[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85898-en":3,"doc-seo-85898-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85898,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Evolution of AI from Image Interpretation Toward Scientific Inference in Nanoparticle Electron Microscopy","Artificial intelligence (AI) is reshaping electron microscopy by enabling quantitative analysis of large, complex datasets for nanoparticle characterization. Advances in machine learning, deep learning, and data-driven modeling extend microscopy from descriptive imaging to structural interpretation, dynamic analysis, and scientific inference. The review covers AI methods across TEM, HRTEM, STEM, and in situ TEM, addressing key challenges in detection, segmentation, morphology quantification, restoration, defect identification, and 2D-to-3D inference, while evaluating limitations and data needs.","The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy  \nEvropi Toulkeridou1 *, Jiafei Li2, Leonardo Lari3,4, and Panagiotis Grammatikopoulos5 *  \n1Statistical Analysis and Optimal Design of Experiments Group, Department of Mathematics, University of Castilla-La Mancha, 45071 Toledo, Spain  \n2Materials Science and Engineering, Guangdong Technion – Israel Institute of Technology, Shantou, Guangdong 515063, China  \n3LAME Laboratory, Area Science Park Campus di Basovizza, SS14, KM163 .5, 34149, Trieste, Italy 4The University of York, School of Physics, Electronics and Technology, Heslignton, YO10 5DD, York, UK  \n5Regional Institute for Applied Scientific Research (IRICA) and Department of Physics, University of Castilla-La Mancha, 13071, Ciudad Real, Spain  \nEmail: [evropi.toulkeridou@uclm.es](evropi.toulkeridou@uclm.es)  \n[Email: ](Email: p.grammatikopoulos@uclm.es)[p.grammatikopoulos@uclm.es](Email: p.grammatikopoulos@uclm.es)  \nABSTRACT  \nArtificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle  \ncharacterization. Recent advances in machine learning (ML) and deep learning (DL) have expanded microscopy from a descriptive imaging technique into a data-driven platform for structural interpretation, dynamic analysis, and scientific inference. This review examines AI methodologies for nanoparticle electron microscopy, focusing on transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), and in situ TEM. The  \ndiscussion is organized around the principal challenges in nanoparticle characterization, including particle detection, segmentation, morphology quantification, atomic-resolution restoration, defect identification, two-dimensional-to-three-dimensional structural inference, and analysis of dynamic processes in situ. We review computational approaches from conventional ML and convolutional neural networks to transformer architectures, self-supervised learning, foundation models, multimodal AI, and physics-informed learning. We further discuss integrating microscopy data with simulations, metadata, and autonomous experimentation to relate nanoparticle structure, dynamics, synthesis conditions, and functional properties. The advantages, limitations, benchmarking, and data requirements of current methodologies are critically assessed. Finally, emerging opportunities for foundation models, AI-guided microscopy, closed-loop experimentation, and autonomous materials discovery are discussed. By integrating advances across computer vision, materials informatics, and electron microscopy, this review highlights the role of AI in next-generation nanoparticle characterization and accelerated materials discovery.  \nKeywords: Electron Microscopy, Computer Vision, Artificial Intelligence, Nanoparticles  \n1. Introduction  \nNanoparticles constitute one of the most intensively investigated classes of materials owing to the unique physical and chemical properties that emerge at the nanoscale [1] . Their performance is governed not only by composition but also by particle size, morphology, crystallographic structure, surface chemistry, defect distributions, and interparticle interactions, all of which strongly influence their functional behavior and technological performance [2–6] . These characteristics influence a wide range of applications, including heterogeneous catalysis [2, 9], energy conversion and storage [3, 10], plasmonics [4], magnetic devices [5, 11], biomedical technologies [6], sensing platforms [7, 12, 12], and environmental remediation [8] . Consequently, accurate characterization of nanoparticle systems is essential for understanding structure– property relationships and guiding the rational design of advanced nanomaterials [2, 3, 5] .  \nElectron microscopy has become one of the most powe","cbCailKcuOvQQwpK","https://ap.wps.com/l/cbCailKcuOvQQwpK","pdf",2608264,5,1,53,"English","en",105,"# Abstract\n# Introduction\n## Nanoparticles and the need for accurate characterization\n## Electron microscopy techniques and information content\n## Challenges in interpreting microscopy data","[{\"question\":\"Why is accurate nanoparticle characterization important?\",\"answer\":\"Nanoparticle performance depends not only on composition but also on size, morphology, structure, surface chemistry, defect distributions, and interactions, which govern structure–property relationships and functional behavior.\"},{\"question\":\"What electron microscopy modalities are discussed in the review?\",\"answer\":\"The review focuses on transmission electron microscopy (TEM), high-resolution TEM (HRTEM), scanning transmission electron microscopy (STEM), diffraction-based methods such as 4D-STEM, and in situ TEM for observing dynamic processes.\"},{\"question\":\"What challenges motivate the use of AI in nanoparticle electron microscopy?\",\"answer\":\"Image interpretation remains labor-intensive due to manual measurements, threshold-based processing, and handcrafted feature extraction, which struggle to scale to high-throughput, atomic-resolution, and time-resolved in situ datasets.\"}]",1784207028,134,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-evolution-of-ai-from-image-interpretation-toward-scientific-inference-in-nanoparticle-electron-microscopy","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-evolution-of-ai-from-image-interpretation-toward-scientific-inference-in-nanoparticle-electron-microscopy/85898/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is accurate nanoparticle characterization important?","Question",{"text":76,"@type":77},"Nanoparticle performance depends not only on composition but also on size, morphology, structure, surface chemistry, defect distributions, and interactions, which govern structure–property relationships and functional behavior.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What electron microscopy modalities are discussed in the review?",{"text":81,"@type":77},"The review focuses on transmission electron microscopy (TEM), high-resolution TEM (HRTEM), scanning transmission electron microscopy (STEM), diffraction-based methods such as 4D-STEM, and in situ TEM for observing dynamic processes.",{"name":83,"@type":74,"acceptedAnswer":84},"What challenges motivate the use of AI in nanoparticle electron microscopy?",{"text":85,"@type":77},"Image interpretation remains labor-intensive due to manual measurements, threshold-based processing, and handcrafted feature extraction, which struggle to scale 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