Bibliographic record
Abstract
Single-crystal mid nickel NMC positive electrode materials, like LiNi 0.65 Mn 0.30 Co 0.05 O 2 , are gaining interest for a variety of applications, including electric vehicles. Such materials can be produced at lower cost than high Ni materials, like LiNi 0.91 Mn 0.045 Co 0.045 O 2 , and can yield Li-ion cells with competitive energy density provided they can be charged to 4.4 V repeatedly. We show how electrolyte additives and simple surface coatings can enable the operation of mid-nickel NMC cells to 4.4 V for thousands of charge-discharge cycles at 40 o C. Emphasis is placed in the lecture on illustrating the mysteries behind the function of the additives and coatings. Figure 1, taken from reference 1, shows the impact of various electrolyte additives on the lifetime of NMC640 cells tested at C/3 and at 40 o C. Coatings show a comparatively enhanced effect. Figure 1. Number of cycles to 80% original capacity for cells charged to 4.4V plotted versus number of cycles to 80% original capacity for cells charged to 4.3 V. The cathode was single crystal NMC640. The data point labelled “Control” is for cells with no additives. Electrolyte additives are listed by “code” which is available in reference 1. Reference 1 – Saad Azam et al. J. Electrochem. Soc., 2024 171 110510 Figure 1
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".