Redox-Active Organic Molecules and Modified Graphene-Based Materials As Active Material Composites for Supercapacitor Applications
Bibliographic record
Abstract
Green and sustainable energy demand has directed the modern world toward producing clean energy storage devices to cut down fossil fuel consumption. Supercapacitors are energy storage devices that have attracted much attention due to owning outstanding properties. Supercapacitors use two main mechanisms to storge charge: I) electrical double layer II) pseudocapacitance. Graphene-based materials have shown promising performance in supercapacitors due to their high surface area and proper electrical conductivity. One of the challenges of working with graphene is the restacking of the graphene sheets, leading to poor performance and lower capacitance. Studies have used doping graphene with different heteroatoms as one of the techniques to overcome the restacking and enhancement of its electrochemical performance. Moreover, redox-active organic molecules, including anthraquinones, phenols, and phenathrequinones, also can be employed to modify graphene. In this work we develop composite active material structures consisting of modified graphene-based materials and organic molecules containing redox active functional groups to provide pseudo-capacitance capabilities. By combining these materials, specific capacitance values above 600 F/g were obtained (at cyclic voltammetry scan rates of 50 mV/s) along with good capacitance retention following repeated electrochemical cycling. In this presentation we will discuss the influence of synthesis route and processing parameters and the resulting structures and performance of the active supercapacitor materials. By correlating these results, we provide insight into the energy storage mechanisms and durability considerations pertaining to these hybrid materials.
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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 | 0.001 | 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 | 0.001 | 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 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".