Synthesis and Electrochemical Studies of 3D Reduced Graphene Oxide for Efficient Energy Storage
Bibliographic record
Abstract
Three-dimensional (3D) graphene-based materials are highly desirable for supercapacitor applications; however, their synthesis requires multiple time-consuming steps that involve templates and cross-linkers. Thus, chemically derived graphene through the reduction of graphene oxide is preferred for scalable synthesis. Here, a facile one-pot wet chemical synthesis for an improved highly interconnected 3D reduced graphene oxide (3D-rGO) was developed where the extent of oxidation and the temperature for reduction were optimized. These facile interconnected structures were achieved through covalent linkages via functional groups. The optimized 3D-rGO demonstrated a high C/O ratio (5.2) and nominal defect density ( I D / I G = 0.90) due to its stable structure. Electrochemical characterization revealed that the 3D-rGO possessed a superior specific capacitance of 256 F g –1 in an aqueous electrolyte. Trasatti analysis revealed an 84% contribution from the electrical double-layer (EDL) mechanism, which implied a high sp 2 carbon content and superior conductivity. This superb performance, which was further validated in a quasi-solid-state device in an aqueous gel electrolyte (H 2 SO 4 -PVA), revealing that an excellent gravimetric energy density of 24.4 Wh kg –1 could be delivered at a power density of 1 kW kg –1 . A maximum power density of 28 kW kg –1 delivered a stable 18.8 Wh kg –1 of energy. Furthermore, the supercapacitor exhibited 184 F g –1, with a capacity retention of 91% following 10,000 cycles at 10 A g –1, which is promising for practical energy storage applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".