Evidence map›Paper›PMID 37149605›Full record

ReviewJournal of nanobiotechnology2023

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

Abstract readReview
In one paragraph

Review in Journal of nanobiotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Virtual Overlay Staining With Plasmonic Oligomer Metasurface.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  2. Review
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  18. Morphology Engineering of SnSExploration (Beijing, China) · 2025
    Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Chenglong LinState 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.
Yanyan LiState 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.
Yusi PengState 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.
Shuai ZhaoState 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.
Meimei XuState 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.
Lingxia ZhangState 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.
Zhengren HuangState 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. zhrhuang@mail.sic.ac.cn.
Jianlin ShiState 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.
Yong YangState 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. yangyong@mail.sic.ac.cn.

Funding

National Key Research and Development Project 2022YFE0110100Natural Science Fund 52172167Shanghai Science and Technology Program 22DX1900300Shanghai Science and Technology Program 22XD1404000
6 · The paper itself

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.

Indexed as

Biosensing TechniquesCOVID-19NanostructuresHumansNanotechnologySARS-CoV-2Spectrum Analysis, RamanBiological imagingBiomolecularMachine learningSARS-CoV-2SERSTumor

Identifiers

PMID37149605
PMCPMC10163864

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.