Facile determination of formal transfer potentials for hydrophilic alkali metal ions at water|ionic liquid microinterfaces
Bibliographic record
Abstract
The formal transfer potentials of hydrophilic alkali metal ions Li(+), Na(+), K(+), Rb(+), and Cs(+) were determined at water|room temperature ionic liquid (w|RTIL) interfaces. A working curve for an interface held at the tip of a micropipette (25 μm in diameter) was developed through simulated cyclic voltammograms (CVs) via finite element analysis with Comsol Multiphysics software. This methodology takes advantage of the symmetric diffusion regime experienced at the w|RTIL micropipette interface between two immiscible electrolytic solutions (micro-ITIES) which generates peak-shaped waves in the forward and reverse scans similar to those in CVs obtained at large (centimeter scale) ITIES. Through the simulation a profile of IT was generated in order to construct the working curve from which, in conjunction with experimentally obtained CVs, the formal transfer potentials were extrapolated. The unique characteristics of diffusion at an interface utilizing a pulled capillary make this approach possible. Additionally, within the simulation the geometry can be tailored to approximate closely the actual physical and experimental conditions. In this way the formal transfer potentials of Li(+), Na(+), K(+), Rb(+), and Cs(+) were found to be 0.565, 0.548, 0.521, 0.531, and 518 V, respectively, at the interface between water and our extremely hydrophobic ionic liquid, trihexyltetradecylphosphonium tetrakis(pentafluorophenyl)borate. The implications of these constants towards the evaluation of metal ion extractions will also be discussed.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".