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Structure of Electrical Double Layer at Mica/KI Solution Interface

2012· article· en· W2122589977 on OpenAlexvenueno aff
Hiroshi Sakuma, Hironori Nakao, Yuichi Yamasaki, Katsuyuki Kawamura

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2012
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsnot available
Fundersnot available
KeywordsMicaAdsorptionChemistryAqueous solutionElectrokinetic phenomenaIonChemical physicsHalideAlkali metalMoleculeScatteringColloidDispersion (optics)Electron densityElectronInorganic chemistryPhysical chemistryMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Solid/liquid interfaces control many physical and chemical processes such as electrokinetic phenomena, dispersion of colloidal particles, heteroepitaxial growth of alkali halide, removal of toxic elements in water. The atomic distribution at solid/liquid interfaces is strongly correlated with these properties, and understanding these atomic structures is necessary in order to establish the fundamental physics and chemistry of solid/liquid interfaces. In this study, we investigated the structure of interfaces of mica with aqueous KI solution using surface x-ray scattering. The sub-angstrom-scale electron density profile of the interface is revealed as a function of the distance normal to the interface. The electron density of the KI solution oscillates to remain 10 Å away from the surfaces. The oscillations are interpreted in terms of the adsorbed hydrated K+ ions, adsorbed water molecules, and water molecules surrounding the hydrated ions. The adsorbed K+ ions are present as inner sphere complexes and the number is enough to compensate the negatively charged mica surface. No significant difference appeared between the surface x-ray scattering profiles of the KCl and KI solution interfaces, indicating that their interfacial structures are similar.

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 categoriesnone
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 score0.031
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.270
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2012
Admission routes1
Has abstractyes

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