Obinna Onyebuchi Barah, Mushabe David, Malisaba Joseph
{"title":"Atomic-scale characterization: a review of advances in microscopy, spectroscopy, and machine learning","authors":"Obinna Onyebuchi Barah, Mushabe David, Malisaba Joseph","doi":"10.1186/s42252-025-00073-x","DOIUrl":null,"url":null,"abstract":"<div><p>Atomic-scale characterization is pivotal in elucidating structure–property relationships across quantum materials, energy systems, and biological nanostructures. This review critically examines recent advances in high-resolution imaging, aberration-corrected TEM/STEM, cryo-EM, scanning probe microscopy (SPM), and helium ion microscopy (HIM), alongside spectroscopies such as electron energy loss spectroscopy (EELS), tip-enhanced Raman spectroscopy (TERS), and atom probe tomography (APT). Particular focus is placed on their integration with in-situ/operando environments and AI-driven workflows, enabling real-time, multimodal analysis at sub-ångström resolutions. We propose a unified framework combining machine learning, unsupervised clustering, and automated data pipelines to accelerate insight extraction and materials design. Case studies highlight this convergence: perovskite solar cells reached 25.7% efficiency through defect passivation guided by TEM; silicon–carbon anodes retained over 80% capacity across 1,000 cycles via nanostructure-informed optimization; cryo-EM resolved biomolecular assemblies below 2 Å with direct electron detection; and 4D-STEM enabled atomic-scale 3D reconstructions in cathodes with 0.3 nm precision. These tools have revealed critical structure–function linkages, such as lithium heterogeneity in nickel-manganese-cobalt (NMC) cathodes driving capacity fade, and PbI₂ segregation at perovskite grain boundaries impairing photovoltaic performance. Persistent challenges-resolution-dose tradeoffs, dataset reproducibility, and global disparities in instrumentation access are also assessed. Future directions include quantum-enhanced metrology and cloud-based remote experimentation. This review presents an integrated, forward-looking perspective on the fusion of atomic-scale metrology and autonomous experimentation, outlining a strategic roadmap to accelerate materials discovery while foregrounding sustainability, equity, and open-access principles often overlooked in prior literature.</p></div>","PeriodicalId":576,"journal":{"name":"Functional Composite Materials","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jmscomposites.springeropen.com/counter/pdf/10.1186/s42252-025-00073-x","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Functional Composite Materials","FirstCategoryId":"1","ListUrlMain":"https://link.springer.com/article/10.1186/s42252-025-00073-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Atomic-scale characterization is pivotal in elucidating structure–property relationships across quantum materials, energy systems, and biological nanostructures. This review critically examines recent advances in high-resolution imaging, aberration-corrected TEM/STEM, cryo-EM, scanning probe microscopy (SPM), and helium ion microscopy (HIM), alongside spectroscopies such as electron energy loss spectroscopy (EELS), tip-enhanced Raman spectroscopy (TERS), and atom probe tomography (APT). Particular focus is placed on their integration with in-situ/operando environments and AI-driven workflows, enabling real-time, multimodal analysis at sub-ångström resolutions. We propose a unified framework combining machine learning, unsupervised clustering, and automated data pipelines to accelerate insight extraction and materials design. Case studies highlight this convergence: perovskite solar cells reached 25.7% efficiency through defect passivation guided by TEM; silicon–carbon anodes retained over 80% capacity across 1,000 cycles via nanostructure-informed optimization; cryo-EM resolved biomolecular assemblies below 2 Å with direct electron detection; and 4D-STEM enabled atomic-scale 3D reconstructions in cathodes with 0.3 nm precision. These tools have revealed critical structure–function linkages, such as lithium heterogeneity in nickel-manganese-cobalt (NMC) cathodes driving capacity fade, and PbI₂ segregation at perovskite grain boundaries impairing photovoltaic performance. Persistent challenges-resolution-dose tradeoffs, dataset reproducibility, and global disparities in instrumentation access are also assessed. Future directions include quantum-enhanced metrology and cloud-based remote experimentation. This review presents an integrated, forward-looking perspective on the fusion of atomic-scale metrology and autonomous experimentation, outlining a strategic roadmap to accelerate materials discovery while foregrounding sustainability, equity, and open-access principles often overlooked in prior literature.