Evidence map›Paper›PMID 39792780›Full record

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

Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy.

Jingan Wang, Yi Sun, Yuting Yang, Cheng Zhang, Weiqiang Zheng, Chen Wang, Wei Zhang, Lianqun Zhou, Hui Yu, Jinghong Li

Erratum issuedAbstract 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. An erratum has been issued. 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. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Jingan WangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.ORCID https://orcid.org/0009-0004-7315-071X
Yi SunDepartment of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing, 100084, China.
Yuting YangSchool of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
Cheng ZhangSchool of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
Weiqiang ZhengSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
Chen WangSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.
Wei ZhangSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Lianqun ZhouSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Hui YuSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.ORCID https://orcid.org/0000-0003-4430-2230
Jinghong LiDepartment of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing, 100084, China.ORCID https://orcid.org/0000-0002-0750-7352

Funding

Beijing Life Science Academy Initiative Scientific Research Program 2023000CA0050Beijing Life Science Academy Initiative Scientific Research Program 2023100CC0240Beijing Life Science Academy Initiative Scientific Research Program 2023100CC0250China Postdoctoral Science Foundation 2022M711786National Key Research and Development Program of China 2022YFF1202000National Key Research and Development Program of China (subproject2022YFF1202001)National Natural Science Foundation of China 22027807National Natural Science Foundation of China 62271315New Cornerstone Science Foundation
6 · The paper itself

Abstract

Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles. Image sequences are recorded by the state-of-the-art plasmonic microscopy during single nanoparticle collision onto the sensor surface. Deep-SM can enhance signal detection and suppresses noise by leveraging spatio-temporal correlations of the unique signal and noise characteristics in plasmonic microscopy image sequences. Deep-SM can provide significant scattering signal enhancement and noise reduction in dynamic imaging of biological nanoparticles as small as 10 nm, as well as the collision detection of metallic nanoparticle electrochemistry and quantum coupling with plasmonic microscopy. The high sensitivity and simplicity make this approach promising for routine use in nanoparticle analysis across diverse scientific fields.

Indexed as

collisiondeep learningmicroscopynanoparticleplasmonic

Identifiers

PMID39792780
PMCPMC11884610

What OpenQuestion holds

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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.