Combinatorial Study of Systematic Aluminum Substitution into NMC Cathode Materials
Bibliographic record
Abstract
Li-ion batteries power our modern mobile world. They are the cornerstone of the electric vehicle, a market whose growth is accelerating rapidly; and the mobile phone, which encompasses billions of devices used by most people on the planet. The cathode material of Li-ion batteries is the limiting factor of battery capacity, as well as one of the culprits of battery failure and thus improving this material is critical to realize better performing batteries. The layered metal oxide LiNixMnyCozO2 is the current market-leader in the cathode space. Many compositions are in use with the industry pushing capacity higher with high-Ni compositions. However, high-Ni compositions come at the cost of material stability, i.e., battery lifetime. To stabilize the cathode, many metals have been substituted into the material to resist structural transformations and to prevent surface reactions with the electrolyte. Of the substituted metals, aluminum has been explored extensively. However, most studies are limited to a few key compositions of NMC and a systematic study of the structural and electrochemical impact of Al substitution has not been completed on the full range of NMC compositions. In this work, the effects of different levels of aluminum substitution are investigated and, in total, 320 different compositions are explored using high-throughput techniques. X-ray diffraction is used to understand the structures at all compositions and combinatorial electrochemistry is performed to extract important battery metrics related to energy density, cycling performance, and irreversible capacity. This work provides insight into how accommodating different NMC compositions are to aluminum substitution and the effects of the substitutions on their electrochemistry.
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".