Laser Scanning Method for High-Resolution Thickness Mapping of Lithium-Ion Pouch Cells
Bibliographic record
Abstract
A precise understanding of the physical properties of lithium-ion cells including the cell thickness distribution during cycling and its connection with lifetime is important for cell improvement. A laser scanning instrument has been developed to perform contact-free thickness measurements in operando for small 250 mAh lithium-ion pouch cells and a large 60 Ah automotive format pouch cell. LiNi0.83Mn0.07Co0.10O2/silicon-graphite cells with 20 and 10 wt% micron-sized silicon particles, a LiNi0.8Mn0.1Co0.1O2/natural graphite cell, and a medium-Ni/graphite automotive cell were either cycled in the laser scanning setup with continuous operando thickness mapping or aged at elevated temperature on separate battery cyclers with intermittent operando thickness mapping using the new laser scanning instrument. During the operando cycles, the cell thickness was measured periodically every 1 h and a graphical quantification method was developed to determine reversible and irreversible swelling of the silicon-containing and silicon-free cells. Using the high-resolution laser scanning technique, irreversible cell swelling could be correlated with capacity loss, especially in cells with high silicon content. Graphite-based cells with mature interface like the large automotive pouch cell showed a fully reversible swelling profile indicative of a long-lived 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".