSC .
8. G. Hanrahan, D. G. Patil and J. Wang, J. Environ. Monit., 2004, 6, 657–664 RSC .
9. O. Kanoun, T. Lazarević-Pašti, I. Pašti, S. Nasraoui, M. Talbi, A. Brahem, A. Adiraju, E. Sheremet, R. D. Rodriguez and M. Ben Ali, Sensors, 2021, 21, 4131 CrossRef CAS PubMed .
10. X. Tian, L. Liu, Y. Li, C. Yang, Z. Zhou, Y. Nie and Y. Wang, Sens. Actuators, B, 2018, 256, 135–142 CrossRef CAS .
11. E. K. Janghel, J. K. Rai, M. K. Rai and V. K. Gupta, J. Braz. Chem. Soc., 2007, 18, 590–594 CrossRef CAS .
12. P. Leepheng and D. Phromyothin, Jpn. J. Appl. Phys., 2019, 58, SDDG02 CrossRef CAS .
13. M. S. Nazir, Z. Tahir, S. U. Hassan, Z. Ali, M. N. Akhtar, K. Azam and M. A. Abdullah, Sustainable Agriculture Reviews 47: Pesticide Occurrence, Analysis and Remediation Vol. 1 Biological Systems, 2021, pp. 271–307 Search PubMed .
14. M. Nurdin, M. Maulidiyah, M. Z. Muzakkar and A. A. Umar, Microchem. J., 2019, 145, 756–761 CrossRef CAS .
15. S. Viswanathan and P. Manisankar, J. Nanosci. Nanotechnol., 2015, 15, 6914–6923 CrossRef CAS PubMed .
16. P. Geng, L. Wang, M. Du, Y. Bai, W. Li, Y. Liu, S. Chen, P. Braunstein, Q. Xu and H. Pang, Adv. Mater., 2022, 34, 2107836 CrossRef CAS PubMed .
17. T. Chen, F. Wang, S. Cao, Y. Bai, S. Zheng, W. Li, S. Zhang, S. X. Hu and H. Pang, Adv. Mater., 2022, 34, 2201779 CrossRef CAS PubMed .
18. M. Asif, H. Sajid, K. Ayub, M. Ans and T. Mahmood, J. Phys. Chem. Solids, 2022, 160, 110345 CrossRef CAS .
19. D. López Serna, P. Elizondo Martínez, M. Á. Reyes González, A. A. Zaldívar Cadena, E. A. Zaragoza Contreras and M. G. Sánchez Anguiano, Polymers, 2019, 11, 1080 CrossRef PubMed .
20. A. Rudnitskaya, D. V. Evtuguin, L. C. Costa, M. P. F. Graça, A. J. S. Fernandes, M. R. P. Correia, M. T. S. R. Gomes and J. Oliveira, Analyst, 2013, 138, 501–508 RSC .
21. S. W. Phang, M. Tadokoro, J. Watanabe and N. Kuramoto, Polym. Adv. Technol., 2009, 20, 550–557 CrossRef CAS .
22. T. Sharma and M. Garg, rans. Electr. Electron. Eng., 2021, 22, 394–418 Search PubMed .
23. S. G. Chen, J. W. Hu, M. Q. Zhang, M. Z. Rong and Q. Zheng, Sens. Actuators, B, 2006, 113, 361–369 CrossRef CAS .
24. F. A. C. Faria, D. V. Evtuguin, A. Rudnitskaya, M. T. S. R. Gomes, J. A. B. P. Oliveira, M. P. F. Graça and L. C. Costa, Polym. Int., 2012, 61, 788–794 CrossRef CAS .
25. U. Rajaji, S. V. Selvi, S.-M. Chen, S. Chinnapaiyan, T.-W. Chen and M. Govindasamy, Microchim. Acta, 2020, 187, 1–10 CrossRef .
26. S. Pandharipande, M. Gujrati, N. Mulkutkar and S. Pandey, Int. J. Eng. Sci. Res. Technol., 2018, 7, 659–666 Search PubMed .
27. M. M. Bharamanagowda and R. K. Panchangam, ChemistrySelect, 2022, 7, e202103793 CrossRef .
28. M. A. Qamar, S. Shahid, M. Javed, S. Iqbal, M. Sher, A. Bahadur, M. M. Al-Anazy, A. Laref and D. Li, Colloids Surf., A, 2021, 614, 126176 CrossRef .
29. K. V. Savunthari, D. Arunagiri, S. Shanmugam, S. Ganesan, M. V. Arasu, N. A. Al-Dhabi, N. T. L. Chi and V. K. Ponnusamy, Chemosphere, 2021, 272, 129801 CrossRef .
