Pyrrolidinium-based protic ionic liquid electrolytes for high performance RuO2 micro-supercapacitors
Bibliographic record
Abstract
Advances in smart technologies and the Internet of Things (IoT) require compact and efficient energy storage devices. Among microscale systems, RuO2-based micro-supercapacitors (MSCs) can deliver high-power pulses, but the limited stability window of their aqueous electrolytes restricts their energy density. This study investigated the use of pyrrolidinium-based protic ionic liquids (PILs) with different alkyl chain lengths and anion types to achieve pseudocapacitive charge storage in RuO2 MSCs with an extended cell voltage of up to 1.5 V. Of the tested electrolytes, 1-propyl pyrrolidinium trifluoroacetate [Pyr3H]⁺[TFA]⁻ exhibited excellent energy density and cycling performance, and 1-methyl pyrrolidinium tetrafluoroborate [Pyr1H]⁺[BF4]⁻ demonstrated low equivalent series resistance and superior power retention. The experimental findings were consistent with Reactive Force Field (ReaxFF) molecular dynamics simulations which revealed proton exchange and ion diffusion mechanisms in both ionic liquids. Ionogel-based MSCs demonstrated long-term cycling stability, retaining performance over 5000 charge/discharge cycles. These results highlight the potential of using pyrrolidinium-based PILs in on-chip, solid-state MSCs to power microelectronics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".