ArticleScientific data2020
Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Data-driven ELNES/XANES analysis: predicting spectra, unveiling structures and quantifying properties.Microscopy (Oxford, England) · 2026Article
- Image-to-molecule benchmarking dataset with fractal pattern and hierarchical morphology recognition.Scientific data · 2026Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Article
- Automated Recognition of Nanoparticles in Electron Microscopy Images of Nanoscale Palladium Catalysts.Nanomaterials (Basel, Switzerland) · 2022Article
- Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials.Chemical science · 2021Article
- Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles.Scientific data · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A unique ordering effect has been observed in functional catalytic nanoscale materials. Instead of randomly arranged binding to the catalyst surface, metal nanoparticles show spatially ordered behavior resulting in formation of geometrical patterns. Understanding of such nanoscale materials and analysis of corresponding microscopy images will never be comprehensive without appropriate reference datasets. Here we describe the first dataset of electron microscopy images comprising individual nanoparticles which undergo ordering on a surface towards the formation of geometrical patterns. The dataset developed in this study spans three levels of nanoscale organization: (i) individual nanoparticles (1-5 nm) and arrays of nanoparticles (5-20 nm), (ii) ordering effects (20-200 nm) and (iii) complex patterns (from nm to μm scales). The described dataset for the first time provides a possibility for the development of machine learning algorithms to study the unique phenomena of nanoparticles ordering and hierarchical organization.
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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.