Advances in colloid and interface science · 2026
Strategies for glyphosate detection: recent advances and future outlook.
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Deshmukh Megha A, Thorat Hanuman N, Ramanaviciene Almira, Ramanavicius Arunas
Аннотация
Sensitive detection of glyphosate is of global importance due to its widespread use and persistent accumulation in water, soil, and food, raising concerns related to human health and environmental safety. As a result, the development of rapid, selective, and field-deployable sensing strategies has attracted growing attention across electrochemical, fluorescent, colorimetric, and spectroscopic platforms. However, established analytical methods face practical limitations, including specialized instrumentation and sample-preparation requirements for chromatographic-mass spectrometric methods, derivatization or indirect detection in some optical methods, and potential cross-reactivity in immunoassays. Recent progress in glyphosate sensing is increasingly governed by advances in material design rather than by transduction techniques alone. Glyphosate's phosphonate, carboxylate, and amine functionalities mediate metal coordination, electrostatic interactions, and hydrogen bonding at sensing interfaces. These interactions influence analyte adsorption and recognition and, depending on the sensing platform, interfacial charge transfer or optical signal generation. Carbon-based materials, noble and non-noble metals, metal oxides, conducting polymers, molecularly imprinted polymers (MIPs), and metal-organic frameworks (MOFs) provide tailored surface chemistry, catalytic activity, molecular recognition, and adsorption capability essential for effective glyphosate detection. By controlling analyte recognition, interfacial interactions, and signal amplification, these functional materials play a central role in improving sensing performance. This review connects material properties with glyphosate-interface behaviour while highlighting remaining challenges associated with matrix interference, glyphosate complexation, sensor reproducibility, and validation in real samples.