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Related Experiment Video

Updated: Jul 16, 2026

Optical Trapping of Plasmonic Nanoparticles for In Situ Surface-Enhanced Raman Spectroscopy Characterizations
06:19

Optical Trapping of Plasmonic Nanoparticles for In Situ Surface-Enhanced Raman Spectroscopy Characterizations

Published on: June 23, 2022

Gold- or Silver-Nanoparticle SERS Platforms for Plasma-Based Diagnostics and AI-Driven Analysis.

Gideon L Elizur1,2, Alexandre Canhoto1,2, Gabriela Soares3

  • 1Associate Laboratory i4HB-Institute for Health and Bioeconomy, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Surface-enhanced Raman spectroscopy (SERS) offers sensitive disease diagnostics using gold and silver nanoparticles in plasma. Advances in nanostructures, AI, and standardization are paving the way for clinical use.

Keywords:
artificial intelligencegold nanoparticlesliquid biopsymachine learningplasma diagnosticsplasmonic nanoparticlessilver nanoparticlessurface-enhanced Raman spectroscopy (SERS)

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Last Updated: Jul 16, 2026

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Area of Science:

  • Nanotechnology
  • Spectroscopy
  • Biomedical Diagnostics

Background:

  • Surface-enhanced Raman spectroscopy (SERS) is a sensitive technique for disease diagnostics.
  • Gold and silver nanoparticles are key components of SERS platforms.
  • Plasma analysis presents unique challenges and opportunities for SERS.

Purpose of the Study:

  • To review gold- and silver-nanoparticle-based SERS platforms for plasma disease diagnostics.
  • To highlight advances in nanostructures, sample analysis, biomarker detection, and AI.
  • To discuss challenges and strategies for clinical translation of SERS.

Main Methods:

  • Review of plasmonic nanostructures for SERS.
  • Analysis of biological sample preparation and biomarker detection in plasma.
  • Integration of AI and machine learning for spectral data processing.

Main Results:

  • SERS shows potential for detecting cancer, inflammatory, and neurological diseases in plasma.
  • AI and machine learning enhance diagnostic robustness and reproducibility.
  • Standardization and interdisciplinary collaboration are crucial for clinical translation.

Conclusions:

  • SERS diagnostic technologies are progressing towards clinical applicability.
  • Addressing substrate variability, matrix effects, and reproducibility is essential.
  • Further standardization and collaboration will bridge the gap to routine clinical implementation.