Bibliographic record
Abstract
Development of low cost, high energy, safe and long-life rechargeable battery technology is critical for widespread commercialization of smart grid and electric vehicle. Rechargeable lithium-ion batteries have been considered as most promising candidates as energy storage system for transportation, smart grids and stationary power. In this presentation, I will present our recent work on advanced Silicon(Si) and Tin(Sn) anode materials development for next generation rechargeable lithium-ion batteries: (1) The latest achievements and some ongoing work in silicon anode based high energy Li-ion battery through the collaboration with General Motors. More specifically, advanced Si electrodes have been developed by a simple flash heat treatment and sulfur-doped graphene wrapping technique which can efficiently accommodate Si volume expansion and demonstrate excellent electrochemical reversibility and cycling. (2) A novel and facile method was developed to synthesize a rod-on-sheet-like nanohybrid (denoted as SnS-SG), consisting of one-dimentional (1D) single-crystalline, orthorhombic tin sulfide (SnS) supported on two-dimentional (2D) sulfur-doped graphene. The SnS-SG nanohybrid exhibited a superior cycle stability over 1500 cycles with a high capacity retention of 85%, the longest demonstrated cyclability among numerous Sn-based anode materials reported so far in LIBs.
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".