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Record W1988470866 · doi:10.1039/c2cp42107k

Facile determination of formal transfer potentials for hydrophilic alkali metal ions at water|ionic liquid microinterfaces

2012· article· en· W1988470866 on OpenAlexaff
Zhifeng Ding

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2012
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsAlkali metalDiffusionITIESIonic liquidMultiphysicsIonic bondingElectrolyteIonAnalytical Chemistry (journal)ChemistryMaterials scienceChemical physicsFinite element methodElectrodeThermodynamicsPhysical chemistryCyclic voltammetryElectrochemistryPhysicsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.245
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2012
Admission routes1
Has abstractyes

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