Micron-sized Secondary Silicon Spherical Composite for Practical Li-ion Batteries
Bibliographic record
Abstract
The ever-growing demand for smaller batteries with higher energy density and longer life cycle has triggered research interest on metal alloy materials as an anode for Li-ion batteries (LIBs) in broad applications such as electric vehicle and portable electronics, because metal alloys usually possess much higher theoretical gravimetric and volumetric capacities than those of graphite. Si has been considered as potential candidate due to its highest capacity (4200 mAh/g, according to a state of Li4.4Si), low price and high abundancy. However, progressively large volume change during the charge and discharge reactions hinders Si from being commercially used. We have successfully developed a micron-szied secondary Si-based composite (MSC), with Si nanoparticles (diameter ~70 nm, commercial) embedded in a porous conductive network constructed with conductive carbon and binder materials. Si NPs were purchased in bulk and used as received. The MSC anode exhibited an initial CE of 81%, and >99.8% thereafter. The volume expansion of Si has been effectively limited inside the secondary particle, avoiding damage to the structure of electrode. As a result, the MSC electrode delivered excellent cycling stability with 8% loss over 500 cycles at a rate of 0.5C. Considering the advantages of micron-sized composite, this material could be of interest in the application of real-life LIBs.
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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".