Biorenewable Calcite as an Inorganic Filler in Ionic Liquid Gel Polymer Electrolytes for Supercapacitors
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Supercapacitors play a crucial role in the global shift toward cleaner, renewable energy and away from fossil fuels. Ionic liquid electrolytes have a larger electrochemical window than some organic electrolytes and have been mixed with various polymers to make ionic liquid gel polymer electrolytes (ILGPEs), a solid-state electrolyte and separator combination. One way to improve the conductivity of these electrolytes is to add inorganic materials such as ceramics and zeolites to increase their ionic conductivity. Herein, we incorporate a biorenewable calcite from waste blue mussel shells as an inorganic filler in ILGPEs. ILGPEs composed of 80 wt % [EMIM][NTf 2 ] and 20 wt % PVdF- co -HFP are prepared with various amounts of calcite to determine the effect on the ionic conductivity. The optimal addition of calcite is 2 wt % based on the mechanical stability of the ILGPE. The ILGPE with calcite has the same thermostability (350 °C) and electrochemical window (3.5 V) as the control ILGPE. Symmetric coin cell capacitors were fabricated using ILGPEs with 2 wt % calcite and without calcite as a control. Their performance was compared using cyclic voltammetry and galvanostatic cycling. The specific capacitances of the two devices are similar, 110 and 129 F g –1, with and without calcite, respectively.
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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".