Optimization of Loading Content of Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub>-Hard Carbon Composite Anode for the Fast Charging Li-Ion Battery
Bibliographic record
Abstract
Fast charge of lithium-ion batteries (LIBs) needs anode and cathode materials operating at high current densities. Li4Ti5O12 (LTO) can enable fast lithium ion (Li+) transport due to its 3D crystal structure. Nonetheless, this material suffers from low specific capacity and high operating voltage. In contrast, Hard Carbon (H.C) with high specific capacity and low practical voltage is one of the promising anode materials for high-energy lithium-ion batteries. However, practical application of this material is compromised with its slow kinetic, and reduced cycling performance associated with irreversibly trapped ions in its structure [1]. In this report, effect of loading ratio of H.C and LTO composite anode on the electrochemical behavior of fast charging lithium ion battery is studied. Electrochemical characterization results show that a certain loading of H.C plays a critical role in improving the electrochemical performance of LTO-H.C composite electrodes. Specifically, superior cycling stability and specific capacity achieved for the composite electrode with 20 wt.% H.C. It is believed that composite electrodes with an optimized ratio can effectively contribute to Li-ion storage and form a robust protective solid electrolyte interface (SEI) on the electrode surface. The latter might minimize further electrolyte decomposition and continuous growth of the SEI layer upon cycling, then improves the cycling stability of the electrode. In addition, the cycling performance and rate capability of LiNiMnCoO2 (NMC) with different nickel (Ni) and manganese (Mn) contents were evaluated and compared. The results clearly suggest that higher Ni content can improve specific capacity (115 mAhg-1for NMC333 vs 149 mAhg-1 for NMC811 at 1C). However, cycling stability deteriorates with increasing the Ni content (77% capacity retention for NMC333 vs 45% for NMC811 after 300 cycles). It is postulated that electrodes with higher Ni content are susceptible to transition metal dissolution and side reactions at electrode-electrolyte interface, which could lead to performance degradation by impeding Li+ diffusion across the electrode, and irreversible consumption of Li+ [2]. Finally, the possibility of use of the optimized anode for fabricating full cell with the selected NMC cathode is investigated. References: [1] Weiss, M., Ruess, R., Kasnatscheew, J. et al. Fast Charging of Lithium‐Ion Batteries: A Review of Materials Aspects. Adv. Energy Mater 11,2101126 (2021). [2] Lin, R., Bak, SM., Shin, Y. et al. Hierarchical nickel valence gradient stabilizes high-nickel content layered cathode materials. Nat Commun 12, 2350 (2021).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".