Ionic Liquid/Non-Ionic Surfactant Mixtures as Versatile, Non-Volatile Electrolytes: Double-Layer Capacitance and Conductivity
Bibliographic record
Abstract
Ionic liquids (ILs) are being increasingly used as processing aids to formulate electrode/electrolyte composites where the electrolyte acts as a template, defining the effective electrolyte-filled pore space between 2D materials such as graphene and MXenes. This is often facilitated with non-ionic surfactants. However, little is currently known about how these surfactants impact double-layer formation and ionic conductivity. Herein, we measure these properties for two commonly used non-ionic surfactants, P123 and Triton X-100 (TX-100) mixed with the IL, 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMImTFSI). A significant increase in the minimum capacitance is observed at 40 wt% surfactant by up to 88% and 102% for P123 and TX-100, respectively. On the other hand, the higher viscosity of the mixtures, lowers the ionic conductivity from 8.5 mS cm −1 (neat IL) to 1 mS cm −1 (40 wt% surfactant). Despite the significantly higher viscosity of P123/IL mixtures compared to TX-100/IL, both electrolyte series show the same reduction in ionic conductivity with respect to concentration. Pulse field gradient nuclear magnetic resonance was also used to show that for both electrolyte series, the diffusion coefficients follow a similar trend.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".