The Impact of Graphene Sheet Size on the Performance of Interconnected Graphene Foam-Based Supercapacitors
Bibliographic record
Abstract
The increasing demand for energy and growing concerns of propagating greenhouse emissions has directed many investments towards the furtherance of benign energy storage commercialization. Unfortunately, batteries continue to maintain their dominance on the markets of vehicles, and portable electronics. Supercapacitors, however, have actually recently started carving out market shares in these two areas. Supercapacitors can be found complementing existing technology, and in some cases replacing battery powered devices. The major of advantage of utilizing oil as the primary energy source lies in its ability to conjure large amounts of energy in short amounts of time. This is another niche market that supercapacitors can establish for itself: supplying moderate energy storage but capable of large bursts when necessary. \nA derivative of carbon called graphene has also been at the forefront of many research fields. The pace of graphene research has escalated quickly within the past decade, especially after Geim and Novoselov introduced the “scotch-tape method” of obtaining monolayer graphene. Graphene is a two-dimensional sp2 honeycomb allotrope of carbon that exhibit amazing optical, electrical, thermal, electrical and mechanical properties. Although many scientists have discovered many creative ways of producing graphene-based products, some fundamental properties remain a mystery. \nHerein, this proposed work was directed at affirming the relationship between the lateral graphene sheet size and its effect on graphene foam-based supercapacitors. For this, two graphene oxide (GO) materials produced from two different graphite precursors, were deposited into porous nickel and then chemically reduced in-situ. Subsequently, these electrodes were lyophilized and fabricated into a coin cell supercapacitor. These foam devices prepared with smaller planar graphene sheets exhibited 3-16% higher gravimetric capacitance than those prepared with larger sheets. These differences in performance was likely resulted from the higher surface area, lower ion transport resistance and improved ionic wettability inherent to graphene with smaller sheets. However, impedance characterizations has shown that larger graphene sheets possess higher electrical conductivity than that of smaller graphene sheets. \nIt is believed this work will provide some guidance when tailoring the graphene planar sheet size depending on the situation and application. This work may also find use in other energy applications such as electrolyte development, fuel cells and batteries.
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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 | 0.001 |
| 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 | 0.001 |
| 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".