Characterisation of Si-Based Anodes for Li-Ion Batteries By Operando Dilatometry and Acoustic Emission Measurements
Bibliographic record
Abstract
The replacement of graphite by silicon as the active material in negative electrodes of Li-ion batteries is very attractive since the specific capacity of Si is 10 times higher than that of graphite. However, Si suffers from huge volume variation (up to ~300% vs 10% for C) during its lithiation. This leads to the electrode cracking which induces electrical disconnections in addition to cause an instability of the solid electrolyte interface (SEI), resulting in poor cycle life and low coulombic efficiency. A precise evaluation of the electrode volume variation and cracking upon cycling is thus crucial to develop more efficient Si-based anodes. To date, the study of their morphological changes is usually limited to post mortemexaminations by microscopy. This does not allow a detailed analysis of the morphological degradation process, which can significantly vary depending on the electrode composition and processing, and the charge/discharge conditions. In the present study, the volume change with cycling of Si-based anodes is monitored by operando dilatometry experiments. For that purpose, a specific displacement transducer is used, which allows measuring expansion or shrinkage of the electrode during cycling down to the sub-micrometer range. Operando acoustic emission (AE) measurements are also performed to study the electrode cracking [1]. The AE technique is based on the detection and analysis of transient elastics waves generated by stress events. The influence of the cycling conditions and electrode formulation on the volume variation and cracking of Si-based electrodes is highlighted, and correlated to their electrochemical performance. [1] A. Tranchot, A. Etiemble, P-X. Thivel, H. Idrissi, L. Roué In-situ acoustic emission study of Si-based electrodes for Li-ion batteries, J. Power Sources 279 (2015) 259-266.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".