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Silver-Programmed Dual-Optical Au Nanostructures and Machine Learning for Intelligent Biosensing.

Wei Li1, Yuqian Wang2,3, Lianghui Fan4

  • 1School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, P. R. China.

Analytical Chemistry
|November 4, 2025
PubMed
Summary

This study introduces a novel biosensing platform using gold nanorods and nanoclusters for dual-mode detection. Machine learning enhances image analysis for accurate diagnostics, paving the way for advanced point-of-care testing.

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Area of Science:

  • Nanomaterial engineering for advanced biosensing.
  • Integration of plasmonics and fluorescence for dual-mode signal transduction.

Background:

  • Biosensor evolution requires improved signal transduction and data processing.
  • Existing biosensing platforms often lack integrated data analysis capabilities.

Purpose of the Study:

  • To develop a universal biosensing platform combining dual-mode optical signals with machine learning-enhanced image analysis.
  • To enable high-precision detection and robust data analysis for clinical diagnostics.

Main Methods:

  • Synthesis of silver-modulated gold nanorods (AuNR@Ag) and gold-silver nanoclusters (AuAgNCs).
  • Characterization of localized surface plasmon resonance (LSPR) shifts and fluorescence variations.
  • Development and integration of a machine learning algorithm for bimodal signal image analysis.

Main Results:

  • The dual-mode platform achieved high-precision detection of alkaline phosphatase (R² > 0.99).
  • Machine learning analysis demonstrated robust correlations (R² > 0.95) for bimodal data.
  • Validated ML-augmented analytics on serum samples, showing good agreement with standard methods.

Conclusions:

  • The developed platform offers a versatile framework for next-generation biosensors.
  • Integration of nanomaterials and machine learning enhances diagnostic capabilities.
  • The approach shows significant potential for point-of-care testing and clinical diagnostics.