Cu2O nanoparticles for the degradation of methyl parathion

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.

Enhancement of photocatalytic activity of Ba-doped CoO for degradation of Emamectin benzoate in aqueous solution

The present study was focused on the preparation of cobalt oxide (CoO) and barium-doped cobalt oxide (Ba-doped CoO) by following the co-precipitation method for the degradation of Emamectin benzoate pesticide in the aqueous medium. The prepared catalysts were characterized using SEM, EDX, and XRD to confirm the formation of catalysts and to observe the variation in the composition of catalysts during the degradation study. It can be suggested from the results of SEM, EDX, XRD, and FTIR analyses that Ba atom has successfully incorporated in the crystalline structure of CoO. The degradation of Emamectin benzoate pesticide was studied under the influence of different factors like solution pH, the dose of catalyst, contact time, temperature, and initial concentration of pesticide. It was observed that solution pH affects the degradation of the pesticide, and maximum degradation (23% and 54%) was found at pH 5.0 and 6.0 using CoO and Ba-doped CoO, respectively. The degradation of pesticides was found to be increased continuously (27-35% in case of CoO while 47-58% in case Ba-doped CoO) with the time of contact. However, the degradation was found to be decreased (23-3% in case of CoO while 47-44% in case Ba-doped CoO) with an increase in temperature. Likewise, in the beginning, degradation was observed to be increased up to some extent with the dose of catalyst and initial concentration of pesticide but started to decrease with further augmentation in the dose of catalyst and initial concentration of pesticide. It may be concluded from this study that doping of Ba considerably enhanced the photocatalytic ability of CoO for Emamectin benzoate pesticide.