Impact of Graphite Materials on the Lifetime of NMC811/Graphite Pouch Cells: Part I. Material Properties, ARC Safety Tests, Gas Generation, and Room Temperature Cycling
Bibliographic record
Abstract
The impact of graphite materials on capacity retention in Li-ion cells is important to understand since Li inventory loss due to SEI formation, and cross-talk reactions between the positive and negative electrodes, are important cell failure mechanisms in Li-ion cells. Here, we investigate the impact of five graphite materials from reputable suppliers on the performance of NMC811/graphite cells. We show that natural graphites (NG) here have a mixture of 3R and 2H phases, while artificial graphites (AG) were 2H only. We find that there are differences between the N2 BET surface area and the electrochemically-accessible area where redox reactions can take place and it is the latter that is most important when optimizing graphite-containing cells. Part I of this 2-part series investigates physical and electrochemical differences between the graphite materials of interest here, as well as room temperature cycling to probe improvements in capacity retention. We demonstrate that advanced AG materials with small accessible surface areas can improve safety, 1st cycle efficiency (FCE) and long-term cycling compared to NG materials with higher accessible surface areas. Part II of this work examines elevated temperature cycling, cell swelling, and makes lifetime predictions for the best NMC811/graphite cells.
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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 | 0.001 | 0.001 |
| 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".