Expanded graphite/reduced graphene oxide hybrid architecture functionalized with RuO2 nanoclusters for high performance energy storage
Bibliographic record
Abstract
In this study, a one-step thermal process was employed to functionalize a graphene-based composite consisting of expanded graphite (EG) and reduced graphene oxide (rGO) with ruthenium nanoclusters of 2–3 nm in diameter. The Ru/EG-rGO nanocomposites were optimized by varying the EG content and annealing temperature, revealing their effects on substrate interconnectivity, nanoparticle binding affinity, and the degree of reduction of graphene oxide (GO) and ruthenium chloride. The optimized Ru/EG-rGO nanocomposite, with a 1:1 ratio of EG:GO and annealing temperature of 350 °C, exhibited the highest specific capacitance at 382 F g −1 in 0.5 M H 2 SO 4 . In a symmetric capacitor configuration, the material demonstrated superior performance compared to other Ru-based supercapacitors, achieving a high energy density of 22.1 Wh kg −1 at a power density of 0.5 kW kg −1 , and retained 13.6 Wh kg −1 at a power density of 10 kW kg −1 . Furthermore, the material showcased remarkable durability, retaining 97 % of the initial capacitance after 10,000 cycles at 5 kW kg −1 . The high performance and stability, combined with the ease of fabrication, makes this novel Ru/EG-rGO nanocomposite promising for supercapacitor applications, and the design strategies demonstrated in this work can be further applied to the development and large-scale fabrication of energy storage materials. • A hybrid graphene-based substrate was synthesized and used to support RuO 2 nanoclusters. • The nanocomposite was made thermally in one step from common precursors. • The composition and annealing temperature can be tuned to increase performance. • The optimized nanocomposite exhibited high capacitance and high stability.
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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".