ArticleJACS Au2021
AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles.
Article in JACS Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Evaluating Pt Nanoparticle Electrocatalyst Stability on Corrosion Free Carbon Supports Using Identical Location TEM.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Parameter-Free Determination of Au Nanorod Dimensions Using Depolarized DLS and Genetic Optimization.The journal of physical chemistry. B · 2026Article
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach.Scientific reports · 2024Article
- Article
- Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives.International journal of molecular sciences · 2024Review
- Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization.ACS nano · 2024Article
- Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds.Nanomaterials (Basel, Switzerland) · 2024Article
- Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.Sensors (Basel, Switzerland) · 2024Review
- Review
- Automated analysis of transmission electron micrographs of metallic nanoparticles by machine learning.Nanoscale advances · 2023Article
- Navigating with chemometrics and machine learning in chemistry.Artificial intelligence review · 2023Article
- DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time.Scientific reports · 2022Article
- Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities.Scientific data · 2022Article
- Rapid DNA origami nanostructure detection and classification using the YOLOv5 deep convolutional neural network.Scientific reports · 2022Article
- Mechanism and performance relevance of nanomorphogenesis in polyamide films revealed by quantitative 3D imaging and machine learning.Science advances · 2022Article
- A Nanoscale Shape-Discovery Framework Supporting Systematic Investigations of Shape-Dependent Biological Effects and Immunomodulation.ACS nano · 2022Article
- Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm.Scanning · 2022Article
- Metrology of convex-shaped nanoparticlesNanoscale advances · 2021Article
- Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images.Nanomaterials (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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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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.