ArticleNature communications2020
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Conversion-Type Anodes: Challenges, Mitigation Strategies, and Evaluation Guidelines.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-State Battery Cathodes.ACS energy letters · 2026Article
- Chemical heterogeneity for battery materials.Nature reviews. Chemistry · 2026Review
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Seeing the Middle: Reconstructing 3D Internal Electrode Microstructures from Low-Resolution Surfaces with Generative Diffusion Artificial Intelligence.Small science · 2025Article
- Ionically Conductive Elastic Polymer Binder for Ultrahigh Loading Electrode in High-Energy-Density Lithium Batteries.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Impacts of the Conductive Networks on Solid-State Battery Operation.Angewandte Chemie (International ed. in English) · 2025Article
- Enabling High-Performance Electrode Materials for Potassium-Ion Batteries: Ionic Transport, Size and Electro-Chemo-Mechanical Effects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning.Nature communications · 2025Article
- Enhanced Electrical Connectivity in High Energy Density Single-Crystal NCA Electrodes via Polycrystalline Blending Design.ACS applied materials & interfaces · 2025Article
- A comprehensive and quantitative SEM-EDS analytical process applied to lithium-ion battery electrodes.Scientific reports · 2025Article
- Revealing the Chemical and Structural Complexity of Electrochemical Ion Exchange in Layered Oxide Materials.Journal of the American Chemical Society · 2024Article
- Probing Multiscale Dynamics of Energy-Dense Batteries by Operando Imaging.Chem & bio engineering · 2024Review
- Asynchronous domain dynamics and equilibration in layered oxide battery cathode.Nature communications · 2023Article
- Multiscale Imaging Techniques for Real-Time, Noninvasive Diagnosis of Li-Ion Battery Failures.Small science · 2023Article
- Methods for Tomographic Segmentation in Pseudo-Cylindrical Coordinates for Bobbin-Type Batteries.ACS measurement science au · 2023Article
- X-ray Tomography Applied to Electrochemical Devices and Electrocatalysis.Chemical reviews · 2023Review
- Full-field hard X-ray nano-tomography at SSRF.Journal of synchrotron radiation · 2023Article
- Deep-learning-based ring artifact correction for tomographic reconstruction.Journal of synchrotron radiation · 2023Article
- Effective transport network driven by tortuosity gradient enables high-electrochem-active solid-state batteries.National science review · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles' evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode's microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity.
Identifiers
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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.