Evidence map›Paper›PMID 40277455›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Gradient Nanostructures and Machine Learning Synergy for Robust Quantitative Surface-Enhanced Raman Scattering.

Xiaoyu Zhao, Yuxia Wang, Yuting Liu, Xinyi Chen, Mingyu Cheng, Yaxin Wang, Jiahong Wen, Renxian Gao, Kun Zhang, Fengyi Zhang and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

14 authors.

Xiaoyu ZhaoCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Yuxia WangCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.ORCID https://orcid.org/0009-0002-1872-2290
Yuting LiuSchool of Microelectronics and Communication Engieerimng, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing, 400044, P. R. China.
Xinyi ChenSchool of Microelectronics and Communication Engieerimng, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing, 400044, P. R. China.
Mingyu ChengSchool of Microelectronics and Communication Engieerimng, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing, 400044, P. R. China.
Yaxin WangCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Jiahong WenThe College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, P. R. China.
Renxian GaoCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Kun ZhangCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Fengyi ZhangCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Rufei CuiCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Yongjun ZhangCollege of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, P. R. China.
Zengyao WangShandong Second Medical University, Weifang, Shandong, 261053, P. R. China.
Bin AiSchool of Microelectronics and Communication Engieerimng, Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing, Chongqing University, Chongqing, 400044, P. R. China.ORCID https://orcid.org/0000-0002-0502-9166

Funding

Fundamental Research Funds for the Provincial Universities of Zhejiang GK239909299001-007 GK239909299001- 027 GK229909299001-028 GK249909299001-307Key R&D project of Zhejiang Province 2024C03258National Natural Science Foundation of China 62105043 22202155 12374345 U24A20103 12404358 12304309Natural Science Foundation of Shandong Province ZR2021QB092
6 · The paper itself

Abstract

Surface-Enhanced Raman Scattering (SERS) holds significant promise for trace-level molecular detection but faces challenges in achieving reliable quantitative analysis due to signal variability caused by non-uniform "hot spots" and external factors. To address these limitations, a novel SERS platform based on gradient nanostructures is developed using shadow sphere lithography, enabling the acquisition of diverse spectral features from a single analyte concentration under identical conditions. The gradient design minimizes fabrication variability and enhances spectral diversity, while the machine learning (ML) model trained on the multi-spectral dataset significantly outperformed traditional single-spectrum approaches, with the test Mean Squared Error (MSE) reduced by 84.8% and the coefficient of determination (R

Indexed as

Gradient Nanostructuresmachine learningshadow sphere lithographySurface‐Enhanced Raman Scattering

Identifiers

PMID40277455
PMCPMC12244994

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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.