Characterizing the Influence of Water on Charging and Layering at Electrified Ionic‐Liquid/Solid Interfaces
Bibliographic record
Abstract
The importance of water on molecular ion structuring and charging mechanism of solid interfaces in room temperature ionic liquid (RTIL) is unclear and has been largely ignored. Water may alter structures, charging characteristics, and hence performance at electrified solid/RTIL interfaces and is utilized in various fields including energy storage, conversion, or catalysis. Here, atomic force microscopy and surface forces apparatus experiments are utilized to directly measure how water alters the interfacial structuring and charging characteristics of [C 2 mim][Tf 2 N] on mica and electrified gold surfaces. On hydrophilic and ionophobic mica surfaces, water‐saturated [C 2 mim][Tf 2 N] dissolves surface‐bound cations, which leads to high surface charging and strong layering. In contrast, layering of dry RTIL at weakly charged mica surfaces is weakly structured. At electrified, hydrophobic, and ionophilic gold electrodes, significant water effects are found only at positive applied electrochemical potentials. Here, the influence of water is limited to interactions within the RTIL layers, and is not related to a direct electrosorption of water on the polarized electrode. More generally, the results suggest that effects of water on interfacial structuring of RTIL strongly depend on both (1) surface charging mechanism and (2) interfacial wetting properties. This may greatly impact utilization/design of RTILs and surfaces for interface‐dominated processes.
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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 | 0.001 |
| 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".