Effects of Surface Coating on Gas Evolution and Impedance Growth at Li[Ni<sub>x</sub>Mn<sub>y</sub>Co<sub>1-x-y</sub>]O<sub>2</sub>Positive Electrodes in Li-Ion Cells
Bibliographic record
Abstract
The effects of surface coatings on Li[Ni x Mn y Co z ]O 2 (NMC, x+y+z = 1, x:y:z = 4:4:2 (NMC442), x:y:z = 5:3:2 (NMC532), x:y:z = 6:2:2 (NMC622)) electrodes in pouch cells and pouch bags, containing electrolyte with a certain additive blend, were systematically studied. Ex-situ gas measurements, gas chromatography coupled with a thermal conductivity detector and electrochemical impedance spectra were used to study the reactions that occurred. The results obtained from pouch bag experiments at elevated temperature indicate that the LaPO 4 surface coating did not impede impedance growth at the NMC442 surface and did not reduce gas generation at 60°C while the Al 2 O 3 surface coating effectively prevented impedance growth at the NMC622 surface at 60°C. The coating procedures and the underlying NMC materials were different so it is difficult to conclude that the Al 2 O 3 coating is more effective than the LaPO 4 coating in any situation. Hydrogen was only detected in pouch cells rather than pouch bags suggesting a crosstalk between the lithiated graphite and delithiated NMC electrodes as has been proposed before by the Gasteiger group. That is, species created at the positive electrode migrate to the negative, and react there to produce hydrogen.
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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 | 0.000 |
| 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".