In-Situ Characterization of Si-Based Electrodes By Dilatometry and Acoustic Emission
Bibliographic record
Abstract
The replacement of carbon by silicon as active material in negative electrodes of Li-ion batteries is very attractive since the gravimetric and volumetric capacities of silicon are much higher than those of carbon. However, silicon suffers from huge volume variation (up to ~300% versus 10% for C) upon cycling. This leads to the electrode pulverization inducing electrical disconnections in addition to cause an instability of the solid electrolyte interface (SEI), resulting in poor cycle life and coulombic efficiency. A precise evaluation of the volume variation and cracking processes is thus crucial to develop more efficient Si-based electrodes. For that purpose, in the present study, the volume change of Si-based anodes during cycling is monitored by in situ dilatometry. In addition, in situ acoustic emission (AE) measurements are performed to study the electrode pulverization process with cycling. For a better identification of the origin of the different populations of detected AE signals, a detailed study of their energetic and temporal characteristics (amplitude, rise time, frequency...) is conducted. The influence of the cycling conditions (discharge capacity, cycle number, C-rate), electrode formulation (Si particle size…) and processing (calendar pressure) and electrolyte composition on the electrode volume variation and cracking is highlighted and correlated to its electrochemical performance.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".