Phosphate Polyanion Materials as High-Voltage Lithium-Ion Battery Cathode: A Review
Bibliographic record
Abstract
Followed by decades of successful efforts in developing cathode materials for high specific capacity lithium-ion batteries, currently the attention is on developing a high-voltage battery (>5 V vs Li/Li + ) with an aim to increase the energy density for their many fold advantages over conventional <4 V batteries. Among the various cathode materials, phosphate polyanion materials (LiMPO 4, where M is a single metal or a combination of metals) showed promising candidacy given their high electrochemical potential (4.8–5 V vs Li/Li + ), long cycle stability, low cost, and achieved specific capacity (∼165 mAh·g –1 ) near to its theoretical limit (170 mAh·g –1 ). In this review, factors affecting the electrochemical potential of the cathode materials are reviewed and discussed. Techniques to improve the electrical and ionic conductivities of phosphate polyanion cathodes, namely, surface coating, particle size reduction, doping, and morphology engineering, are also discussed. A processing–property correlation in phosphate polyanion materials is also undertaken to understand relative merits and drawbacks of diverse processing techniques to deliver a material with targeted functionality. Strategies required for high-voltage phosphate polyanion cathode materials are envisioned, which are expected to deliver lithium-ion battery cathodes with higher working potential and gravimetric specific capacity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".