ArticleNanoscale advances2023
Automated analysis of transmission electron micrographs of metallic nanoparticles by machine learning.
Article in Nanoscale advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- A Lightweight Data-Augmented Deep Learning Framework for Real-Time Instance Segmentation in Liquid-Phase In Situ Transmission Electron Microscopy.ACS measurement science au · 2026Article
- Fabrication of pantoprazole-loaded microsphere clusters with enteric and mucoadhesive properties for enhanced ulcer targeting.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Tuning DLVO Interactions Alters Polymer-Mediated pDNA Delivery in a Cell Type-Dependent Manner.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Ultrasmall Gold Nanoparticles (2 nm) Decorated with a High Density of Photochemically Switchable Ligands.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Practical Guide to Automated TEM Image Analysis for Increased Accuracy and Precision in the Measurement of Particle Size and Morphology.ACS nanoscience Au · 2025Article
- Assessing particle count in electron microscopy measurements of nanomaterials to support regulatory guidance.Scientific reports · 2025Article
- Tutorial on In Situ andACS nano · 2024Review
- Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization.ACS nano · 2024Article
- Exploiting Temporal Features in Calculating Automated Morphological Properties of Spiky Nanoparticles Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Simulated HRTEM images of nanoparticles to train a neural network to classify nanoparticles for crystallinity.Nanoscale advances · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metallic nanoparticles were analysed with respect to size and shape by a machine learning approach. This involved a separation of particles from the background (segmentation), a separation of overlapping particles, and the identification of individual particles. An algorithm to separate overlapping particles, based on ultimate erosion of convex shapes (UECS), was implemented. Finally, particle properties like size, circularity, equivalent diameter, and Feret diameter were computed for each particle of the whole particle population. Thus, particle size distributions can be easily created based on the various parameters. However, strongly overlapping particles are difficult and sometimes impossible to separate because of an
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.