ArticleNature communications2021
Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 42 citations in OpenAlex.
- Deep learning for scalable atomic electron tomography.National science review · 2026Article
- A general data-driven framework for scalable electron tomography.National science review · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Generative-model-based null-space iterative reconstruction for atomic electron tomography with sparse data.Nature communications · 2026Article
- Morphology prediction of small nanoparticles in any orientation from single electron micrographs.npj computational materials · 2026Article
- Article
- PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval.Nature communications · 2025Article
- Atomic-scale 3D structural dynamics and functional degradation of Pt alloy nanocatalysts during the oxygen reduction reaction.Nature communications · 2025Article
- Characterizing DNA Origami Nanostructures in TEM Images Using Convolutional Neural Networks.Journal of chemical information and modeling · 2025Article
- Advancing atomic electron tomography with neural networks.Applied microscopy · 2025Review
- The Essential Role of Well-Defined Materials in Assessing Electrocatalyst Structure and Function.ACS materials Au · 2025Review
- Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution.Nanomaterials (Basel, Switzerland) · 2024Article
- Revealing the three-dimensional arrangement of polar topology in nanoparticles.Nature communications · 2024Article
- Three-dimensional reconstruction of Y-IrNi rhombic dodecahedron nanoframe by STEM/EDS tomography.Applied microscopy · 2023Article
- Probing the atomically diffuse interfaces in Pd@Pt core-shell nanoparticles in three dimensions.Nature communications · 2023Article
- Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Functional properties of nanomaterials strongly depend on their surface atomic structures, but they often become largely different from their bulk structures, exhibiting surface reconstructions and relaxations. However, most of the surface characterization methods are either limited to 2D measurements or not reaching to true 3D atomic-scale resolution, and single-atom level determination of the 3D surface atomic structure for general 3D nanomaterials still remains elusive. Here we demonstrate the measurement of 3D atomic structure at 15 pm precision using a Pt nanoparticle as a model system. Aided by a deep learning-based missing data retrieval combined with atomic electron tomography, the surface atomic structure was reliably measured. We found that <[Formula: see text]> and <[Formula: see text]> facets contribute differently to the surface strain, resulting in anisotropic strain distribution as well as compressive support boundary effect. The capability of single-atom level surface characterization will not only deepen our understanding of the functional properties of nanomaterials but also open a new door for fine tailoring of their performance.
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Registered trials
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