Evidence map›Paper›PMID 41402825›Full record

ArticleJournal of nanobiotechnology2025

Inverse-designed plasmonic biosensors with LSPR-SPP-wood anomaly coupling enhanced for biomolecular analysis.

Yihui Yang, Youqian Chen, Rui Li, Wen Li, Mingqian Chen, Yanan Li, Wenjun Hu, Gang Logan Liu, Liping Huang

Abstract read
In one paragraph

Article in Journal of nanobiotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Yihui Yang *Department of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China.
Youqian Chen *Department of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China.
Rui LiDepartment of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China.
Wen LiDepartment of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China.
Mingqian ChenDepartment of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China.
Yanan LiState Key Laboratory of Microbial Technology, Nanjing Normal University, Nanjing, 210023, P. R. China.
Wenjun HuDepartment of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China. hu_wenjun@hust.edu.cn.
Gang Logan LiuDepartment of Nano Biosensing and Artificial Intelligence, College of Life Science and Technology, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, 430074, China. loganliu@hust.edu.cn.
Liping HuangState Key Laboratory of Microbial Technology, Nanjing Normal University, Nanjing, 210023, P. R. China. lphuang@nnu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 2021XXJS112State Key Laboratory for Diagnosis and Treatment of Infectious Diseases 2024ZZ00019
6 · The paper itself

Abstract

Investigating molecular interactions is crucial for advancing biological research and therapeutic discovery. Traditional analytical techniques often face limitations in sensitivity, quantification accuracy, and simplicity. Metasurfaces support resonances that are widely explored both for far-field wavefront shaping and for near-field sensing. Here, we introduce an innovative inverse-designed multilayer metasurface plasmon resonance (IDMM-SPR) sensor that overcomes these challenges. Using a double-objective optimization method that combines numerical simulations and machine learning, we developed an IDMM-SPR sensor featuring an optimized periodic nanocup array. This design yields unparalleled sensitivity and stability by harnessing collective resonances-including localized SPR, Wood's anomalies, and the Bloch wave SPR-which collectively enhance the sensing performance to enable the analysis of ultra-high affinity and low molecular weight interactions. The sensor achieves a figure of merit (FoM) of 26.3 and a detection limit (LOD) for C-reactive protein (CRP) as low as 0.84 ng/mL. Its compatibility with microplate absorbance readers and imaging detection makes it highly practical. The IDMM-SPR sensor shows exceptional promise for high-throughput direct molecular fishing, drug development, and disease diagnosis, offering a powerful tool for real-time, label-free affinity detection and quantitative analysis of biomolecular interactions.

Indexed as

Biosensing TechniquesC-Reactive ProteinSurface Plasmon ResonanceGoldHumansLimit of DetectionMachine LearningC-Reactive ProteinGoldAffinityBiosensorMetasurface SPRMolecular interactionNumerical simulation

Identifiers

PMID41402825
PMCPMC12821191

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.