In-Situ Computed Tomography of Particle Microcracking and Electrode Damage in Cycled NMC622/Graphite Pouch Cell Batteries
Bibliographic record
Abstract
Mechanical degradation of electrode materials is an important failure mode in lithium-ion batteries. High-energy-density cathode materials like nickel-rich NMC (LiNi x Mn y Co z O 2 ) undergo significant anisotropic volume expansion during cycling that applies mechanical stress to the material. Computed tomography (CT) of cells can be used to image cell-level and electrode-level changes that result from long-term cycling, without the need for cell disassembly or destructive sampling. Previous work by our group has used synchrotron CT to show cathode thickness growth and depletion of liquid electrolyte after long-term (>2 years) cycling of polycrystalline NMC622/graphite cells. These phenomena were attributed to cathode microcracking, but direct evidence of this was not available at the time. In this study, we present in-situ, sub-micron CT of these unmodified pouch cells, providing new insights into the morphological changes occurring at the particle level. These results confirm that extensive microcracking and dramatic morphological changes are occurring in the cathode that were not previously observed. Combined with the cell-level and electrode-level scans presented previously, this study provides a complete, multi-scale picture of cathode microcracking and how its effects propagate throughout the cell.
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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 | 0.000 | 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".