4.2 Article

AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles

Journal

JACS AU
Volume 1, Issue 3, Pages 316-327

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/jacsau.0c00030

Keywords

transmission electron microscopy; nanoparticles; machine learning; unsupervised learning; image analysis

Funding

  1. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division within the Data Science for Data-Driven Synthesis Science (KCD2S2) [DE-AC02-05CH11231]
  2. National Institutes of Health [5U01GM121667]

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The AutoDetect-mNP algorithm is an unsupervised method that can automatically extract morphological information of convex mNPs from TEM images, providing objectivity and reliability while reducing human effort. It can serve as a generalizable measure for studying the morphology distributions of various mNP syntheses and will aid in future developments of high-throughput characterization and time-resolved TEM studies.
The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labeled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information on convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two data sets of bright field TEM images of Au nanoparticles with different shapes and further extended to palladium nanocubes and cadmium selenide quantum dots, demonstrating that the algorithm is quantitatively reliable and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of mNPs and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.

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