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<p>This study presents an optical sensor to detect glyphosate in wastewater using a Copper Metal-Organic Framework (Cu-MOF). To detect glyphosate directly, the sensor uses the fluorescence enhancement caused by the Cu-MOF dispersion in an ethanol solution. The sensor can detect glyphosate with an impressive 37.1 nM detection limit in a linear detection range of 0 µM to 0.63 µM. The exceptional selectivity of Cu-MOF for glyphosate, even in the presence of other pesticides and ions, was demonstrated by selectivity experiments, underscoring its potential for use in environmental monitoring. Furthermore, we tested the selectivity and applicability of the proposed sensor by using real-world samples. This selectivity highlights how well the sensor measures glyphosate, even in the complex matrices seen in wastewater systems. By tackling the issue of glyphosate contamination, the developed sensor marks substantial development in remediation tactics and environmental monitoring. Its high sensitivity, selectivity, and durable performance in challenging situations represent a significant advancement in the creation of efficient techniques for detecting glyphosate in wastewater, thereby enhancing efforts to safeguard the environment.</p>

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Fluorescence enhancement of glyphosate detection using Cu-MOF

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

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  • Lakshya Sankhla,
  • Rinku Yadav,
  • Aman Kumar,
  • Himmat Singh Kushwah

摘要

This study presents an optical sensor to detect glyphosate in wastewater using a Copper Metal-Organic Framework (Cu-MOF). To detect glyphosate directly, the sensor uses the fluorescence enhancement caused by the Cu-MOF dispersion in an ethanol solution. The sensor can detect glyphosate with an impressive 37.1 nM detection limit in a linear detection range of 0 µM to 0.63 µM. The exceptional selectivity of Cu-MOF for glyphosate, even in the presence of other pesticides and ions, was demonstrated by selectivity experiments, underscoring its potential for use in environmental monitoring. Furthermore, we tested the selectivity and applicability of the proposed sensor by using real-world samples. This selectivity highlights how well the sensor measures glyphosate, even in the complex matrices seen in wastewater systems. By tackling the issue of glyphosate contamination, the developed sensor marks substantial development in remediation tactics and environmental monitoring. Its high sensitivity, selectivity, and durable performance in challenging situations represent a significant advancement in the creation of efficient techniques for detecting glyphosate in wastewater, thereby enhancing efforts to safeguard the environment.