Optimization of Si-containing and SiO based Anodes with Single-Walled Carbon Nanotubes for High Energy Density Applications
Bibliographic record
Abstract
Lithium-ion batteries require a high energy density when being used in applications such as electric vehicles or portable electronics. This can be achieved on a large scale by improving packaging and implementation, or on a material scale by selecting more energy dense electrode active material. Silicon can be used as a replacement for graphite in negative electrodes if the detrimental volume expansions can be contained. These volume expansions cause continuous mechanical degradation capacity loss leading to short lifetimes that do not meet industry standards. These high-capacity high volume expansion materials such as silicon and SiO must be used in conjunction with more stable electrode materials like graphite to reduce the mechanical degradation caused by volume change. Single-walled carbon nanotubes are shown to be a simple yet effective drop in addition to improve electrical connectivity and increase capacity retention in these silicon-based composite negative electrodes. This added particle interconnectivity from the high tensile strength carbon nanotubes allows for the use of simple binders such as CMC/SBR to create composite electrodes with competitive performance without the use of expensive polymers or complex nanostructures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".