Silicon Inverse‐Opal‐Based Macroporous Materials as Negative Electrodes for Lithium Ion Batteries
Bibliographic record
Abstract
Abstract Several types of silicon‐based inverse‐opal films are synthesized, characterized by a range of experimental techniques, and studied in terms of electrochemical performance. Amorphous silicon inverse opals are fabricated via chemical vapor deposition. Galvanostatic cycling demonstrates that these materials possess high capacities and reasonable capacity retentions. Amorphous silicon inverse opals perform unsatisfactorily at high rates due to the low conductivity of silicon. The conductivity of silicon inverse opals can be improved by their crystallization. Nanocrystalline silicon inverse opals demonstrate much better rate capabilities but the capacities fade to zero after several cycles. Silicon–carbon composite inverse‐opal materials are synthesized by depositing a thin layer of carbon via pyrolysis of a sucrose‐based precursor onto the silicon inverse opals. The amount of carbon deposited proves to be insufficient to stabilize the structures and silicon–carbon composites demonstrate unsatisfactory electrochemical behavior. Carbon inverse opals are coated with amorphous silicon producing another type of macroporous composite. These electrodes demonstrate significant improvement both in capacity retentions and in rate capabilities. The inner carbon matrix not only increases the material conductivity but also results in lower silicon pulverization during cycling.
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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".