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Plasmonic cellulose microfilament assisted SERS detection in microfluidics.

Kaibin Yao1, Xin Xie1, Jiawei Jiao2

  • 1College of Physics, Chongqing University, Chongqing 400044, China; Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, Chongqing University, Chongqing 400044, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|November 23, 2023
PubMed
Summary

This study introduces plasmonic cellulose microfilaments for highly sensitive surface-enhanced Raman spectroscopy (SERS) in microfluidics. This novel approach enables accurate, label-free urea detection with deep learning, offering a promising tool for online screening.

Keywords:
Deep learningOnline detectionPlasmonic cellulose microfilamentSERS microfluidics

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Area of Science:

  • Nanotechnology
  • Analytical Chemistry
  • Biomedical Engineering

Background:

  • Surface-enhanced Raman spectroscopy (SERS) sensitivity is limited by nanoscale enhancement areas in solution-based microfluidic detection.
  • Existing methods struggle with low sensitivity for trace molecule detection in microfluidic systems.

Purpose of the Study:

  • To develop a highly sensitive SERS detection method for microfluidics using a novel plasmonic cellulose microfilament.
  • To optimize the plasmonic cellulose microfilament structure for enhanced Raman signal.
  • To demonstrate the application of this system for label-free urea detection and screening.

Main Methods:

  • Fabrication of cellulose microfilaments embraced by silver nanoparticles (plasmonic cellulose microfilaments).
  • Integration of plasmonic cellulose microfilaments into a microchannel for microfluidic SERS detection.
  • Optimization of the plasmonic cellulose microfilament structure for sensitivity.
  • Application of deep learning (DL) for automatic urea identification.

Main Results:

  • The plasmonic cellulose microfilament achieved high sensitivity detection down to 10^-13 M.
  • The system demonstrated good reproducibility in SERS detection.
  • Deep learning enabled automatic urea identification with 99% diagnostic accuracy.

Conclusions:

  • Plasmonic cellulose microfilaments significantly enhance SERS sensitivity in microfluidics.
  • This technology offers a fast, label-free screening tool for urea detection.
  • The developed SERS microfluidics show great potential for online, high-sensitivity detection applications.