Achieving Thermodynamic Stability of Single‐Crystal Co‐Free Ni‐Rich Cathode Material for High Voltage Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Ni‐rich layered cathode materials are progressively considered as the standard configuration of high‐energy electric vehicles by virtues of their high capacity and eliminated “range anxiety.” However, the poor cyclic stability and severe cobalt supply crisis would restrain their wide commercial applicability. Here, a cost‐effective single‐crystal Co‐free Ni‐rich cathode material LiNi 0.8 Mn 0.18 Fe 0.02 O 2 (NMF), which outperforms widely commercial polycrystalline LiNi 0.83 Co 0.11 Mn 0.06 O 2 (MNCM) and single‐crystal LiNi 0.83 Co 0.11 Mn 0.06 O 2 (SNCM) is reported. Surprisingly, NMF can compensate for the reversible capacity loss under the designed conditions of high‐temperature and elevated‐voltage, achieving a competitive energy density compared with conventional MNCM or SNCM. Combining operando characterizations and density functional theory calculation, it is revealed that NMF cathode with improved dynamic structure evolution largely alleviates the mechanical strain issue commonly found in Ni‐rich cathode, which can reduce the formation of intragranular cracks and improve the safety performance. Consequently, this new Co‐free NMF cathode can achieve a perfect equilibrium between material cost and electrochemical performance, which not only reduces the production cost by >15%, but also demonstrates excellent thermal stability and cycling performance..
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 | 0.000 | 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".