Sn nanoparticles embedded into porous hydrogel‐derived pyrolytic carbon as composite anode materials for lithium‐ion batteries
Bibliographic record
Abstract
Abstract The composite powders, Sn nanoparticles embedded into the porous hydrogel‐derived carbon (Sn@PHDC), were successfully prepared by polymerization and calcination processes, and the characterization results confirmed that Sn nanoparticles were homogeneously dispersed in the porous hydrogel‐derived pyrolytic carbon. The coin cell assembled with the Sn@PHDC‐50 composite electrode presented good cyclic stability and rate performance when the weight ratio of Sn nanoparticles to hydrogel‐derived pyrolytic carbon was maintained at 1:1. Moreover, the Sn@PHDC‐50 electrode manifested a lower charge transfer resistance of 58.57 Ω and a higher lithium ions diffusion coefficient of 1.117 × 10 –14 cm 2 ·s −1 than pure Sn and other Sn@PHDC electrodes. Those improvements can be partly ascribed to the fact that the hydrogel‐derived pyrolytic carbon matrix can release the volume strain and enhance the electronic conductivity of the composite electrode, and partly to the fact that the porous hydrogel‐derived pyrolytic carbon matrix can suppress agglomerations of Sn nanoparticles and shorten Li + diffusion paths. This work may provide a new approach for the improvement of Sn‐based anode materials for lithium‐ion 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) | 0.000 | 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".