Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution
Abstract
:1. Introduction
2. Materials and Methods
2.1. Gabor Filter
2.2. Non-Negative Matrix Factorization
2.3. K-Means Clustering
2.4. Synthesis of Au Nanoparticles
2.5. Synthesis of PtNi Intermetallic Nanoparticles
2.6. Synthesis of PtCo Intermetallic Nanoparticles
2.7. STEM Characterization and Simulation of Nanoparticles
3. Results and Discussion
3.1. Segmentation of Polycrystalline Nanoparticles
3.2. Potential of the Methodology: Capturing Unknown Features
3.3. Automated Segmentation of Nanoparticles
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sohn, W.; Kim, T.; Moon, C.W.; Shin, D.; Park, Y.; Jin, H.; Baik, H. Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution. Nanomaterials 2024, 14, 1614. https://doi.org/10.3390/nano14201614
Sohn W, Kim T, Moon CW, Shin D, Park Y, Jin H, Baik H. Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution. Nanomaterials. 2024; 14(20):1614. https://doi.org/10.3390/nano14201614
Chicago/Turabian StyleSohn, Woonbae, Taekyung Kim, Cheon Woo Moon, Dongbin Shin, Yeji Park, Haneul Jin, and Hionsuck Baik. 2024. "Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution" Nanomaterials 14, no. 20: 1614. https://doi.org/10.3390/nano14201614
APA StyleSohn, W., Kim, T., Moon, C. W., Shin, D., Park, Y., Jin, H., & Baik, H. (2024). Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution. Nanomaterials, 14(20), 1614. https://doi.org/10.3390/nano14201614