Detection Of Pesticides Research Articles

Application of Carbon Quantum Dots Derived from Waste Tea for the Detection of Pesticides in Tea: A Novel Biosensor Approach

Application of Carbon Quantum Dots Derived from Waste Tea for the Detection of Pesticides in Tea: A Novel Biosensor Approach

Unravelling the potential of bismuth ferrite nanoparticles for electrochemical pesticide detection

Unravelling the potential of bismuth ferrite nanoparticles for electrochemical pesticide detection

Ultrasensitive electrochemical detection of thymol using areca nut fibre-derived carbon sensors for environmental protection

The increasing use of thymol (THY) as a common pesticide in agriculture raises significant concerns regarding its potential environmental and human health risks. Therefore, developing a sensitive detection method for thymol residues is essential for monitoring and ensuring safety standards. This study presents a novel approach for sensitively detecting thymol using activated carbon derived from areca nut (Arecanut catechu L) fibre, an agricultural by-product as the precursor. Cetyltrimethylammonium bromide (CTAB) is added to the test solution to improve the material's surface properties and increase the number of active sites. Electrochemical sensing techniques, specifically cyclic voltammetry (CV) and square wave voltammetry (SWV), are employed to evaluate the performance of Cetyltrimethylammonium bromide mediated activated carbon as a thymol detection sensor. The sensor exhibits outstanding sensitivity with an ultralow limit of detection (DL) of 1.84 × 10⁻⁸ M and a limit of quantification (QL) of 6.14 × 10⁻⁸ M, enabling the quantification of trace amounts of thymol in complex matrices. Furthermore, the sensor demonstrates exceptional reproducibility and long-term stability, making it suitable for real-time applications. Utilizing areca nut fibre as a precursor represents a sustainable and eco-friendly approach. This method offers a promising solution for pesticide detection, contributing to enhanced food safety, environmental protection, and regulatory compliance in the agricultural sector.

Sustainable analytical approach for selective fluorescence sensing of sulfosulfuron using copper nanoclusters from Tinospora cordifolia leaves extract

In recent times, there has been a growing trend in utilizing medicinal plant extracts for the fabrication of fluorescent nanomaterials. In this work, Tinospora cordifolia-copper nanoclusters (T. cordifolia-CuNCs) were produced by employing Tinospora cordifolia (common name is “giloy”), a medicinal plant. A green chemistry approach was employed to generate blue fluorescent T. cordifolia-CuNCs, displaying λEm at 430 nm when λEx at 330 nm, which shows a good quantum yield (QY) of 26.67 %. Sulfosulfuron pesticide was able to quench the fluorescence intensity of T. cordifolia-CuNCs via a “turn-off” mechanism. It was noticed that T. cordifolia-CuNCs could be used for the detection of sulfosulfuron pesticide in the range of 0.025–90 µM with a detection limit of 6.52 nM. Furthermore, a cellulose-based paper strip sensor was created for thevisual detection of sulfosulfuron pesticide. Moreover, T. cordifolia-CuNCs-based fluorescence method was applied to quantify sulfosulfuron pesticide in apple, tomato, and rice samples, showing good recoveries, which demonstrates that this probe offers great potentiality for sensing of sulfosulfuron pesticide in food and environmental samples

Parallel Detection of Mixed Pesticides Based on Dual Quantum Dot/Porous Silicon Optical Biosensors

Parallel Detection of Mixed Pesticides Based on Dual Quantum Dot/Porous Silicon Optical Biosensors

Selective colorimetric detection of carbosulfan based on its hydrolysis behavior and Ti3C2/AuPt nanozyme

