ReviewNature nanotechnology2022
Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence.
Review in Nature nanotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Fabrication of Catalytic Distillation Membranes with Atomic Layer Deposition.ACS applied materials & interfaces · 2026Article
- Advanced Technologies for Characterizing and Detecting Battery Thermal Failure: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Designing High-Performance Dual-Ion Batteries at High-Voltage: Challenges, Strategies, and Prospects.Nano-micro letters · 2026Review
- Advancing Battery Manufacturing: Synchrotron Characterization for Industry.Chemical reviews · 2026Review
- Scaling deep learning for material imaging with a pseudo 3D model for domain transfer.Nature communications · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- X‑ray Micro-Computed Tomography for Structural Analysis of All-Solid-State Battery at Pouch Cell Level.ACS energy letters · 2025Review
- Visualization of the hatching of brine shrimp eggs using ultrafast and high-resolution phase-contrast CTs.APL bioengineering · 2025Article
- Characterizing Electrode Materials and Interfaces in Solid-State Batteries.Chemical reviews · 2025Review
- Enhancing Mechanical Resilience in Li-Ion Battery Cathodes with Nanoscale Elastic Framework Coatings.ACS nano · 2025Article
- Article
- Origin of fast charging in solid-state batteries revealed by Cryo-transmission X-ray microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Computed tomography technologies to measure key structural features of polymeric biomedical implants from bench to bedside.Journal of biomedical materials research. Part A · 2024Article
- Revealing and reconstructing the 3D Li-ion transportation network for superionic poly(ethylene) oxide conductor.Nature communications · 2024Article
- Visualization of CONature chemistry · 2024Article
- Rescuing zinc anode-electrolyte interface: mechanisms, theoretical simulations andChemical science · 2024Review
- Designing electrolytes and interphases for high-energy lithium batteries.Nature reviews. Chemistry · 2024Review
- Application of neutron imaging in observing various states of matter inside lithium batteries.National science review · 2023Review
- Recent developments in X-ray diffraction/scattering computed tomography for materials science.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Methods for Tomographic Segmentation in Pseudo-Cylindrical Coordinates for Bobbin-Type Batteries.ACS measurement science au · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.
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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.