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Record W2329840760 · doi:10.1021/acs.jpcc.5b00257

Ion Spatial Distributions at the Air– and Vacuum–Aqueous K<sub>2</sub>CO<sub>3</sub> Interfaces

2015· article· en· W2329840760 on OpenAlexaff
Matthew A. Brown, Ming‐Tao Lee, Armin Kleibert, Markus Ammann, Javier B. Giorgi

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Ottawa
FundersPaul Scherrer InstitutSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsX-ray photoelectron spectroscopyAqueous solutionIonElectrolyteAnalytical Chemistry (journal)SynchrotronChemistryInorganic chemistryPhysical chemistryNuclear magnetic resonancePhysicsOpticsElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

The spatial distribution of electrolyte ions at water interfaces remains a topic of considerable interest to the atmospheric, geochemical, and physical sciences communities. Here, the depth-resolved spatial distributions of K + and CO 3 2– from 0.5 and 1.1 M aqueous solutions of potassium carbonate (K 2 CO 3 ) are measured by synchrotron-based X-ray photoelectron spectroscopy (XPS) in combination with a liquid microjet. The ion distributions determined from the intensities of the K 2p and C 1s orbitals are consistent with the K + cation residing on average slightly closer to the interface than the CO 3 2– anion. The interface of this solution is the broadest yet reported for an electrolyte solution by depth-resolved XPS, consistent with earlier molecular dynamics simulations of aqueous Na 2 CO 3 that showed a large (>1 nm) ion depletion layer at the interface. Results are compared, where possible, between liquid jets running in vacuum (1 × 10 –4 mbar) and jets in an equilibrated background vapor pressure that is determined by the temperature of the solution (6 mbar in this case). The ion spatial distributions and the molecular-level pictures of the air– and vacuum–aqueous electrolyte interfaces as derived by XPS are identical.

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 score0.005
Threshold uncertainty score0.515

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 integrity0.0000.001
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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