BackgroundCarbosulfan (CBS) is a widely used carbamate pesticide in agricultural production, its easy decomposition into hypertoxic carbofuran poses serious threats to human health and food safety. Therefore, sensitive and accurate detection of CBS is of significant importance. Conventional chromatography-based techniques require expensive instruments and complicated sample pretreatment, limiting their application for fast detection. Current electrochemical and colorimetric methods for detection of pesticides based on the cascade catalytic reactions between acetylcholinesterase (AChE) and nanozymes, which exhibit inferior selectivity. Hence, selective, sensitive and fast detection of CBS is still challenging. ResultsIn this work, an AChE-free colorimetric method was proposed for selective detection of CBS based on its unique hydrolysis behavior and nanozyme. Ti3C2 nanosheets/AuPt nanoparticles (Ti3C2/AuPt NPs) with enhanced peroxidase-like activity were prepared via one-step self-reduction reaction. CBS can be hydrolyzed under acidic condition and produce -SH moieties, which could bond to Pt atoms of Ti3C2/AuPt NPs and shield the active sites of nanozyme, resulting in decreased catalytic activity. Based on the inhibitory effect on the peroxidase-like activity of Ti3C2/AuPt NPs, a colorimetric method was proposed for direct detection of CBS. Under optimal conditions, the method showed wide linear range (0.5 ng mL−1-5 μg mL−1), low limit of detection (0.342 nM), good selectivity and anti-interference ability. The feasibility of this method for practical use was confirmed by analysis of CBS in real lake water samples. SignificanceThis work proposed a simple colorimetric method for selective and fast detection of CBS, which avoided employing AChE and cascade catalytic reactions, significantly lowering the detection cost and improving detection efficiency. The method showed great potential for accurate detection of CBS in actual samples, and provided a new avenue for developing nanozyme-based colorimetric method for detection of other pesticide residues.

Artificial intelligence-driven tool for spectral analysis: identifying pesticide contamination in bees from reflectance profiling

Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides—dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)—as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98% accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27seconds. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control.

Significantly enhancing SERS sensitivity to distinguish pesticide concentrations at ppm levels in juices through inherent and added internal standards

BackgroundSurface-enhanced Raman scattering (SERS) aids in pesticide detection, including thiram. The U.S. Environmental Protection Agency has set a 7 ppm residue standard specifically for thiram. Nonetheless, conventional SERS methods often lack sensitivity at ppm levels. We introduce internal standards to overcome this limitation. MethodsInitially, we assessed thiram concentrations in apple and orange juices using the SERS peak at 1383 cm-1, denoted by its intensity as S1383. However, S1383 alone lacked sensitivity to ppm level variation. Apple juice exhibited a SERS peak at 732 cm-1 (intensity denoted as S732), serving as an inherent internal standard. The ratio of S1383 to S732 could differentiate thiram concentrations by ∼1 ppm within the range of 1 × 10-5 to 3 × 10-5 M. Pure orange juice lacked a discernible SERS peak, necessitating an added internal standard. Adenine, with SERS intensity at 731 cm-1 (S731) and 1322 cm-1 (S1322), was employed for this purpose. The combined use of S1383 and the ratios of S1383 to S731 and S1383 to S1322 could distinguish thiram concentration differences by ∼1 ppm within the range of 1 × 10-5 to 5 × 10-5 M. Significant findingsOur approach facilitates exceptional differentiation at the ppm level, enhancing pesticide detection for improved food safety and environmental health.

A simple, rapid, and low-cost approach for colloidal nanoparticle-based surface enhanced Raman Scattering detection of endosulfan pesticide at trace levels

Abstract Surface Enhanced Raman Spectroscopy (SERS) is a highly specific, sensitive, and portable technique with great potential for on-site pesticide detection and monitoring. Endosulfan, an organochlorine pesticide known for its high toxicity, slow degradation, and bioaccumulation, has poor affinity for metallic SERS substrates. This study presents a label-free SERS detection method for endosulfan, using aggregating agents like potassium chloride (KCl), potassium hydroxide (KOH), potassium bromide (KBr), and sodium hydroxide (NaOH) to modify the behavior of Ag colloidal nanoparticles (NPs) and enhance the SERS signal of endosulfan molecules trapped within formed hot-spots. We analyzed the UV–Vis spectra, the hydrodynamic diameter, and zeta-potential of Ag NPs with the addition of these agents and endosulfan. Successful detection of both α- and ß- endosulfan isomers at μM concentrations in both ethanol and methanol was achieved with KOH-treated Ag NPs. The method was also applied to detect endosulfan in real water samples, along with simultaneous detection of λ-cyhalothrin, showcasing its capability to identify multiple analytes. The selectivity and specificity were confirmed using a mixture of endosulfan and thiabendazole, highlighting the crucial role of selecting the appropriate aggregating agent for each analyte. Overall, the findings emphasize the potential of aggregating agents to mediate the SERS enhancement of endosulfan, facilitating simple and rapid protocols for environmental pollutant detection, while shedding light on the intricate interplay between NP behavior, surface chemistry, and analyte interaction.

