Simultaneous determination of 4-tert-octylphenol, chlorpyrifos-ethyl and penconazole by GC–MS after sensitive and selective preconcentration with stearic acid coated magnetic nanoparticles

A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography-mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea.

Controllable synthesis of flower-like hierarchical porous TiO2 at room temperature and its affinity application

Flower-like particles have attracted much attention due to their efficient surface accessible sites and unique hierarchical porous structure. However, their synthesis is usually challenging and requires complex procedures. Herein, we present a simple method for rapid preparation of flower-like hierarchical porous TiO2 (FHP-TiO2) at room temperature for the first time. This method can accurately control the size of FHP-TiO2 from 150 nm to 400 nm by combining co-assembly and Stober reaction. The formation mechanism and influencing factors of FHP-TiO2 were systematically investigated, and its excellent metal oxide affinity was confirmed by theoretical calculations. Due to its hierarchical porous structure, large surface area and high specificity performance, FHP-TiO2 served as an appealing restricted-access adsorbent for specific and efficient enrichment of molecules with phosphate groups in a complex sample matrix, thereby realizing the quantitative analysis of these important biomolecules by coupling with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Moreover, compared with other morphologies (rough surface, and hollow dendritic and mesoporous structure) of TiO2 and flower-like SiO2, FHP-TiO2 showed the best affinity binding ability. This research not only presents a novel approach for tunable room-temperature synthesis of FHP-TiO2 with different sizes, but also expands the application of FHP-TiO2 as an appealing sample-enricher for food safety monitoring and early disease diagnosis.

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

Recent advancements in non-enzymatic electrochemical sensor development for the detection of organophosphorus pesticides in food and environment

Organophosphorus Pesticides (OPPs) are among the extensively used pesticides throughout the world to boost agricultural production. However, persistent residues of these toxic pesticides in various vegetables, fruits, and drinking water poses detrimental health effects. Consequently, the rapid monitoring of these harmful chemicals through simple and cost-effective methods has become crucial. In such an instance, electrochemical methods offer simple, rapid, sensitive, reproducible, and affordable detection pathways. To overcome the limitations associated with electrochemical enzymatic sensors, non-enzymatic sensors have emerged as promising and simpler alternatives. The non-enzymatic sensors have demonstrated superior activity, reaching detection limit up to femto (10-15) molar concentration in recent years, leveraging higher selectivity obtained through the molecularly imprinted polymers, synergistic effects between carbonaceous nanomaterials and metals, metal oxide alloys, and other alternative approaches. Herein, this review paper provides an overview of the recent advancements in the development of non-enzymatic electrochemical sensors for the detection of commonly used OPPs, such as Chlorpyrifos (CHL), Diazinon (DZN), Malathion (MTN), Methyl parathion (MP) and Fenthion (FEN). The design method of the electrodes, electrode functioning mechanism, and their analytical performance metrics, such as limit of detection, sensitivity, selectivity, and linearity range, were reviewed and compared. Furthermore, the existing challenges within this rapidly growing field were discussed along with their potential solutions which will facilitate the fabrication of advanced and sustainable non-enzymatic sensors in the future.

Recent advances in assessment methods and mechanism of microbe-mediated chlorpyrifos remediation

Chlorpyrifos (CP) is one of the Organophosphorus pesticides (OPs) primarily used in agriculture to safeguard crops from pests and diseases. The pervasive use of chlorpyrifos is hazardous to humans and the environment as it inhibits the receptor for acetylcholinesterase activity, leading to abnormalities linked to the central nervous system. Hence, there is an ardent need to develop an effective and sustainable approach to the on-site degradation of chlorpyrifos. The role of microbes in the remediation of pesticides is considered the most effective and eco-friendly approach, as they have strong degradative potential due to their gene and enzymes naturally adapted to these sites. Several reports have previously been published on exploring the role of microbes in the degradation of CP. However, detection of CP as an environmental contaminant is an essential prerequisite for developing an efficient microbial-mediated biodegradation method with less harmful intermediates. Most of the articles published to date discuss the fate and impact of CP in the environment along with its degradation mechanism but still fail to discuss the analytical portion. This review is focused on the latest developments in the field of bioremediation of CP along with its physicochemical properties, toxicity, fate, and conventional (UV-Visible spectrophotometer, FTIR, NMR, GC-MS, etc) and advanced detection methods (Biosensors and immunochromatography-based methods) from different environmental samples. Apart from it, this review explores the role of metagenomics, system biology, in-silico tools, and genetic engineering in facilitating the bioremediation of CP. One of the objectives of this review is to educate policymakers with scientific data that will enable the development of appropriate strategies to reduce pesticide exposure and the harmful health impacts on both Human and other environmental components. Moreover, this review provides up-to-date developments related to the sustainable remediation of CP.

