(Invited) Towards High Cycle Efficiency of Si Based Negative Electrodes for Next Generation Lithium Ion Batteries
Bibliographic record
Abstract
Si is an attractive negative electrode material for lithium ion batteries due to its high specific capacity (~3600 mAh/g). However, the huge volume swelling and shrinking during cycling leads to several coupled mechanical and chemical degradation at the material/electrode/cell level, including fracture of Si particles, unstable solid electrolyte interphase (SEI), and low Coulombic efficiency. In this talk, we will discuss how to improve the cycle efficiency from those aspects, starting with the fundamental understanding of the relationship between the structure and properties of SEI to pinpoint the SEI failure mechanisms using advanced in-situ electrochemical tools. Then we will discuss how to develop artificial SEI, combining the design of Si nanostructure and electrode architecture to enable the high cycle efficiency and the extended life. At last, we will discuss how the nanostructure design at material level can impact the electrochemical-mechanical behaviors at electrode and cell level and the trade-off between gravimetric energy density and volumetric energy density for EV applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".