Evidence map›Paper›PMID 42230373›Full record

ReviewDiscover nano2026

Plasmonic and surface-enhanced Raman nanobiosensors for quantitative molecular detection.

Yeongbeom Kim, Jaewon Choi, Subin Lee, Yerim Kim, Jisu Park, Kisung Lee, Eunsoo Cho, Jaewon Lee, Kwang Suk Lim, Hyun-Ouk Kim

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Discover nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Yeongbeom Kim *Division of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Jaewon Choi *Division of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Subin Lee *Division of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Yerim KimDivision of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Jisu ParkDivision of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Kisung LeeDivision of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Eunsoo ChoDivision of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea.
Jaewon LeeDepartment of Mechanical and Aerospace Engineering, University of Missouri, Columbia, 65211, USA.
Kwang Suk LimDivision of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea. kslim@kangwon.ac.kr.
Hyun-Ouk KimDivision of Chemical Engineering and Bioengineering, College of Art, Culture and Engineering, Kangwon National University, Chuncheon, Gangwon State, 24341, Republic of Korea. kimhoman@kangwon.ac.kr.

Funding

National Research Foundation of Korea RS-2024-00441289National Research Foundation of Korea RS-2025-16068330
6 · The paper itself

Abstract

Plasmonic surface-enhanced Raman scattering (SERS) nanobiosensors employ nanoscale electromagnetic field amplification to achieve ultrasensitive, multiplex molecular detection. This review systematically outlines the fundamental plasmonic principles, nanostructure engineering strategies, and surface chemical functionalization approaches that dictate sensor performance. Quantitative analysis methodologies-including internal standards, ratio-based quantification, and machine learning-driven spectral interpretation-are critically examined. Potential clinical and field applications are highlighted through examples involving nucleic acids, proteins, pathogens, and environmental toxicants. Key technical challenges, such as reproducibility, scalable manufacturing, and methodological standardization, are discussed in detail. Finally, future directions are proposed, emphasizing single-molecule quantification, in vivo SERS applications, and the integration of sustainable materials into sensor design.

Identifiers

PMID42230373
PMCPMC13230435

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Registered trials

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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 OpenQuestion graph.