A syringe-aided apta-nanosensing method for colorimetric determination of acetamiprid

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.

Metal-organic framework composite Mn/Fe-MOF@Pd with peroxidase-like activities for sensitive colorimetric detection of hydroquinone

The construction of highly sensitive detection methods for hydroquinone (HQ) in environment and cosmetics is of great significance for environmental protection and human health. In this work, a novel detection method for HQ was successfully developed by constructing a metal-organic framework mimic enzyme colorimetric sensor (Mn/Fe-MOF@Pd1.0) with excellent peroxidase-like activity, which was synthesized by doping manganese ions into Fe-MOF by introducing bimetallic active centers, thereby improving the peroxidase-like activity of Fe-MOF, and the acid resistance and stability of Mn/Fe-MOF were improved by supporting palladium (Pd NPs). It is proven that Mn/Fe-MOF@Pd1.0 promoted the decomposition of hydrogen peroxide (H2O2) to generate active species, therefore, oxidized chromogenic substrate discoloration. On this basis, the detection of HQ based on the Mn/Fe-MOF@Pd1.0 colorimetric sensor was constructed, in which the limit of detection (LOD) was 0.09 μM in the linear range of 0.3-30 μM. Furthermore, Mn/Fe-MOF@Pd1.0 was successfully used for detecting HQ in hydroquinone whitening cream and actual water samples. The successful synthesis of Mn/Fe-MOF@Pd1.0 may provide new insights for further study of the enzyme-like activity of metal-organic framework composites, and the constructed facile and sensitive sensor system could broaden the application prospects of HQ detection.

A gold nanoparticle-based visual aptasensor for rapid detection of acetamiprid residues in agricultural products using a smartphone

Based on the colorimetric analysis of gold nanoparticles and a smartphone readable strategy, a stable, sensitive, and visual method was established for rapid detection of acetamiprid residues in agricultural products. By optimizing the key parameters, the detection process only took 40 minutes with good specificity. The acetamiprid aptamer can help AuNPs to resist salt-induced aggregation. Conversely, in the presence of acetamiprid, the anti-protection is weakened and the AuNPs aggregated with the color change of the solution. The photographs of the solution are recorded by the smartphone and analyzed through image processing. In the range from 25 to 300 μM the method can realize a quantitative analysis of acetamiprid, and the detection limit is about 3.81 μM. Excellent recoveries are taken in samples of cucumber, cabbage, and river water, ranging from 96.78% to 129.95%. These results show no significant difference from the results obtained by the microplate reader. What's more, the method employs a smartphone to read without the assistance of professional equipment, which greatly reduces the cost of detection, and shows a promising application prospect for on-site rapid detection of acetamiprid.

Based on the colorimetric analysis of gold nanoparticles and a smartphone readable strategy, a stable, sensitive, and visual method was established for rapid detection of acetamiprid residues in agricultural products.