A Facile and Very Effective Method to Enhance the Mechanical Strength and the Cyclability of Si‐Based Electrodes for Li‐Ion Batteries
Bibliographic record
Abstract
Abstract It is well known that the mechanical properties of lithium‐ion battery electrodes impact their electrochemical performance. This is especially critical for Si‐based negative electrodes, which suffer from large volume changes of the active mass upon cycling. Here, this study presents a postprocessing treatment (called maturation) that improves the mechanical and electrochemical stabilities of silicon‐based anodes made with an acidic aqueous binder. It consists of storing the electrode in a humid atmosphere for a few days before drying and cell assembly. This results in a beneficial in situ reactive modification of the interfaces within the electrode. First, the binder tends to concentrate at the silicon interparticle contacts. As a result, the cohesion of the composite film is strengthened. Second, the corrosion of the copper current collector, inducing the formation of copper carboxylate bonds, improves the adhesion of the composite film. The great improvement of the mechanical stability of the matured electrode is confirmed by in‐operando optical microscopy showing the absence of film delamination. The result is a significant electrochemical performance gain, up to a factor 10, compared to a not‐matured electrode. This maturation procedure can be applied to other types of electrodes for improving their electrochemical performance and also their handling during cell manufacturing.
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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 | 0.001 |
| 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".