Impact of Graphite Materials on the Lifetime of NMC811/Graphite Pouch Cells: Part II. Long-Term Cycling, Stack Pressure Growth, Isothermal Microcalorimetry, and Lifetime Projection
Bibliographic record
Abstract
Part II of this 2-part series examines the impact of competitive graphite materials on NMC811 pouch cell performance using Ultra-High Precision Coulometry (UHPC), isothermal microcalorimetry, and in-situ stack growth. A simple lifetime projection of the best NMC811/graphite cells as a function of operating temperature is made. We show that graphite choice greatly impacts fractional fade, while fractional charge endpoint capacity slippage was largely unchanged due to identical cathodes. We show that an increase in graphite 1st cycle efficiency due to limited redox-active sites—favourable for minimizing Li inventory loss—is concomitant with an increase in negative electrode charge transfer resistance. Further, we demonstrate that cells with competitive artificial graphites (AG) have a lower parasitic heat flow (∼0.060 mW A −1 h −1 at 40 o C) compared to cells with natural graphites (NG), and that the cells with the AGs had minimal stack thickness change with cycling. Finally, we model SEI growth for NMC811 cells limited to 4.06 V with the square-root time model, and project that the best NMC811/graphite cells here can have decades-long lifetimes at 20 –30 °C when Li plating is avoided. Such cells are excellent candidates for grid storage applications where energy density is less important compared to long lifetime.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".