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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

484
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
484

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Recent development of surface-enhanced Raman scattering for biosensing.

Chenglong Lin1,2,3, Yanyan Li1,2,3, Yusi Peng1,2,3

  • 1State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China.

Journal of Nanobiotechnology
|May 6, 2023
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Summary

Surface-Enhanced Raman Scattering (SERS) technology offers sensitive molecular detection for environmental, medical, and food safety applications. Recent advancements in SERS substrates and biosensing strategies are enhancing biomolecular detection and imaging capabilities.

Keywords:
Biological imagingBiomolecularMachine learningSARS-CoV-2SERSTumor

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Biotechnology

Background:

  • Surface-Enhanced Raman Scattering (SERS) is a powerful technique for single-molecule level identification.
  • SERS has demonstrated significant progress across environmental science, medical diagnosis, food safety, and biological analysis.
  • Emerging high-performance SERS substrates are expanding its application scope, particularly in biological analysis.

Purpose of the Study:

  • To summarize recent developments in SERS substrates and their applications in biomolecular detection and biological imaging.
  • To comprehensively discuss SERS concepts, sensing mechanisms, and strategies for enhancing biosensing performance.
  • To explore the role of machine learning in SERS data analysis for biosensing and diagnostics.

Main Methods:

  • Review of recent literature on SERS substrate materials and their performance.
  • Discussion of intrinsic and extrinsic SERS sensing schemes in biological applications.
  • Analysis of strategies including nanomaterial design and surface bio-functionalization.
  • Examination of machine learning applications for SERS data analysis and diagnosis.

Main Results:

  • SERS substrates show promise for detecting biomolecules like SARS-CoV-2 virus and tumor markers.
  • Applications extend to biological imaging and pesticide detection.
  • Advanced nanomaterials and bio-functionalization significantly improve SERS biosensing sensitivity and specificity.
  • Machine learning aids in data interpretation for SERS-based diagnostics.

Conclusions:

  • SERS technology is a rapidly advancing field with substantial potential in various scientific domains.
  • Continued development of SERS substrates and biosensing strategies will drive future applications.
  • Addressing current challenges and exploring new perspectives are crucial for the future of SERS biosensing.