Functionalized silver nanoparticles as colorimetric probes for sensing tricyclazole

A paper-based lateral flow immunochromatographic sensor for the detection of tricyclazole in rice

Tricyclazole is commonly used to prevent rice blast to meet the carbohydrate intake needs of half of the global population, and a large number of toxicological reports indicate that monitoring of tricyclazole is necessary. Here, we analyzed the structure of tricyclazole and designed different hapten derivatization strategies to prepare a high-performance monoclonal antibody (half inhibition concentration of 1.61 ng/mL), and then a lateral flow immunochromatographic sensor based on gold nanoparticles for the detection of tricyclazole in rice, with a limit of detection of 6.74 μg/kg and 13.58 μg/kg in polished and brown rice, respectively. The recoveries in rice were in the range of 84.6-107.4%, no complex pretreatment was required for comparison with LC-MS/MS, and the comparative analysis demonstrated that our method had good accuracy and precision. Therefore, the developed lateral flow immunochromatographic analysis was a reliable and rapid means for the on-site analysis of tricyclazole in rice.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Sodium-Alginate-Functionalized Silver Nanoparticles for Colorimetric Detection of Dimethoate

Sodium alginate (SA) was used to functionalize the surfaces of silver nanoparticles (AgNPs) to form SA-AgNPs for sensing dimethoate with a rapid and sensitive visual readout. UV-Vis spectrophotometry, Fourier transform infrared spectroscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and zeta potential measurements were used to characterize SA-AgNPs that were synthesized under the ideal conditions. SA-AgNPs were spherical with an average size of 14.6 nm. The stability of SA-AgNPs was investigated with changes in pH, salinity, and storage time. This colorimetric assay of dimethoate relied on the change in the absorption ratio (A₄₇₅/A₄₀₀) of SA-AgNPs, resulting in their aggregation caused by dimethoate, leading to a visual change for SA-AgNPs from yellow to pale yellow. As a result, the absorption ratio (A₄₇₅/A₄₀₀) of SA-AgNPs showed good linearity in the range of 0.05 to 2.0 ppm (R² = 0.9986) with a limit of detection (LOD) of 30 ppb. Adding other pesticides did not significantly change the absorption ratio of SA-AgNPs, indicating its high selectivity as a colorimetric assay. The sensor was successfully used to detect dimethoate in actual water samples.

Application, advancement and green aspects of magnetic molecularly imprinted polymers in pesticide residue detection

Molecularly imprinted polymers (MIPs) have added a vital contribution to food quality and safety with the effective extraction of pesticide residues due to their unique properties. Magnetic molecularly imprinted polymers (MMIPs) are a superior approach to overcome stereotypical limitations due to their unique core-shell and novel composite structure, including high chemothermal stability, rapid extraction, and high selectivity. Over the past two decades, different MMIPs have been developed for pesticide extraction in actual food samples with a complex matrix. Nevertheless, such developments are desirable, yet the synthesis and mode of application of MMIP have great potential as a green chemistry approach that can significantly reduce environmental pollution and minimize resource utilization. In this review, the MMIP application for single or multipesticide detection has been summarized by critiquing each method's uniqueness and efficiency in real sample analysis and providing a possible green chemistry exploration procedure for MMIP synthesis and application for escalated food and environmental safety.

Gold and Silver Nanoparticle-Based Colorimetric Sensors: New Trends and Applications

Gold and Silver nanoparticles (AuNPs and AgNPs) are perfect platforms for developing sensing colorimetric devices thanks to their high surface to volume ratio and distinctive optical properties, particularly sensitive to changes in the surrounding environment. These characteristics ensure high sensitivity in colorimetric devices. Au and Ag nanoparticles can be capped with suitable molecules that can act as specific analyte receptors, so highly selective sensors can be obtained. This review aims to highlight the principal strategies developed during the last decade concerning the preparation of Au and Ag nanoparticle-based colorimetric sensors, with particular attention to environmental and health monitoring applications.