Evidence map›Paper›PMID 41441496›Full record

ArticleNanomaterials (Basel, Switzerland)2025

Automated Particle Size Analysis of Supported Nanoparticle TEM Images Using a Pre-Trained SAM Model.

Xiukun Zhong, Guohong Liang, Lingbei Meng, Wei Xi, Lin Gu, Nana Tian, Yong Zhai, Yutong He, Yuqiong Huang, Fengmin Jin and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 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. Review
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

11 authors.

Xiukun ZhongSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Guohong LiangSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Lingbei MengSchool of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen 518172, China.
Wei XiSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Lin GuSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.ORCID 0009-0006-9540-1326
Nana TianSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Yong ZhaiSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Yutong HeSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Yuqiong HuangSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Fengmin JinSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Hong GaoSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.

Funding

National Natural Science Foundation of China 52375155
6 · The paper itself

Abstract

This study addresses the challenges associated with transmission electron microscopy (TEM) image analysis of supported nanoparticles, including low signal-to-noise ratio, poor contrast, and interference from complex substrate backgrounds. This study proposes an automated segmentation and particle size analysis method based on a large-scale deep learning model, namely segment anything model (SAM). Using Ru/TiO

Indexed as

deep learningimage segmentationparticle size analysissegment anything modelsupported nanoparticlestransmission electron microscopy

Identifiers

PMID41441496
PMCPMC12736136

What OpenQuestion 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 OpenQuestion graph.