30. G. Bhanjana, N. Mehta, G. R. Chaudhary, N. Dilbaghi, K.-H. Kim and S. Kumar, J. Mol. Liq., 2018, 264, 198–204 CrossRef CAS .
31. Q. Shen, T. Zhang and M.-F. Zhu, Colloids Surf., A, 2008, 320, 57–60 CrossRef CAS .
32. F. Kong, K. Parhiala, S. Wang and P. Fatehi, Eur. Polym. J., 2015, 67, 335–345 CrossRef CAS .
33. Q. Yan, C. R. Boardman and Z. Cai, Thermochim. Acta, 2020, 690, 178659 CrossRef CAS .
34. W. Li, Q. Chen and Q. Zhong, J. Mater. Sci., 2020, 55, 10712–10724 CrossRef CAS .
35. A. Rahdar, M. Aliahmad and Y. Azizi, 2015.
36. A. Bathla and B. Pal, ChemistrySelect, 2018, 3, 4738–4744 CrossRef CAS .
37. R. A. C. Gomide, A. C. S. de Oliveira, D. A. C. Rodrigues, C. R. de Oliveira, O. B. G. de Assis, M. V. Dias and S. V. Borges, J. Polym. Environ., 2020, 28, 1326–1334 CrossRef CAS .
38. S. Yin, C. Li, S. Wang, X. Ren, L. Zeng and L. Zhang, Dalton Trans., 2020, 49, 8036–8040 RSC .
39. V. Charles, Y. Yang, M. Yuan, J. Zhang, Y. Li, J. Zhang, T. Zhao, Z. Liu, B. Li and G. Zhang, New J. Chem., 2021, 45, 14822–14830 RSC .
40. S. Kumar, Y. S. Negi and J. S. Upadhyaya, Adv. Mater. Lett., 2010, 1, 246–253 CrossRef .
41. A. Goudarzi, L.-T. Lin and F. K. Ko, J. Nanotechnol. Eng. Med., 2014, 5, 021006–021011 CrossRef .
42. K. Sun, J. Shen, Q. Liu, H. Tang, M. Zhang, S. Zulfiqar and C. Lei, Chin. J. Catal., 2020, 41, 72–81 CrossRef CAS .
43. M. Govindasamy, S. Sakthinathan, S.-M. Chen, T.-W. Chiu, A. Sathiyan and J. P. Merlin, Electroanalysis, 2017, 29, 1950–1960 CrossRef CAS .
44. S. Razzaque, K. Wang, I. Hussain and B. Tan, Chem. – Eur. J., 2019, 25, 548–555 CrossRef CAS PubMed .
45. S. Vinoth, M. Govindasamy, S.-F. Wang and S. Anandaraj, Ultrason. Sonochem., 2020, 64, 105036 CrossRef CAS .
46. M. Keerthi, M. Akilarasan, S.-M. Chen, S. Kogularasu, M. Govindasamy, V. Mani, M. A. Ali, F. M. A. Al-Hemaid and M. S. Elshikh, J. Electrochem. Soc., 2018, 165, B27 CrossRef CAS .
47. D. Song, Y. Wang, X. Lu, Y. Gao, Y. Li and F. Gao, Sens. Actuators, B, 2018, 267, 5–13 CrossRef CAS .
48. C. S. Kushwaha and S. K. Shukla, J. Mater. Sci., 2019, 54, 10846–10855 CrossRef CAS .
49. Y. Li, L. Zhang, Y. Dang, Z. Chen, R. Zhang, Y. Li and B.-C. Ye, Biosens. Bioelectron., 2019, 127, 207–214 CrossRef CAS .
50. A. Gupta, N. Prabhakar, R. Singh, A. Kaushik and B. D. Malhotra, Thin Solid Films, 2010, 519, 1122–1127 CrossRef CAS .
51. M. Díaz-Álvarez and A. Martín-Esteban, Biosensors, 2021, 11, 79 CrossRef .
52. J.-M. Moon, N. Thapliyal, K. K. Hussain, R. N. Goyal and Y.-B. Shim, Biosens. Bioelectron., 2018, 102, 540–552 CrossRef CAS .
53. H. A. Saputra, Monatsh. Chem., 2023, 154, 1083–1100 CrossRef CAS .
54. J. Zhao, Y. Jiang, L. Gong, X. Chen, Q. Xie, Y. Jin, J. Du, S. Wang and G. Liu, Chem. Eng. J., 2022, 430, 132961 CrossRef .

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4tb00951g

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