An Analysis of Artificial and Natural Graphite in Lithium Ion Pouch Cells Using Ultra-High Precision Coulometry, Isothermal Microcalorimetry, Gas Evolution, Long Term Cycling and Pressure Measurements
Bibliographic record
Abstract
Natural graphite (NG) negative electrode materials can perform poorly compared to synthetic, or artificial, graphite (AG) negative electrodes in certain lithium ion cells. LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532)/(AG or NG) pouch cells were tested with various loadings of an electrolyte additive blend to study the effect of the graphite type as well as the formed solid electrolyte interphase (SEI). Cells underwent testing using ultra-high precision coulometry, isothermal microcalorimetry, in-situ pressure measurements, long term cycling and in-situ gas measurements. In short term experiments NMC532/AG and NMC532/NG cells showed similar coulombic efficiencies, parasitic heat flows, and gas production with large electrolyte additive loadings, but NG cells showed worse capacity retention in long-term tests. With low additive loadings NMC532/NG cells showed lower coulombic efficiency, higher capacity fade, more parasitic heat flow, and more gas production. In-situ cell stack pressure measurements showed that NMC532/NG cells irreversibly expanded during cycling while NMC532/AG cells did not. Although these results lead one to propose a simple model for the poor performance of NMC532/NG cells, NMC622/NG and NMC622/AG cells showed very different behavior in long term tests suggesting that positive/negative interactions play a strong role in governing the behavior of graphites in Li-ion 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".