High Performance and Flexible Si Anodes Enabled By a Facile Electrode Design
Bibliographic record
Abstract
As the traditional LIB electrode materials approach their theoretical limits, new electrodes that can provide much higher energy densities have attracted significant research interest. Silicon, which undergoes an alloying mechanism with lithium, possesses a theoretical capacity of around 3500 mAh g-1, and has aroused tremendous attention. However, Si anodes usually suffer from several intrinsic problems, including self-pulverization, delamination, unstable SEI, etc. It is indispensable to address these challenges before Si anodes can be employed in large-scale “real-world” applications. In this work, high performance and flexible C-SiNW-FB anodes were designed and prepared via a facile method. 1-D Si NWs were in-situ grown and coated with a thin-layer of carbon on a flexible carbon cloth (CC) substrate, and a functional polymer binder was subsequently applied to intertwine with SiNWs, forming a stable C-SiNW-FB matrix on CC. This advanced electrode design presents the electrode with good flexibility, high capacity, as well as stable cycling performance. Furthermore, the amount of active material on the electrode can be easily manipulated during the synthesis procedure, and electrodes with high loading mass of active materials have been obtained. The C-SiNW-FB electrodes deliver a specific capacity of 3000mA h g-1, corresponding to areal capacity of 5.87 mAh cm-2. Cycling program with a current density of 1A g-1 was performed with the material, and capacity retention of 98% was achieved after 130 cycles. Extremely high loading of 10 mg cm-2 of the active Si was achieved, delivering a much higher areal capacity than commercial graphite anodes. Good flexibility, high specific and areal capacity, together with the stable cycling, all bode great potential for C-SiNW-FB as a promising candidate to fulfill the high energy density needs of next generation batteries.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".