Unlocking 3D Nanoparticle Shapes from 2D High-Resolution Transmission Electron Microscopy images: A Deep Learning Breakthrough

Unlocking 3D Nanoparticle Shapes from 2D High-Resolution Transmission Electron Microscopy images: A Deep Learning Breakthrough | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unlocking 3D Nanoparticle Shapes from 2D High-Resolution Transmission Electron Microscopy images: A Deep Learning Breakthrough Romain Moreau, Hakim Amara, Maxime Moreaud, Jaysen Nelayah, Adrien Moncomble, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7158591/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The analysis of high-resolution transmission electron microscopy (HRTEM) images with atomic resolution remains limited due to the lack of automated methods capable of processing a statistically significant number of nano-objects. In particular, the accurate characterization of nanoparticle (NP) morphology at the nanoscale remains a major challenge. To address this issue, we have developed a deep learning (DL) framework trained on a dataset of simulated HRTEM images of NPs ranging in size from 4 to 8 nm, labeled according to their 3D shape. The dataset is generated by constructing atomistic models of NPs deposited on amorphous carbon and subjected to random rotations in order to account for all possible orientations observed in experimental samples. HRTEM images are then simulated using the multi-slice method, to mimic experimental acquisition of an aberration-corrected transmission electron microscope. A systematic study is carried out to evaluate the impact of different key physical and numerical quantities (amorphous carbon structure, image resolution, focusing conditions, NP size and orientation) on the predictive performance of the DL model. Finally, a robust and accurate framework is developped and proposed for inferring 3D NP morphologies from 2D HRTEM images, validated on both simulated and experimental datasets. Physical sciences/Materials science Physical sciences/Nanoscience and technology Full Text Additional Declarations No competing interests reported. Supplementary Files 3DShapeIATEMSM.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 26 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 21 Jul, 2025 First submitted to journal 18 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7158591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496208199,"identity":"37500561-6105-4d73-8f71-5f9ff1b6df3f","order_by":0,"name":"Romain Moreau","email":"","orcid":"","institution":"Laboratoire d'Étude des Microstructures","correspondingAuthor":false,"prefix":"","firstName":"Romain","middleName":"","lastName":"Moreau","suffix":""},{"id":496208200,"identity":"21060af2-b530-4cf3-aa5c-d08d9c93fc8c","order_by":1,"name":"Hakim 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