Decorating Graphene Oxide with Ionic Liquid Nanodroplets: An Approach Leading to Energy-Dense, High-Voltage Supercapacitors
Bibliographic record
Abstract
A major stumbling block in the development of high energy density graphene-based supercapacitors has been maintaining high ion-accessible surface area combined with high electrode density. Herein, we develop an ionic liquid (IL)–surfactant microemulsion system that is found to facilitate the spontaneous adsorption of IL-filled micelles onto graphene oxide (GO). This adsorption distributes the IL over all available surface area and provides an aqueous formulation that can be slurry cast onto current collectors, leaving behind a dense nanocomposite film of GO/IL/surfactant. By removing the surfactant and reducing the GO through a low-temperature (360 °C) heat treatment, the IL plays a dual role of spacer and electrolyte. We study the effect of IL content and operating temperature on the performance, demonstrating a record high gravimetric capacitance (302 F/g at 1 A/g) for 80 wt % IL composites. At 60 wt % IL, combined high capacitance and bulk density (0.76 g/cm 3 ), yields one of the highest volumetric capacitances (218 F/cm 3, at 1 A/g) ever reported for a high-voltage IL-based supercapacitor. While achieving promising rate performance and cycle-life, the approach also eliminates the long and costly electrolyte imbibition step of cell assembly as the electrolyte is cast directly with the electrode material.
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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 | 0.000 | 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 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".