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Nanophotonic Glyphosate Sensing Integrated into a Portable Optoelectronic Device with Deep-Learning Imaging.

Jorge Molina-González1, C Mateo Frausto-Avila1, Haggeo Desirena2

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A new glyphosate nanosensor uses upconversion nanoparticles and a convolutional neural network (CNN) for rapid, on-site detection. This portable device accurately quantifies glyphosate in soil and water, offering a practical solution for environmental monitoring.

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convolutional neural networkglyphosate detectionmachine learningoptical signal processingportable optoelectronic sensorreal-time monitoringremote sensing

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Area of Science:

  • Nanotechnology
  • Analytical Chemistry
  • Biomedical Engineering

Background:

  • Glyphosate is a widely used herbicide with environmental concerns.
  • Accurate and rapid detection methods for glyphosate are crucial for monitoring its impact.
  • Existing detection methods can be time-consuming or require specialized laboratory equipment.

Purpose of the Study:

  • To design and validate a novel nanosensor for glyphosate detection.
  • To develop a portable, on-site detection system using advanced data analysis.
  • To achieve rapid, accurate, and reliable quantification of glyphosate in environmental samples.

Main Methods:

  • Utilized NaYF4:Yb3+/Er3+ upconverting nanoparticles (UCNPs) for signal transduction.
  • Developed a sensing mechanism based on selective resonant energy transfer triggered by glyphosate.
  • Integrated a convolutional neural network (CNN) into a portable optoelectronic device for data analysis.
  • Employed image capture of upconversion emission and light scattering for quantification.

Main Results:

  • The nanosensor demonstrated selective interaction with glyphosate via Cu2+ coordination.
  • The portable system achieved a high coefficient of determination (R2 = 0.987) for glyphosate quantification.
  • Measurements were rapid, completed in 5 seconds, with a minimum detectable concentration of 12.5 ppm.
  • The system showed a linear response range up to 3125 ppm.

Conclusions:

  • The developed glyphosate nanosensor and portable system offer a promising solution for rapid, on-site environmental monitoring.
  • The combination of UCNPs, selective sensing, and CNN-based analysis enables accurate and practical glyphosate detection.
  • This technology has significant potential for scalable monitoring of glyphosate in soil and water under field conditions.