Is Aluminium Useful in NiMn Cathode Systems?: A Study of the Effectiveness of Al in Co-Free, Ni-Rich Positive Electrode Materials for Li-Ion Batteries
Bibliographic record
Abstract
Aluminium has become a dopant of interest in many positive electrode materials, particularly the widely used LiNi1−x−yMnxCoyO2 (NMC). Despite the shift of the positive electrode active material space towards Co-free alternatives, the benefits of Al-doping in Co-free LiNixMn1−xO2 (NM) systems have yet to be extensively studied. In this work a series of polycrystalline NM and NMA pairs are compared head-to-head to better understand the effect of Al in Ni-rich, Co-free systems in terms of electrochemical, mechanical, surficial, and thermal stability. The materials tested vary in Ni-content, Al-doping amount as well as secondary particle size, as these parameters influence the effect of Al-presence on certain aspects of material performance. Although Al can bring certain advantages to NM materials, Al-substitution does not universally lead to improved performance in these systems.
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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 | 0.000 |
| 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".