Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

Abstract

The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiomegut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

Glyphosate: Hepatotoxicity, Nephrotoxicity, Hemotoxicity, Carcinogenicity, and Clinical Cases of Endocrine, Reproductive, Cardiovascular, and Pulmonary System Intoxication

“…Regarding GLP poisoning and GLP above’s modes of actions, i.e., dysbiosis and the inhibition of AChE, these devices, electronically powered, decision-making, and user-friendly, shall enable self-handled or point-of-care professional-assisted evaluation of the harm followed with rapid and precise determining and removing the herbicide and coformulants. In technological terms, these devices will be constructed as biocompatible chemo- and (bacteria cell)-based electronic biohybrids and nanorobots, capable of measuring the GBH analyte or GBH pathology-associated biomarkers in skin, when implanted or wearable as a chip, in body, when swallowed, or in body fluids liquid, when used as a portable, smart device. ,− Finally, it is expected that chemisorptive or adsorptive tissue-specific systems will be manufactured to detect and capture the GBH xenobiotics with molecular recognition-provided selectivity and sensitivity, which will be followed by harmless, on-site, and nature-mimicking microbial biodegradation of GLP. , …”

“…Besides, we introduce the potential carcinogenic effects of this poisoning. Ailments of the gastrointestinal tract and nervous system, including gut dysbiosis and the dysregulation of microbiota-gut-brain-axis, are discussed in our review article concerning GLP and GBH toxicity, whereas an extensive summary of novel technologies applied to GLP sensing is presented in our recent critical review …”

Quantum Simulations and Experimental Insights into Glyphosate Adsorption Using Graphene-Based Nanomaterials

“…Despite these regulations, it is unanimously acknowledged that due to its small, polar, and highly water-soluble nature, indiscriminate GLY use can result in severe environmental impacts, particularly water pollution, classifying it as an emerging contaminant. Therefore, investigations focusing on materials for GLY monitoring, detection, and removal systems are of great significance, and new technologies based on nanomaterials will be essential to combine precision agriculture with environmental protection …”

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

“…Since such recent beginnings, these techniques have been demonstrated to be a highly effective potential tool [47][48][49][50][51][52][53][54][55]. Some of the challenges addressed have been fruit classification and segmentation [56][57][58][59], crop [60] and biochar [61] yield estimation, timber defect detection [62], water stress measurement [63,64], weed detection [65,66], and pesticide detection [67] and design [68].…”