Investigation of The Failure Mechanisms of Li-Ion Pouch Cells with Si/Graphite Composite Negative Electrodes and Single Wall Carbon Nanotube Conducting Additive
Bibliographic record
Abstract
Silicon-Graphite composite electrodes are a rapidly developing area of research and commercialization. Increasing the energy density of current Li-ion battery technology can be done by simply creating silicon-graphite composite electrodes. It is well known that the failure of these silicon-graphite composite electrodes stems from the expansion of the silicon during cycling that causes mechanical degradation, excessive SEI formation, and electrode shift loss. Here we explore the use and capacity loss mechanisms of a silicon-graphite composite anode employing CMC/SBR binder used in conjunction with single wall carbon nanotubes. These nanotubes are thought to be effective in increasing mechanical resiliency of the electrodes and increase the electrical connectivity between particles within the formed electrode. When the Si/graphite electrode cycles, it is believed that the SWCNTs help keep the active particles electrically connected and, hence, electrochemically active. Through dV/dQ analysis and in situ pressure monitoring, the pouch cells studied here are shown to exhibit minimal loss of active mass in the positive and negative electrodes but experience capacity loss due to continued negative electrode SEI growth leading to lithium inventory or shift loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".