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Related Experiment Video

Updated: May 5, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

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Published on: November 12, 2014

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical

Min Gee Cho^1,2, Katherine Sytwu¹, Luis Rangel DaCosta^1,2

¹National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.

|October 19, 2024

Summary

This summary is machine-generated.

Deep learning reveals how cobalt oxide nanocrystal shape evolves with size. This study quantifies growth transitions, enabling precise control for advanced material properties and applications.

Keywords:

deep learning growth regimes high-resolution TEM high-throughput statistical analysis image analysis nanocrystal synthesis size-resolved analysis

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Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

Published on: December 4, 2020

Area of Science:

Materials Science
Nanotechnology
Chemical Engineering

Background:

Precise control over nanocrystal size and shape is critical for applications in catalysis, sensing, and energy.
Traditional methods often fail to capture individual nanocrystal variations, limiting structure-property understanding.

Purpose of the Study:

To investigate the detailed shape evolution and growth mechanisms of cobalt oxide (Co3O4) nanocrystals at the subnanometer scale.
To establish robust structure-property relationships by analyzing size-dependent shape changes.

Main Methods:

Utilized deep-learning-assisted statistical characterization on high-resolution electron microscopy images.
Analyzed population-wide data from over 441,067 individual nanocrystals.
Controlled synthesis parameters including cobalt precursor concentration and water amount.

Main Results:

Uncovered previously unobserved size-resolved shape evolution in Co3O4 nanocrystals.
Quantified transitions in growth regimes, including faceting and thermodynamic-to-kinetic shifts, evidenced by convex to concave polyhedral changes.
Introduced the concept of "onset radius" for critical size thresholds of these transitions.

Conclusions:

High-throughput statistical analysis is essential for accurate population representation and studying size-dependent nanocrystal growth.
The identified "onset radius" provides critical size thresholds for controlling nanocrystal morphology.
Findings enable finely tuned correlations between nanocrystal geometry and material properties, advancing synthesis and applications.