Enhancing the photo-induced oxidase-like activity of fluorescein with methyl viologen for colorimetric detection of organophosphorus pesticide

Overcoming the intrinsic low activity of most photoinduced oxidase mimics has been extremely challenging. In this work, we developed a methyl viologen (MV2+) mediated strategy to enhance the oxidase-like activity of fluorescein. The presence of MV2+ gives it a high affinity for TMB with a low Michaelis-Menten constant (Km) of 0.053 mM, which is about 2.8 times lower than that of fluorescein and with a remarkable catalytic constant (Kcat) as 0.2490 s−1, which is 3 times as high as that of fluorescein. Fluorescein diacetate (FDA) without oxidase-like activity can be hydrolyzed in situ to produce fluorescein in the presence of carboxylesterases (CaE). Based on the inhibition of CaE activity by organophosphorus pesticides (OP), a novel colorimetric signal biosensor was established with a wide linear range from 1.0 to 200 ng/mL. This work not only provides a convenient and feasible strategy for enhancing the activity of photoinduced oxidase mimics but also blazes a new pathway for the sensitive detection of OP.

Highly sensitive colorimetric method for the detection of organophosphorus pesticides based on H2O2 etching of silver-coated gold nanostars

The residues of organophosphorus pesticides in agricultural production and soil constitute a significant threat to both human health and ecosystems, underscoring the critical importance of developing effective detection methods for these pesticides. Here, we introduce an innovative method for detecting trichlorfon that integrates hydrogen peroxide (H2O2)-induced etching of silver-coated gold nanostars (AuNS@Ag) with an enzymatic reaction cascade. By precisely controlling the thickness of the silver layer and the morphology of the gold nanoparticle core, we enhance the sensitivity of the H2O2 etching process. In this detection system, acetylcholinesterase (AChE) catalyzes the hydrolysis of acetylcholine chloride (ACh) into choline, which is then oxidized by choline oxidase (ChOx) to produce H2O2. The generated H2O2 subsequently etches the silver layer of AuNS@Ag, leading to a pronounced red-shift in the localized surface plasmon resonance (LSPR) wavelength, from 590 nm to 735 nm. However, the presence of trichlorfon inhibits the activity of AChE, resulting in a reduced production of H2O2 and a subsequent attenuation of the LSPR wavelength shift. Under optimized experimental conditions, this method enables the linear detection of trichlorfon within a concentration range of 0.1–5.0 μg mL−1 and has been successfully applied to detect trichlorfon in cabbage and tap water samples, showcasing its promising potential in food safety and environmental monitoring.

L-Cysteine-Modified Carbon Dots Derived from Hibiscus rosa-sinensis for Thiram Pesticides Identification on Edible Perilla Leaves.

