Synthesis and Performance Evaluation of Exfoliated Graphene Nanoplatelet Hydrogels As Electrodes for Supercapacitors
Bibliographic record
Abstract
Graphene is an excellent material because of its high electrical conductivity, high surface area, and superior mechanical properties. Stable dispersions of graphene nanoplatelets (GNPs) can be only achieved in organic solvents while in polar solvents the GNPs tend to agglomerate. Therefore, covalent and non-covalent functionalization of the GNP, or the use of surfactants, is required to formulate stable dispersions. Unlike GNP, Graphene oxide (GO) can be readily dispersed in aqueous media because of the hydrophilic oxygen groups on the basal plane and edges. Consequently, GO has amphiphilic material characteristics like a surfactant, and we use it as a dispersing agent for GNP in aqueous solutions. In this work, we formulate highly-concentrated aqueous GO/GNP dispersions (5 mg/ml to 25 mg/ml). The stability of these dispersions mainly depends on the pH, concentrations of GO/GNP and lateral dimensions of the flakes. Stable dispersions are used to synthesize graphene hydrogels which are usually made from reduced graphene oxide (rGO), which is considerably more expensive than GNP obtained from mechanical/thermal exfoliation. Graphene hydrogels are a self-assembled microporous structure that incorporate a liquid phase. The graphene hydrogel matrix has a very high surface area, along with high electronic conductivity, which makes it a very suitable material for supercapacitor applications. The graphene hydrogels that we prepare from GNP, containing a small fraction of GO, are characterized by scanning electron microscopy, electrical conductivity measurements, X-ray diffraction, and Raman spectroscopy. The results indicate that a minimum GO/GNP ratio of 0.1 is required to stabilize the GNP flakes. The three-dimensional self-assembly is promoted due to the reduction of GO. The GNP hydrogels have better mechanical stability compared to a conventional rGO hydrogel; a 50 mg freeze-dried sample can sustain a load of 200 g of weight. A flexible supercapacitor is prepared from the GNP hydrogels. In detail, the symmetrical electrodes are prepared by pressing a 1 mm thick slice of hydrogel onto a carbon cloth current collector and two of these electrodes are separated by a filter paper soaked in a 2 M H 2 SO 4 electrolyte. The supercapacitor is characterized by cyclic voltammetry, electrical impedance spectroscopy as well as galvanostatic charge and discharge measurements. The specific capacitance obtained at 1 A/g is 187 F/g, which is around 30% higher than that of comparable supercapacitor made from rGO hydrogels. Additionally, the specific capacitance and the coulombic efficiency at 10 A/g remains at almost 100% of the initial values, even after 3000 cycles. Figure 1
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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 | 0.002 | 0.002 |
| 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 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".