Material Development for Improved Supercapacitor and Battery-Supercapacitor Hybrid Performance
Bibliographic record
Abstract
In this thesis, we develop materials to improve the performance of supercapacitors and battery-supercapacitor hybrids as energy storage devices. First, process parameters used to synthesize graphene hydrogels from high-concentration aqueous graphene nanoplatelet dispersions are optimized using a design of experiments. This method produces an electrode material with a high specific surface area of 810 m2 g-1 and electrical conductivity of 2016 S m-1 which, when applied in an aqueous supercapacitor, results in volumetric capacitances more than 360% greater than a conventional graphene hydrogel electrode made from aqueous graphene oxide dispersions alone. Next, we develop a novel ionic liquid-based battery-supercapacitor hybrid that uses all three oxidation states of the organic species 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO). Usage of the ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate (EMIM BF4) allows for the reversible reduction of TEMPO, resulting in over 1000 charge/discharge cycles. The specific discharge capacity of the battery-supercapacitor hybrid is 226 mAh g−1 at 1 A g−1, which is almost 10 times higher than that of a comparable supercapacitor without added TEMPO. The device can operate at exceedingly high current densities of at least 50 A g-1 while still achieving 88 mAh g−1 due to the rapid kinetics of the TEMPO reactions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".