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Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline
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Integrating nano crystal sensor with explainable deep learning for nutrients and microplastic-toxicity detection.

Aya Magdy1, Seham Abd-Elsamee1, Doaa A Altantawy2

  • 1Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 60 El-Gomhoria Street, Mansoura, 35516, Egypt.

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|May 14, 2026
PubMed
Summary

A new photonic-AI sensing framework uses a photonic crystal sensor and AI to detect soil nutrients and microplastics with high accuracy. This technology offers a promising solution for intelligent soil monitoring.

Keywords:
Deep learningExplainable AI (XAI)PhC sensorSHAP and LIMESustainable agriculture

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Area of Science:

  • Photonics
  • Artificial Intelligence
  • Environmental Sensing

Background:

  • Soil health is crucial for agriculture and ecosystems.
  • Microplastic contamination poses a significant environmental threat.
  • Accurate detection of soil nutrients and microplastics is essential for monitoring and management.

Purpose of the Study:

  • To propose a simulation-based photonic-AI sensing framework for simultaneous detection of soil nutrients and microplastics.
  • To develop a highly sensitive and selective photonic crystal sensor integrated with an advanced AI model.
  • To achieve high accuracy and explainability in soil contaminant identification.

Main Methods:

  • Integration of a 2D dual-ring cavity photonic crystal (PhC) sensor with a Deep & Cross Network (DCN) model.
  • Utilizing the PhC sensor's optical properties for spectral analysis of soil samples.
  • Employing DCN for analyzing spectral responses and classifying soil components, enhanced by SHAP and LIME for explainability.

Main Results:

  • The PhC sensor achieved high quality factors (up to 18,244) and sensitivity (631 nm/RIU for nutrients, 432 nm/RIU for LDPE microplastics).
  • The DCN model demonstrated high classification accuracy (99.87%) and robust performance in identifying soil elements and contaminants.
  • Explainable AI (XAI) confirmed the physical relevance of spectral features, ensuring reliable predictions.

Conclusions:

  • The proposed photonic-AI framework offers a powerful tool for intelligent soil monitoring.
  • The integrated PhC sensor and DCN model provide accurate and explainable detection of soil nutrients and microplastics.
  • The compact design and lightweight model pave the way for future hardware integration and real-world applications.