Improved graphene-based sodium-ion battery anodes using low surface area, low temperature reduced graphene oxide powders
Bibliographic record
Abstract
Reduced graphene oxide (rGO) is a promising high-capacity anode material for sodium-ion batteries (SIBs). However, the high electrochemically accessible surface area of typical rGO electrodes causes excessive solid electrolyte interface formation yielding large irreversible capacities during initial cycles. To overcome this limitation, we report an approach to generate low surface area rGO powders through a combination of spray drying and slow thermal reduction to avoid exfoliation. The performance of these electrodes reduced at various temperatures (200 °C -1000 °C) is compared to similarly treated rGO but exfoliated to create a more typical high surface area rGO electrode to decouple the effects of surface area and oxygen content on electrochemical performance. Low surface area powders, reduced at lower temperatures than studied before (400 °C) are found to exhibit the best performance in terms of desodiation capacity (216 mAh g-1 at 100 mA g-1), and stability (85% retention over 200 cycles) while reducing the irreversible capacity loss by two to threefold compared to previous literature. While further improvements are necessary to make this material system practical, these results highlight the need for improved granularization strategies that further reduce surface area, increase restacking order and target the optimal level of oxygen functionalization.
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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 | 0.001 | 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 | 0.000 |
| 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".