A comprehensive review on electrochemical and optical aptasensors for organophosphorus pesticides

There has been a rise in pesticide use as a result of the growing industrialization of agriculture. Organophosphorus pesticides have been widely applied as agricultural and domestic pest control agents for nearly five decades, and they remain as health and environmental hazards in water supplies, vegetables, fruits, and processed foods causing serious foodborne illness. Thus, the rapid and reliable detection of these harmful organophosphorus toxins with excellent sensitivity and selectivity is of utmost importance. Aptasensors are biosensors based on aptamers, which exhibit exceptional recognition capability for a variety of targets. Aptasensors offer numerous advantages over conventional approaches, including increased sensitivity, selectivity, design flexibility, and cost-effectiveness. As a result, interest in developing aptasensors continues to expand. This paper discusses the historical and modern advancements of aptasensors through the use of nanotechnology to enhance the signal, resulting in high sensitivity and detection accuracy. More importantly, this review summarizes the principles and strategies underlying different organophosphorus aptasensors, including electrochemical, electrochemiluminescent, fluorescent, and colorimetric ones.

Magnetic Nanoparticle-Based Dispersive Solid-Phase Microextraction of Three UV Blockers Prior to Their Determination by HPLC-DAD

The need for proper handling of environmental samples is significant, owing to their environmental effects on both humans and animals, as well as their immediate surroundings. In the current study, magnetic nanoparticle-based dispersive solid-phase microextraction was combined with high-performance liquid chromatography using a diode array as the detector (HPLC-DAD) for both the separation and determination of three different UV blockers, namely octocrylene, ethylhexyl methoxycinnamate, and avobenzone. The optimum conditions for the extraction were found to be as follows: Stearic acid magnetic nanoparticles (20 mg) as the sorbent, acetonitrile (100 µL) as the eluent, as well as a sample pH of 2.50, adsorption and desorption time of 1.0 min, with a 3.0 mL sample volume. The limits of detection were as low as 0.05 µg mL-1. The coefficient of determination (R2) was above 0.9950, while the percentages of relative recoveries (%RR) were between 81.2 and 112% for the three UV blockers from the environmental water samples and sunscreen products.

Nanoparticles Based Extraction Strategies for Accurate and Sensitive Determination of Different Pesticides

Sample preparation methods have become indispensable steps in analytical measurements not only to lower the detection limit but also to eliminate the matrix effect although more sophisticated instruments are being commonly used in routine analyses. Solid phase extraction (SPE) is one of the main extraction/preconcentration methods used to extract and purify target analytes along with simple and rapid procedures but some limitations have led to seek for an easy, sensitive and fast extraction methods with analyte-selective sorbents. Nanoparticles with different modifications have been used as spotlight to enhance extraction efficiency of target pesticides from complicated matrices. Carbon-based, metal and metal oxides, silica and polymer-based nanoparticles have been explored as promising sorbents for pesticide extraction. In this review, different types of nanoparticles used in the preconcentration of pesticides in various samples are outlined and examined. Latest studies in the literature are discussed in terms of their instrumental detection, sample matrix and limit of detection values. Novel strategies and future directions of nanoparticles used in the extraction and preconcentration of pesticides are also discussed.

Recent Materials Developed for Dispersive Solid Phase Extraction

Solid phase extraction (SPE) is an analytical procedure developed with the purpose of separating a target analyte from a complex sample matrix prior to quantitative or qualitative determination. The purpose of such treatment is twofold: elimination of matrix constituents that could interfere with the detection process or even damage analytical equipment as well as enriching the analyte in the sample so that it is readily available for detection. Dispersive solid phase extraction (dSPE) is a recent development of the standard SPE technique that is attracting growing attention due to its remarkable simplicity, short extraction time and low requirement for solvent expenditure, accompanied by high effectiveness and wide applicability. This review aims to thoroughly survey recently conducted analytical studies focusing on methods utilizing novel, interesting nanomaterials as dSPE sorbents, as well as known materials that have been only recently successfully applied in dSPE techniques, and evaluate their performance and suitability based on comparison with previously reported analytical procedures.

Molecularly imprinted QCM sensor based on delaminated MXene for chlorpyrifos detection and QCM sensor validation

Chlorpyrifos (CHL) is one of the most frequently used broad-spectrum organophosphate insecticides.