In this work, environmentally friendly fluorescent carbon dots (C-dots) were developed for the purpose of thiram identification in the leaves of perilla plants. Powdered plant petals from Hibiscus rosa-sinensis were hydrothermally combined to create C-dots. Analytical techniques, such as scanning electron microscopy, energy dispersive X-ray spectroscopy, high resolution transmission electron microscopy, Raman spectroscopy, ultraviolet spectroscopy, Fourier transmission infrared spectroscopy, and photoluminescence were employed to examine the properties of C-dots. To enhance their functionality, an l-cysteine dopant was added to the C-dots. Since this process produces highly soluble C-dots in water, it is simple, inexpensive, and safe. The excitation process and the size of the blue luminescent C-dots both affect their photoluminescent activity. Furthermore, thiram in aqueous solutions was effectively identified by using the generated C-dots. Additionally, the ImageJ program was used to measure the colors red, green, and blue. High-resolution TEM (HR-TEM) revealed that the l-cysteine-doped carbon dots had an average particle size of 2.208 nm. Additionally, the lattice fringes observed in the HRTEM image showed a d-spacing of around 0.285 nm, which nearly corresponds to the (100) lattice plane of graphitic carbon. A Raman spectrum study was also performed to investigate the relationship between carbon dots and pesticides in the actual samples. In the end, thiram levels in perilla leaves with nondoped and doped C-dots could be distinguished with 100% accuracy using the constructed partial least-squares discriminant analysis machine learning model. The information gathered therefore demonstrated that the synthetic C-dots successfully and efficiently provide rapid and sensitive detection of hazardous pesticides in edible plant products.

Highly Water-Stable 2D MOF as Dual Sensor for the Ultra-Sensitive Aqueous Phase Detection of Nitrofuran Antibiotics and Organochlorine Pesticides.

Misuse of antibiotics and pesticides has led to hazardous effects on human health, livestock, agriculture, and aquaculture, which urges researchers to find simple, rapid, efficient, and cost-effective methods for quick on-site analysis of these organic pollutants with functional materials. Herein, a 2D chemically robust MOF: IITKGP-71, {[Cd(MBPz)(2,6-NDC)]·2H2O}n is strategically developed with ease in scalability and exploited as dual sensors toward the toxic antibiotic and pesticide detection via luminescence quenching in aqueous medium. The framework displays exceptional chemical robustness in water for 3 months, in an open atmosphere over 2months, and wide range of aqueous pH solution (pH = 3-12) for a day. IITKGP-71 can selectively quench the nitrofuran antibiotics (NFZ and NFT) and organochlorine pesticide DCN while remaining unaffected by other interfering antibiotics and pesticides, respectively. An excellent trade-off between high effectivity (high Ksv) and high sensitivity (low LOD)was achieved for the targeted analytes. The easy scalability, high chemical stability, fast responsivity, multi-responsive nature, recyclability with outstanding structural stability made this framework viable in playing a crucial role in safeguarding aquatic ecosystems and public health from the hazardous effects of antibiotics and pesticides.

Ultrasensitive and rapid detection of organophosphates using a dual-signal naked-eye hydrogel sensor based on acetylcholinesterase inhibition

Accurate determination of pesticide residues is crucial for food safety. A self-calibration method was developed for dual-signal “naked-eye” detection of organophosphorus pesticides (OPs) using bifunctional gold nanozymes (AuNEs). OPs inhibit the cascade reaction of acetylcholinesterase/choline oxidase (AChE/CHO) to reduce hydrogen peroxide (H2O2) production, which affects the AuNE-catalyzed color reaction and quenches the fluorescence of AuNEs with the assistance of Fe2+. The multi-enzyme cascade system can be integrated into hydrogel sheets for smartphone-assisted digital, quantitative, and onsite detection of OPs in real food samples by analyzing the linear changes in colorimetric and fluorometric signals, which closely match liquid chromatography-mass spectrometry (LC-MS) results. This method displays a lowest ‘naked-eye’ detection concentration of 7 ppb, a wide linear range (0–2000 ng/mL), and particularly the advantage of anti-interference via self-calibration when one of the signals is abnormal.

Flexible paper-based Ag dendritic SERS chips for rapid in situ detection of thiram residues on pear skin.

