Impact of Electrolyte on the Cycling of Si-Based Materials
Bibliographic record
Abstract
Alloy-based negative electrodes have been considered the “next-generation negative electrode” expected to replace graphite for many years. However, few exist in the marketplace. Manufacturing alloy-based cells with acceptable cycling poses a major challenge, indeed many components of the cell must be adapted in order to obtain successful cycling. 3M has a long history of alloy research for negative electrodes and has developed commercially viable Si-based alloys. Si-based negative electrode materials undergo massive volume changes during cycling. Pure Si expands by approximately 280% while 3M’s active/inactive Si alloys expand on the order of 135%. The expansion and contraction of the particles has important consequences on the surface stability of these materials. Furthermore, the choice of electrolyte has profound impacts on the cycling of cells containing Si-based materials. In this presentation we will show the impact of electrolyte reactivity on the physical and chemical characteristics of the particles, the composition of the electrolyte and the cycling performance of the cells. Cross section SEM studies of cycled 3M Si alloy and pure Si show the dimensional changes occurring in Si-based materials with cycling due to electrolyte reactivity. After 100 cycles, an SEI is found on the 3M active/inactive alloy, while the pure nano Silicon dramatically increases in size. Figure 1 shows cross section images of the electrodes containing only binder and either 3M Si alloy or pure Si. While electrolyte reactivity can occur with minimal fade in half cells, capacity fade will occur in full cells. Fluoroethylene carbonate (FEC) has long been known to be an important electrolyte for Si-based materials. Furthermore, full cells containing Si-based materials can sometimes undergo sudden failure with cycling. GC/MS studies of electrolytes from cycled full cells containing Si-based materials will be presented showing the relationship between electrolyte composition and sudden failure. These results highlight the importance of full cell design optimization for the implementation of Si-based materials in commercially-relevant full cells. Figure 1
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".