(Invited) Controlling Void Space in Crumpled Graphene for High Stability Silicon Anodes
Bibliographic record
Abstract
Silicon anodes are thought to soon replace graphite in next-generation Li-ion batteries due to silicon’s high capacity (3590 mAh/g for the Li15Si4 alloy at room temperature), availability and natural abundance. However, lithiation of silicon causes a large volume expansion (~300%) which can cause pulverization of the primary silicon particles, cracking and delamination of the bulk electrode and the formation of an unstable solid-electrolyte interface which must rebuild upon each cycle. Some combination of these effects leads to rapid anode failure via electrical isolation of active material and/or electrolyte depletion. To mitigate these challenges, clusters of silicon nanoparticles can be wrapped with flexible 2D-materials like graphene which can potentially act as a dimensionally and electrochemically stable, permeable barrier layers. This can be achieved in a scalable way via spray drying of aqueous dispersions of graphene oxide and silicon. In this talk, I will describe recent work from our group which aims to introduce a controlled amount of void space within the graphene protected silicon structures with the aim of engineering zero-strain silicon/carbon anode particles. In the absence of void space control, capillary forces acting during the spray drying process tightly wrap graphene around silicon clusters leaving little room for volume expansion. In one case, void space is introduced via incorporation of sacrificial polystyrene nanoparticles within the core by co-spray drying silicon, polystyrene and graphene oxide. In a second case, we do not use a sacrificial material but, instead, incorporate a responsive, cross-linked hydrogel within the core which can be expanded upon hydration to increase the volume of the graphene shell. When dehydrated, the gel shrinks to generate the required void space and acts as a Li-ion conducting, elastic binder within the core. In both cases, I will describe the systematic evolution of the crumpled graphene microstructure and its impact on anode performance in both half-cells and full Li-ion batteries.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".