Reduced Graphene Oxide/Tin–Antimony Nanocomposites as Anode Materials for Advanced Sodium-Ion Batteries
Bibliographic record
Abstract
Reduced graphene oxides loaded with tin-antimony alloy (RGO-SnSb) nanocomposites were synthesized through a hydrothermal reaction and the subsequent thermal reduction treatments. Transmission electron microscope images confirm that SnSb nanoparticles with an average size of about 20-30 nm are uniformly dispersed on the RGO surfaces. When they were used as anodes for rechargeable sodium (Na)-ion batteries, these as-synthesized RGO-SnSb nanocomposite anodes delivered a high initial reversible capacity of 407 mAh g(-1), stable cyclic retention for more than 80 cycles and excellent cycle stability at ultra high charge/discharge rates up to 30C. The significantly improved performance of the synthesized RGO-SnSb nanocomposites as Na-ion battery anodes can be attributed to the synergetic effects of RGO-based flexible framework and the nanoscale dimension of the SnSb alloy particles (<30 nm). Nanosized intermetallic SnSb compounds can exhibit improved structural stability and conductivity during charge and discharge reactions compared to the corresponding individuals (Sn and Sb particles). In the meantime, RGO sheets can tightly anchor SnSb alloy particles on the surfaces, which can not only effectively suppress the agglomeration of SnSb particles but also maintain excellent electronic conduction. Furthermore, the mechanical flexibility of the RGO phase can accommodate the volume expansion and contraction of SnSb particles during the prolonged cycling, therefore, improve the electrode integrity mechanically and electronically. All of these contribute to the electrochemical performance improvements of the RGO-SnSb nanocomposite-based electrodes in rechargeable Na-ion batteries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".