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. However, in rGO-based electrodes, the typically high surface area of the rGO results from exfoliation during reduction. This, in turn, leads to 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 excessive exfoliation. The performance of low surface area rGO electrodes, reduced at various temperatures (200–1000 °C) is compared to high surface area rGO electrodes. This comparison is used to decouple the effects of surface area and oxygen content on electrochemical performance. Low surface area powders, reduced at lower temperatures (400 °C) exhibited the best performance, with a desodiation capacity of 216 mAh g −1 at 100 mA g −1 , and a capacity retention of 85 % (after 200 cycles). Moreover, the irreversible capacity loss was reduced by two- to three-fold compared to previous literature. While further improvements are necessary to make this system practical, these results highlight the need for improved granularization strategies that further reduce surface area, increase restacking order, and yield the optimal level of oxygen functionalization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".