Controlled SnO<sub>2</sub> Crystallinity Effectively Dominating Sodium Storage Performance
Bibliographic record
Abstract
The exploration of sodium ion batteries (SIBs) is a profound challenge due to the rich sodium abundance and limited supply of lithium on earth. Here, amorphous SnO2/graphene aerogel (a‐SnO2/GA) nanocomposites have been successfully synthesized via a hydrothermal method for use as anode materials in SIBs. The designed annealing process produces crystalline SnO2/graphene aerogel (c‐SnO2/GA) nanocomposites. For the first time, the significant effects of SnO2 crystallinity on sodium storage performance are studied in detail. Notably, a‐SnO2/GA is more effective than c‐SnO2/GA in overcoming electrode degradation from large volume changes associated with charge–discharge processes. Surprisingly, the amorphous SnO2 delivers a high specific capacity of 380.2 mAh g−1 after 100 cycles at a current density of 50 mA g−1, which is almost three times as much as for crystalline SnO2 (138.6 mAh g−1). The impressive electrochemical performance of amorphous SnO2 can be attributed to the intrinsic isotropic nature, the enhanced Na+ diffusion coefficient, and the strong interaction between amorphous SnO2 and GA. In addition, amorphous SnO2 particles with the smaller size better function to relieve the volume expansion/shrinkage. This study provides a significant research direction aiming to increase the electrochemical performance of the anode materials used in SIBs.
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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".