Mesoporous Silicon with Capacity‐Limited Lithiation: A Strategy for Stable High‐Capacity Lithium‐Ion Anodes
Bibliographic record
Abstract
While silicon anodes offer a high theoretical capacity for lithium‐ion batteries, they face challenges related to severe volume expansion and mechanical degradation. Herein, mesoporous structuring is systematically coupled with capacity‐limited (partial) lithiation to improve the durability of high‐Si composite anodes (70% Si). Using galvanostatic cycling and ex situ scanning electron microscopy/thickness measurements on bulk‐Si and mesoporous‐Si (PSi) electrodes at four lithiation depths (100%, 66%, 50%, 33%), it is shown that PSi consistently delivers higher capacity retention over 100 cycles and exhibits markedly lower swelling. At 33% lithiation, PSi retains ≈94% of its initial capacity, outperforming bulk Si, and expands ≈141% compared to 315% for bulk Si under the same cycling conditions. These benefits result from the porous framework's ability to accommodate the volume changes and stabilize the solid electrolyte interface (SEI), reducing the risk of fracture and electrical connectivity loss. These findings contribute to the ongoing research on mesoporous silicon materials and support a higher Si fraction in graphite‐containing anodes, delivering greater energy density with maintained SEI stability and low swelling.
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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) | 0.001 | 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".