Surface-enhanced Raman scattering (SERS) is a powerful, highly efficient analytical technique capable of providing label-free, non-invasive, rapid, and ultrasensitive molecular detection down to the single-molecule level. Despite its advantages, SERS remains largely confined to laboratory settings due to the complexities of substrate fabrication and challenges in analyzing real-world samples. Developing flexible SERS substrates that achieve both high fabrication efficiency and high sensing performance, while being practical for field applications, is critical for advancing SERS toward broader, real-world use. In this study, we present a novel paper-based Ag dendritic SERS chip, fabricated via a simple chemical reduction process that directly forms Ag dendritic nanostructures on cellulose fibers. This chip substrate demonstrates exceptional sensitivity for the detection of thiram pesticide, with a detection limit as low as 7.76 × 10-11 M. The chip substrate also exhibits outstanding reliability, with reproducibility and repeatability both less than 5%. Furthermore, the flexible nature of the paper substrate enables it to conform to curved surfaces and be in direct contact with analytes, exemplified by its ability to adhere to and retrieve thiram from pear skin using a novel "paste-and-peel-off" technique. The substrate shows remarkable performance for thiram detection on pear skin, with sharp recovery rates ranging from 90% to 105%. With its facile fabrication, excellent sensitivity, high reliability, and practical applicability in non-invasive sampling, the paper-based Ag dendritic SERS substrate offers significant potential as an advanced substrate to bring SERS out of the laboratory and closer to real-world applications.

Optimization of Acetylcholinesterase and Sucrose Concentration using Response Surface Methodology (RSM) Approach in the Development of Paper-Based Biosensor for Pesticides Detection

Excessive use of pesticides, particularly organophosphate pesticides (OPs), to increase agricultural productivity may give a risk to consumers if consumed as these pesticides are neurotoxic substances. To increase agricultural productivity, fruits and vegetables are frequently treated with excessive pesticides, particularly organophosphate pesticides (OPs). Several methods have been studied and developed to identify pesticide residue in crops, however the standard laboratory procedures for OPs analysis involves the use of HPLC or GC-MS. These methods are complex in sample preparation and require expert labor to operate. Paper-based biosensors are low-cost platforms for fast detection in the field, offering an easy and reusable option. In this study, a colorimetric paper-based biosensor based on immobilizing acetylcholinesterase (AChE) using sol-gel silica matrices and indoxyl acetate is developed. The Response Surface Methodological (RSM) approach was used to optimize AChE concentration and sucrose as a stabilizer, allowing simultaneous optimization of multiple variables using smaller-size datasets. This study used the central composite design (CCD) fractional factorial design using Design Expert 7.0 software. A quadratic model was selected to represent both immobilization yield and relative AChE activity responses. The model was evaluated using ANOVA values and Lack of Fit test which confirmed that the quadratic models for both responses are suitable for experimental data. Validation of the model demonstrates that the predictive model accurately represents of the validation research. The Limit of Detection (LoD) of the biosensor for pure profenofos pesticide after a 20-minute incubation time is 1 ppm, as indicated by a 32.55 % decrease in color intensity. After a week of storage at 4 °C, the biosensor showed a loss of 5.92 % on immobilization yield and 2.55 % on relative activity indicating a good storage stability. The mean value of lettuce samples was 200.60 ± 1.2 a.u, with pesticide concentration of 2.57 ± 1.2 ppm (n=3). HIGHLIGHTS Incorporating AChE into the sol gel matrices is a great way to enhance the stability of the enzymatic colorimetric pesticide detection. Response Surface Methodology (RSM) is considered as useful for optimizing response level of AChE and sucrose. Combination of optimum AChE and sucrose concentration show beneficial effect on the stability of the biosensor. GRAPHICAL ABSTRACT

Recent Advances in Perovskite Nanomaterials for Sensing Applications

Abstract Perovskite-structured materials have been extensively studied for sensing applications on account of their good thermal stability and 3-4 eV bandgap. They have been utilized to detect traces of O2, NO, CO, NO2, CO2, H2O, CH4, H2 and similar smaller gas molecules such as C2H5OH etc. In addition, other sensing applications are optical sensing, I-V fluctuations, strain sensors, electromechanical sensors, sensing of metal ions, etc. Perovskite-structured halides are reported for sensing of metal ions, toxic gases, Volatile Organic Compounds (VOCs), detection of fungicides, pesticides, explosives, cellular imaging, radiation detection and humidity and temperature sensing.

