Toward Practical Implementation of Cation-Disordered Rock-Salt Cathodes for Sustainable Li-Ion Batteries
Bibliographic record
Abstract
Professor Gerbrand Ceder has made pioneering contributions to the discovery and optimization of novel materials for electrochemical energy storage by integrating computational and experimental approaches. In particular, his work on cation-disordered rock-salt (DRX) cathodes has opened new avenues for developing high-performance, low-cost Li-ion batteries free of nickel and cobalt [1-3]. In this talk, I will discuss the key challenges associated with the practical implementation of these materials, with a focus on their electrical properties, chemo-mechanical behavior, and microstructural limitations. Drawing from our recent publications [4,5], I will also highlight how engineering the carbon/binder system in DRX cathodes and tailoring particle morphology can effectively address some of these critical issues. [1] J. Lee, G. Ceder et al., Science 343, 519-522 (2014) [2] R. Clément, Z. Lun, G. Ceder, Energy Environ. Sci. 13, 345-373 (2020) [3] H. Li, R. Fong, J. Lee et al., Joule 6, 53-91 (2022) [4] E. Lee, D.-H. Lee, D.-H. Seo, J. Lee, Energy Environ. Sci. 17, 3753-3764 (2024) [5] H. Ahmed, J. Lee et al., https://doi.org/10.21203/rs.3.rs-5154732/v1 (Under revision)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".