Carbon Capsules of Ionic Liquid for Enhanced Performance of Electrochemical Double-Layer Capacitors
Bibliographic record
Abstract
Ion accessibility, large surface area, and complete wetting of a carbonaceous electrode by the electrolyte are crucial for high-performance electrochemical double-layer capacitors. Herein, we report a facile and scalable method to prepare electrode–electrolyte hybrid materials, where an ionic liquid (IL) electrolyte is encapsulated within a shell of reduced graphene oxide (rGO) nanosheets as the active electrode material (called rGO-IL capsules). These structures were templated using a Pickering emulsion consisting of a dispersed phase of 1-methyl-3-butylimidazolium hexafluorophosphate ([bmim][PF 6 ]) and a continuous water phase; graphene oxide nanosheets were used as the surfactant, and interfacial polymerization yielded polyurea that bound the nanosheets together to form the capsule shell. This method prevents the aggregation and restacking of GO nanosheets and allows wetting of the materials by IL. The chemical composition, thermal properties, morphology, and electrochemical behavior of these new hybrid architectures are fully characterized. Specific capacitances of 80 F g –1 at 18 °C and 127 F g –1 at 60 °C were achieved at a scan rate of 10 mV s –1 for symmetric coin cells of rGO-IL capsules. These architected materials have higher capacitance at low temperature (18 °C) across many scan rates (10–500 mV s –1 ) compared with analogous cells with the porous carbon YP-50. These results demonstrate a distinct and important methodology to enhance the performance of electrochemical double-layer capacitors by incorporating electrolyte and carbon material together during synthesis.
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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) | 0.001 | 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".