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 for Li ion batteries is very crucial for the success of EVs, grid-scale energy storage and next generation of power electronics. A holistic approach is required where the performance of the electrodes and electrolyte needs to be improved simultaneously. On the anode side, Si has been considered as the best candidate to give much higher capacities reaching, in theory, 10 times those of graphite. [1,2] However, in practice Si alone cannot deliver this high capacity reversibly due to many problems associated with volume expansion/shrinking during lithiation/de-lithiation. Also, current cathode materials have limited specific capacity to 150-200 mAh/g and can only be coated up to certain thickness before fracture or polarization takes place. A composite of silicon and graphite with capacities between 500 and 1000 mAh/g has been considered as a transient and practical alternative. In this work we have conducted theoretical and experimental study of silicon, graphite, and binder composite electrodes. We have calculated the percentage of improvement in capacity of composites and in energy density of full cells. We have tested more than 50 compositions in half cells with different ratios, binder type and silicon (shape, size and surface chemistry). Also, we have tested full cells using NMC cathode, a carbonate solvent mixture and various additives. We will show the result of our theoretical and experimental findings and demonstrate the actual parameters that need to be optimized in order to reach real improvements in practical energy density of Li-ion full cells. [1] Chae-Ho Yim, Fabrice M Courtel, Yaser Abu-Lebdeh,Journal of Materials Chemistry, 1(28), 2013 8234. [2] Nuha Salem, Matt Lavrisa and Yaser Abu-Lebdeh, 2015, Energy Technology, DOI 10.1002/ente.201500250.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".