Evidence map›Paper›PMID 41953392›Full record

ArticleAdvanced functional materials2025

Emerging 2D Materials and Their Hybrid Nanostructures for Label-Free Optical Biosensing: Recent Progress and Outlook.

Xinyi Li, Yonghao Fu, Yuehe Lin, Dan Du

Abstract read
In one paragraph

Article in Advanced functional materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Engineering two-dimensional electronic biosensors.Nature reviews electrical engineering · 2026
    Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
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

4 authors.

Xinyi LiSchool of Mechanical and Material Engineering, Washington State University, Pullman, WA 99164, USA.
Yonghao FuSchool of Mechanical and Material Engineering, Washington State University, Pullman, WA 99164, USA.
Yuehe LinSchool of Mechanical and Material Engineering, Washington State University, Pullman, WA 99164, USA.
Dan DuSchool of Mechanical and Material Engineering, Washington State University, Pullman, WA 99164, USA.

Funding

NIOSH CDC HHS R01 OH012579
6 · The paper itself

Abstract

The increasing demand for rapid detection and remote monitoring with high sensitivity and selectivity has driven the development of label-free optical biosensors based on 2D materials (2DMs). Owing to their high surface-to-volume ratio and unique, tunable electrical and optical properties, 2DMs are highly attractive for biosensing applications. Van der Waals heterostructures (vdWHs) that combine different 2DMs allow the creation of hybrid materials with synergistic or novel functionalities. Beyond purely 2D vdWHs, mixed-dimensional hybrid architectures have recently emerged, offering enhanced sensitivity, stability, and reproducibility in label-free optical detection. This review highlights recent advances in surface-enhanced Raman scattering (SERS)- and surface plasmon resonance (SPR)-based biosensing using 2DMs and their hybrid nanostructures, discussing the role of each component and emphasizing their synergistic effects. It also provides an overview of recent progress in multifunctional sensing platforms and the integration of machine learning to improve performance and enable intelligent data analysis. Finally, the current challenges and future opportunities for advancing next-generation label-free optical biosensors are highlighted.

Indexed as

2D materialsbiosensorshybrid nanostructuressurface-enhanced Raman scatteringsurface plasmon resonance

Identifiers

PMID41953392
PMCPMC13055561

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.