Solid-State NMR and Electrochemical Dilatometry Study of Charge Storage in Supercapacitor with Redox Ionic Liquid Electrolyte
Bibliographic record
Abstract
Redox ionic liquids are emerging as promising new electrolytes for supercapacitors, which provide higher capacitance and energy density than organic or ionic liquid electrolytes. The fundamental studies of charge storage mechanism in supercapacitors are of critical importance for the development and applications of devices. Solid-state NMR (SS-NMR) methodology that has the ability to give atomic information on local environments within electrodes has been recently developed to study the mechanism of charge storage in supercapacitors at molecular level. The charge storage in supercapacitors with organic or ionic liquid electrolytes has been studied by SS-NMR. However, there is until now no research on supercapacitors with redox-active electrolyte published. Therefore, the study of charge storage mechanism in supercapacitors with redox-active electrolyte are highly required. In this context, we employed SS-NMR techniques combined with electrochemical dilatometry measurements that are associated with charge induced strain of electrode to investigate in depth the charge storage during charging process in supercapacitors with redox ionic liquid electrolyte. It is revealed that the charging process of supercapacitors with redox ionic liquid electrolyte EMIM FcNTf/ACN is driven by different charge regimes for different voltages, that is, co-ion desorption at low voltage range and subsequently counter-ion adsorption at higher voltage range. The electrochemical dilatometry measurements show macroscopic change of the electrode during charging and further confirm the proposed mechanism obtained from SS-NMR. The results give a detailed picture of the charging mechanism of supercapacitors with redox ionic liquid electrolyte, providing new insights on the charge storage of supercapacitors.
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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 | 0.000 |
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".