Exploring the application of molecular simulation technology in food sector: Focusing on food component interactions and food quality

The intricate interactions between component molecules that occur during food processing and storage are challenging to elucidate with traditional experimental techniques at the molecular level. Molecular simulation technology, conversely, offer an expedient methodology for investigating these molecular behaviors and anticipating macroscopic experimental phenomena. This review outlines the fundamental principles of molecular simulation techniques commonly employed in the field of food science and their associated computational procedures. It also examines their applications in the context of food component interactions and the quality control of proteins, carbohydrates, and lipids. This technique can be employed to elucidate the interactions between food nutrients, facilitate the rapid detection of pesticide and veterinary drug residues, identify foodborne pathogens and biotoxins, and conduct molecular mechanism analysis of toxicological effects at the atomic and molecular levels. By dynamically characterizing changes in molecular conformation, electron transfer and bonding processes, molecular simulation has become an important tool for optimizing food processing and storage conditions. Further studies should investigate the relationship between molecular conformational changes and food quality in greater depth and seek to optimize the application of molecular simulation in food science in order to provide critical insights into practical operations.

Smartphone-assisted sensing platform based on dual-responsive nitrogen-doped carbon dots for enzyme-free and visual quantitative detection of Cu2+ and glyphosate

Rapid and selective detection of copper ion (Cu2+) and glyphosate (GLY) is crucial to environmental pollution control, especially when using a simple and portable device. In this study, an enzyme-free and visual fluorescence sensor based on nitrogen-doped carbon dots (N-CDs) is successfully designed, featuring strong blue fluorescence and excellent stability. The sensor shows a linear response to Cu2+ over a wide detection range of 0.048 to 40 μM with a low limit of detection (LOD, 48 nM), resulting in fluorescence quenching at 435 nm. Meanwhile, the rapid enhancement in fluorescence intensity of the quenched N-CDs/Cu2+ system can be triggered by GLY owing to the strong complexation between Cu2+ and GLY, with a LOD of GLY as low as 0.24 μM. Furthermore, we develop a smartphone-assisted sensing platform using N-CDs/polyvinyl alcohol films, which captures and analyzes fluorescence images to detect Cu2+ and GLY with sensitive LODs of 73 nM and 0.32 μM, respectively. Satisfactory recoveries, ranging from 94.0 % to 104.6 %, are achieved in real sample detection, implying the potential application in the field of enzyme-free and on-site pesticides detection.

Conjugated polymer-boosted near-infrared electrochemiluminescence of organic dye for detecting acetamiprid

BackgroundThe near-infrared electrochemiluminescence (NIR-ECL) has excellent penetration and near zero background interference, and has shown unique advantages in clinical medicine and bioimaging. Among various types of NIR-ECL emitters, NIR organic dyes have arouse the concern of researchers due to their adjustable structure and diverse optical properties. However, the currently available NIR dyes usually have inherent self-quenching effect and poor photostability, so their ECL efficiency is low, and it is a great challenge to improve their ECL performance. ResultConjugated polymer-boosted NIR-ECL strategy was creatively developed to overcome ECL performance limitations of NIR dyes. IR 783, as one of heptamethine cyanine dyes, was performed a nanoprecipitation in the presence of poly[(9,9-dlhexyfluoren-2,7-dlyl)-co-(anthracen-9,10-dlyl)] (PFAD) to prepare IR polymer nanoparticles (IR PNPs). Due to resonance energy transfer (RET) from PFAD to IR 783 and encapsulation of IR 783 by PFAD, the resulting IR PNPs exhibited a strong and stable NIR-ECL emission with a maximum ECL wavelength of 802 nm under coreactant tripropylamine (TPrA) and H2O2 can effectively quench it. IR PNPs coupled proximity ligation assay (PLA)-induced DNA walker to achieve acetamiprid (ACE) analysis. ACE triggered PLA to form bipedal DNA walker, and further release G-rich secondary target (ST). With ST and hemin being captured on IR PNPs modified electrode, hemin/G-quadruplex was assembled to consume H2O2, thereby restoring ECL signal for ACE detection with a limit of detection of 4.74 × 10−15 M. SignificanceThis work opens up a new and simple way to boost NIR-ECL of organic dyes, and IR PNPs create a promising NIR-ECL platform for pesticide detection.