Nanocomposite Engineering of a High‐Capacity Partially Ordered Cathode for Li‐Ion Batteries
Bibliographic record
Abstract
Abstract Understanding the local cation order in the crystal structure and its correlation with electrochemical performances has advanced the development of high‐energy Mn‐rich cathode materials for Li‐ion batteries, notably Li‐ and Mn‐rich layered cathodes (LMR, e.g., Li 1.2 Ni 0.13 Mn 0.54 Co 0.13 O 2 ) that are considered as nanocomposite layered materials with C2/m Li 2 MnO 3 ‐type medium‐range order (MRO). Moreover, the Li‐transport rate in high‐capacity Mn‐based disordered rock‐salt (DRX) cathodes (e.g., Li 1.2 Mn 0.4 Ti 0.4 O 2 ) is found to be influenced by the short‐range order of cations, underlining the importance of engineering the local cation order in designing high‐energy materials. Herein, the nanocomposite is revealed, with a heterogeneous nature (like MRO found in LMR) of ultrahigh‐capacity partially ordered cathodes (e.g., Li 1.68 Mn 1.6 O 3.7 F 0.3 ) made of distinct domains of spinel‐, DRX‐ and layered‐like phases, contrary to conventional single‐phase DRX cathodes. This multi‐scale understanding of ordering informs engineering the nanocomposite material via Ti doping, altering the intra‐particle characteristics to increase the content of the rock‐salt phase and heterogeneity within a particle. This strategy markedly improves the reversibility of both Mn‐ and O‐redox processes to enhance the cycling stability of the partially ordered DRX cathodes (nearly ≈30% improvement of capacity retention). This work sheds light on the importance of nanocomposite engineering to develop ultrahigh‐performance, low‐cost Li‐ion cathode materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".