Optimized Graphene Hydrogels for Electrochemical Applications from High-Concentration GO/Gnp Dispersions
Bibliographic record
Abstract
As the world combats climate change, energy storage solutions will be required to bridge the gap between the supply and demand of renewable energy sources. Supercapacitors have a higher power density and cyclic stability than batteries, making them more useful for selected applications. These devices utilize high surface area electrode materials, such as graphene, with enough active sites for sufficient ion adsorption to occur. Graphene hydrogels self-assemble via the hydrothermal reduction of graphene oxide (GO) dispersions. These three-dimensional structures have high specific surface areas while avoiding the use of binders. Binders usually negatively impact the conductivity of the material and require the use of conductive additives which lower specific performance data. As such, graphene hydrogels are attractive as a supercapacitor electrode material. Recently, graphene nanoplatelet (GNP) dispersions stabilized by small amounts of GO were used to form more cost-effective and better-performing hydrogels. Though GO/GNP hydrogels are a suitable alternative to those formed from dispersions of GO, the formulation of this material has yet to be optimized. In this work, we investigate the formation of GO/GNP hydrogels by assessing the influence of several input parameters on the specific surface area and mechanical stability of the hydrogel structure. A set of experiments based on a design of experiments is used. Based on the results, an optimized hydrogel is formed and characterized. Compared to typical hydrogels formed from dispersions of GO, the optimized GO/GNP hydrogel leads to significant improvements in both the specific surface area and conductivity of the material. When used in a supercapacitor, low internal resistance and high stability are observed while volumetric capacitance is increased by up to over 400%.
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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) | 0.001 | 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".