High‐Throughput Synthesis of Mn‐Based Disordered Rock‐Salt Li‐Ion Cathodes with Improved Rate Capability via Rapid Joule‐Heating
Bibliographic record
Abstract
Abstract Manganese‐based disordered rock‐salt (Mn‐DRX) cathodes provide high energy density and exceptional compositional tunability, which enables the rational design and discovery of advanced DRX chemistries. However, exploration across the vast Mn‐DRX design space remains hindered by the slow and energy‐intensive nature of traditional synthesis methods. Here, the Joule‐heating (JH) method is demonstrated to enable rapid and energy‐efficient synthesis of high‐performance Mn‐DRX cathodes. Through a case study of Li 1.2 Mn 0.4 Ti 0.4 O 2 (LMTO), we show that optimizing DRX materials under rapid synthesis requires controlling both nanoscale short‐range ordering (SRO) and microscale features such as bulk homogeneity and impurity phases, highlighting that microscale features are as critical as SRO. Based on this understanding, we show that a JH‐synthesized LMTO (annealed at 1050 °C just for 10 min) achieves a near‐phase‐pure DRX structure with uniform elemental distribution and reduced SRO compared to furnace‐synthesized LMTO (annealed at 950 °C for 12 h), leading to improved rate capability (161 vs 137 mAh g −1 at 1 A/g). Additionally, using the JH system, various DRX compositions are synthesized with electrochemical performance comparable to that of furnace‐synthesized counterparts, demonstrating its versatility. These findings demonstrate that rapid JH synthesis offers a robust approach to producing high‐performance Mn‐DRX cathodes, paving the way for high‐throughput discovery across the vast DRX design space.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".