High Energy Density Flake-Type LiMn <sub>0.8</sub> Fe <sub>0.2</sub> PO <sub>4</sub>
Bibliographic record
Abstract
Low surface area and dense LiMn 0.8 Fe 0.2 PO 4 /graphite composite particles (F-LMFP2) made with an all-dry process are described that demonstrate superior electrochemical performance and higher volumetric energy densities than conventional LiMn 0.8 Fe 0.2 PO 4 (LMFP). The F-LMFP2 material exhibited a flake-like morphology, resulting in a low surface area (3.93 m 2 g −1 ) and excellent packing properties, allowing calendered electrodes to reach a high coating density of 2.26 g ml -1 without electrode delamination. When cycled in Li half-cells, calendered F-LMFP2 electrodes demonstrated superior capacity retention (131.3 mAh g −1 /100 cycles), lower polarization, improved rate performance than conventional LMFP, which suffered from rapid capacity fade when densified. Moreover, the ability of F-LMFP2 to be highly densified, while retaining good electrochemical performance resulted in a high volumetric energy density of 1144 Wh l −1 to be obtained, representing a 40% increase over conventional LMFP. These results highlight the potential of dry particle processing techniques to produce high-density olivine-based cathodes, enabling cost-effective and sustainable Li-ion cells with high volumetric energy density.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".