Rational Design and Mechanical Understanding of Three-Dimensional Macro-/Mesoporous Silicon Lithium-Ion Battery Anodes with a Tunable Pore Size and Wall Thickness
Bibliographic record
Abstract
Silicon is regarded as one of the most promising next generation lithium-ion battery anodes due to its exceptional theoretical capacity, appropriate voltage profile, and vast abundance. Nevertheless, huge volume expansion and drastic stress generated upon lithiation cause poor cyclic stability. It has been one of the central issues to improve cyclic performance of silicon-based lithium-ion battery anodes. Constructing hierarchical macro-/mesoporous silicon with a tunable pore size and wall thickness is developed to tackle this issue. Rational structure design, controllable synthesis, and theoretical mechanical simulation are combined together to reveal fundamental mechanisms responsible for an improved cyclic performance. A self-templating strategy is applied using Stöber silica particles as a templating agent and precursor coupled with a magnesiothermic reduction process. Systematic variation of the magnesiothermic reduction time allows good control over the structures of the porous silicon. Finite element mechanical simulations on the porous silicon show that an increased pore size and a reduced wall thickness generate less mechanical stress in average along with an extended lithiation state. Besides the mechanical stress, the evolution of strain and displacement of the porous silicon is also elaborated with the finite element simulation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".