Impact of Cobalt Addition on Single-Crystal Li<sub>1+x</sub>(Ni<sub>0.6</sub>Mn<sub>0.4</sub>)<sub>1−x</sub>O<sub>2</sub> Cathode Material Performance
Bibliographic record
Abstract
Nickel and manganese-based layered oxides with a nickel content ranging from 50% to 80% are promising cathode materials for high-energy density lithium-ion batteries. However, these materials face challenges such as poor rate capability and limited cycling stability. The addition of excess lithium can mitigate these issues to some extent. This study examines the impact of incorporating small amounts of cobalt (5% or 10%) into these materials through an “all-dry” synthesis approach in stoichiometric and excess lithium-containing compositions. Results indicate that adding even these small amounts of cobalt decreases the cation mixing, improves crystallinity, reduces electronic resistance, and influences the morphology depending on whether nickel or manganese is replaced. The materials can accommodate up to 15% excess lithium without significant surface impurities. The addition of cobalt further enhances the rate capability of the material in excess lithium materials, but increasing cobalt content tends to compromise cycling stability when the materials are cycled up to 4.4 V. Materials in which 5% cobalt replaces nickel still exhibit superior rate capability and cycling performance compared to materials without cobalt. Therefore, incorporating small amounts of cobalt can positively impact the performance of Li1+x(Ni0.6Mn0.4)1−xO2 materials, offering a balance between improved rate capability and cycling stability.
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 | 0.001 | 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".