Towards Improving the Practical Energy Density of Li-Ion Batteries: Optimization and Evaluation of Silicon:Graphite Composites in Full Cells
Bibliographic record
Abstract
Increasing the energy density of Li-ion batteries is very crucial for the success of electric vehicles, grid-scale energy storage, and next-generation consumer electronics. One popular approach is to incrementally increase the capacity of the graphite anode by integrating silicon into composites with capacities between 500 and 1000 mAh/g as a transient and practical alternative to the more-challenging, silicon-only anodes. In this work, we have calculated the percentage of improvement in the capacity of silicon:graphite composites and their impact on energy density of Li-ion full cell. We have used the Design of Experiment method to optimize composites using data from half cells, and it is found that 16% improvements in practical energy density of Li-ion full cells can be achieved using 15 to 25 wt% of silicon. However, full-cell assembly and testing of these composites using LiNi 0.5 Mn 0.5 Co 0.5 O 2 cathode have proven to be challenging and composites with no more than 10 wt% silicon were tested giving 63% capacity retention of 95 mAh/g at only 50 cycles. The work demonstrates that introducing even the smallest amount of silicon into graphite anodes is still a challenge and to overcome that improvements to the different components of the Li-ion battery are required.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".