Size and Surface Effects of Silicon Nanocrystals in Graphene Aerogel Composite Anodes for Lithium Ion Batteries
Bibliographic record
Abstract
Silicon is recognized as a promising anode material for high-performance lithium ion batteries due to its high theoretical specific capacity and elemental abundance. Challenges related to the low electrical conductivity of Si and large volume changes during the lithiation/delithiation cycles, as well as the low rate of lithium diffusion in silicon anodes, hinder practical applications. To provide fundamental insights into these issues, silicon nanocrystal/graphene aerogel nanocomposites were synthesized by combining undecanoic acid-functionalized silicon nanocrystals of various sizes (Si X -COOH, where X represents the nanocrystal diameter of 3, 5, 8, and 15 nm) with conductive mesoporous graphene aerogels (GAs). The silicon nanocrystals are evenly dispersed throughout the graphene aerogel as shown by energy-dispersive X-ray (EDX) mapping. In terms of electrochemical performance, Si X -COOH/GA nanocomposites demonstrated a clear dependence on the size of the embedded silicon nanocrystals, with the composites comprising the larger silicon nanocrystals showing a higher initial capacity but accompanied by rapid decay of capacity retention over 100 cycles. To study the effect of thermal processing on the electrochemical performance, Si X -COOH/GA nanocomposites were annealed at 600 °C to yield annealed Si X /GA nanocomposites. The annealed nanocomposite composed of the smallest silicon nanocrystals, Si3/GA, exhibits a stable specific capacity of ∼1100 mAh/g and capacity retention of over 90% after 500 cycles when tested at a current density of 400 mA/g.
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 | 0.000 | 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".