High Energy Lithium-Ion Battery Using Substituted LiCoPO<sub>4</sub>
Bibliographic record
Abstract
There is a constant drive to improve the specific energy and energy density of the state-of-the-art lithium-ion battery. High energy can be achieved by either choosing a cathode material that operates at a higher potential or has a higher specific capacity. Compared with the conventional 4-V cathodes such as LiCoO2 (LCO), LiNi x Co y Al z O2 (NCA), LiMn2O4 (LMO) and 3.5-V LiFePO4 (LFP), LiCoPO4 (LCP) operates at 4.8 V (vs. Li/Li+) with a theoretic capacity of 167 mAh g-1, resulting in a specific energy of ~800 Wh kg-1, which is about 25% higher than those of the conventional LFP and LMO lithium-ion batteries. Although Co is more expensive than the other transition metals, the energy cost of LCP is expected to be cheaper than all commercialized lithium-ion batteries on the market [1] due to the improved energy density (FIG.1). However, LCP suffers from severe capacity fade due to the low intrinsic electronic/ionic conductivity, structure deterioration and electrolyte decomposition [2]. In order to improve the cyclability and reduce the cost of LCP, Co ions were partially substituted by cheaper elements such as Fe, Cr and Si etc [3]. Meanwhile, a carbon-coating was used to improve the electronic conductivity of LCP. Electrochemical tests showed that both carbon-coating and substitution greatly improved the cycling performance of LCP, which suggests that the substituted LCP is a very promising cathode candidate for high energy lithium-ion battery. References [1] S. Brutti, S. Panero, ACS Symposium Series, 1140, Chapter 4, 69 (2013). [2] K. Tadanaga, F. Mizuno, A. Hayashi, T. Minami, M. Tatsumisago, Electrochemistry 71, 1192 (2003). [3] J. L. Allen, J. L. Allen, T. Thompson, S. A. Delp, J. Wolfenstine, T. Richard Jow, J. Power Sources, 327, 229 (2016). FIG. 1. Energy density and energy cost of LCP compared with other cathode materials currently used in lithium-ion batteries. Figure 1
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".