Ionic Liquid/Non-Ionic Surfactant Mixtures As Versatile, Non-Volatile Electrolytes: Double-Layer Capacitance and Conductivity
Bibliographic record
Abstract
Self-assembly of ionic liquids (ILs) on 2D materials such as graphene oxide and MXenes facilitated by non-ionic surfactants is a promising approach being increasingly used for the fabrication of high surface area electrodes resulting in high performance supercapacitors. However, the impact that non-ionic surfactants have on double-layer formation and ionic conductivity has yet to be explored. These surfactants are not ionically conductive, have low dielectric constants and high viscosity which are expected to impact the final performance of the electrode. In this study we analyze the effect of adding two commonly used non-ionic surfactants, P123 and Triton X-100 (TX-100) to 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMImTFSI) on the double-layer capacitance formed at a glassy carbon electrode by means of electrochemical impedance spectroscopy. The results, surprisingly suggest an improvement of 75% and 116% in the double layer capacitance measured at the open circuit voltage for 40% of P123 and TX-100, respectively. We also interpret the changes in the DC potential dependence of the capacitance via the most up-to-date understanding of double-layer charging mechanisms with ionic liquids. Similar to previous literature on solvent-based diluents such as polycarbonate and acetonitrile, which cause a similar effect, the improved capacitance is attributed to the reduced Debye length resulting from an increased effective ionic charge accrued by the IL when surrounded by the low-dielectric constant surfactant. Both electrolyte series show the same reduction in ionic conductivity (from 8.5 mS/cm to 1 mS/cm) with respect to concentration regardless of the higher viscosity measured for the P123 electrolyte series. Pulsed field gradient nuclear magnetic resonance, is used to determine the diffusion coefficient for the IL as a function of surfactant concentration and allow us to calculate the effective Stokes radius which is found to shrink significantly as a function of surfactant concentration. Similar to the improved capacitance, this is caused by a reduction in ion-ion interactions and an increase in the average effective charge on each ion. These effects make the electrolyte less sensitive than expected to the increased viscosity caused by addition of the more viscous surfactant phase. The ability to improve the capacitance with non-volatile, low dielectric constant additives, without significantly sacrificing ionic conductivity, opens up an improved avenue for completely non-volatile, non-flammable electrolyte design.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".