Recent development of surface-enhanced Raman scattering for biosensing

  • Chenglong Lin
  • , Yanyan Li
  • , Yusi Peng
  • , Shuai Zhao
  • , Meimei Xu
  • , Lingxia Zhang
  • , Zhengren Huang*
  • , Jianlin Shi
  • , Yong Yang*
  • *Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

122 Scopus citations

Abstract

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Original languageEnglish
Article number149
JournalJournal of Nanobiotechnology
Volume21
Issue number1
DOIs
StatePublished - Dec 2023
Externally publishedYes

Keywords

  • Biological imaging
  • Biomolecular
  • Machine learning
  • SARS-CoV-2
  • SERS
  • Tumor

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