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Related Experiment Video

Updated: May 10, 2025

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
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SPR-based refractive index sensor design with grated Au-ZnS for dengue detection using machine learning.

Ananya Banerjee1, Jaisingh Thangaraj1

  • 1Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004 Jharkhand, India.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|April 25, 2025
PubMed
Summary

This study introduces a novel Surface Plasmon Resonance (SPR) fiber optic sensor using gold and zinc sulfide nanostructures for refractive index sensing. The sensor demonstrates high sensitivity and accurately detects dengue virus, enhanced by machine learning models.

Keywords:
Dengue detectionMachine LearningOptical fiber sensorsSurface Plasmon ResonanceZinc Sulphide

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Area of Science:

  • Photonics and Optics
  • Nanotechnology
  • Biomedical Sensing

Background:

  • Surface Plasmon Resonance (SPR) sensors are crucial for label-free biosensing.
  • Fiber optic SPR sensors offer advantages in miniaturization and remote sensing.
  • Developing highly sensitive and specific biosensors is essential for early disease detection.

Purpose of the Study:

  • To propose and investigate a novel SPR fiber optic refractive index (RI) sensor.
  • To optimize the sensor design for maximum wavelength sensitivity (WS).
  • To evaluate the sensor's capability for detecting the dengue virus and its integration with machine learning.

Main Methods:

  • Fabrication of a multi-mode fiber (MMF) SPR sensor with a bi-metallic nanostructure (Gold/Zinc Sulphide).
  • Systematic evaluation of design parameters to maximize wavelength sensitivity.
  • Application of machine learning (ML), specifically a neural network (NN) model, for data analysis and prediction.
  • Performance evaluation using metrics like RI sensitivity, Detection Accuracy (DA), Figure of Merit (FOM), and signal-to-noise ratio (SNR).

Main Results:

  • The SPR sensor exhibited non-linear RI sensitivity ranging from 9000 to 18,000 nm/RIU.
  • Achieved a highest WS of 14285.71 nm/RIU for detecting dengue virus in infected haemoglobin.
  • The NN model demonstrated superior performance with a mean square error (MSE) of 0.2828 and R² of 0.9998.
  • Key performance metrics included maximum DA of 14.285 μm⁻¹, FOM of 158.73 RIU⁻¹, SNR of 5.55, and QF of 79365.055 nm/RIU.

Conclusions:

  • The proposed SPR fiber optic sensor with a bi-metallic nanostructure is highly effective for RI sensing.
  • Integration of ML, particularly NN, significantly enhances sensor performance in prediction and classification.
  • The developed biosensor shows great potential for sensitive and accurate biological applications, including disease detection.