ArticleNanomaterials (Basel, Switzerland)2021
Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images.
Article in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Unraveling the structural and optical properties of WORSC advances · 2026Article
- Review
- Morphological analysis of Pd/C nanoparticles using SEM imaging and advanced deep learning.RSC advances · 2024Article
- Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds.Nanomaterials (Basel, Switzerland) · 2024Article
- Article
- Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases.Nanoscale advances · 2023Review
- Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.ACS nanoscience Au · 2023Article
- Metrology of convex-shaped nanoparticlesNanoscale advances · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.