Promising Dual-Doped Graphene Aerogel/SnS<sub>2</sub> Nanocrystal Building High Performance Sodium Ion Batteries
Bibliographic record
Abstract
We report the effort in designing layered SnS 2 nanocrystals decorated on nitrogen and sulfur dual-doped graphene aerogels (SnS 2 @N,S-GA) as anode material of SIBs. The optimized mass loading of SnS 2 along with the addition of nitrogen and sulfur on the surface of GAs results in enhanced electrochemical performance of SnS 2 @N,S-GA composite. In particular, the introduction of nitrogen and sulfur heteroatoms could provide more active sites and good accessibility for Na ions. Moreover, the incorporation of the stable SnS 2 crystal structure within the anode results in the superior discharge capacity of 527 mAh g –1 under a current density of 20 mA g –1 upon 50 cycles. It maintains 340 mAh g –1 even the current density is increased to 800 mA g –1 . Aiming to further systematically study mechanism of composite with improved SIB performance, we construct the corresponding models based on experimental data and conduct first-principles calculations. The calculated results indicate the sulfur atoms doped in GAs show a strong bridging effect with the SnS 2 nanocrystals, contributing to build robust architecture for electrode. Simultaneously, heteroatom dual doping of GAs shows the imperative function for improved electrical conductivity. Herein, first-principles calculations present a theoretical explanation for outstanding cycling properties of SnS 2 @N,S-GA composite.
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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".