MétaCan
Menu
Back to cohort
Record W3110879481 · doi:10.1021/acs.jpclett.0c03565

Role of Ions on the Surface-Bound Water Structure at the Silica/Water Interface: Identifying the Spectral Signature of Stability

2021· article· en· W3110879481 on OpenAlexafffund
Benjamin Rehl, Julianne M. Gibbs

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan Foundation
KeywordsSum-frequency generationChemical physicsMetastabilitySurface chargeZeta potentialIonSurface waterIonic strengthSurface layerChemistryHydrogen bondIonic bondingMaterials scienceAnalytical Chemistry (journal)Aqueous solutionLayer (electronics)MoleculeNanotechnologyNanoparticleOpticsPhysical chemistryNonlinear optics

Abstract

fetched live from OpenAlex

Isolating the hydrogen-bonding structure of water immediately at the surface is challenging, even with surface-specific techniques like sum-frequency generation (SFG), because of the presence of aligned water further away in the diffuse layer. Here, we combine zeta potential and SFG intensity measurements with the maximum entropy method referenced to reported phase-sensitive SFG and second-harmonic generation results to deconvolute the SFG spectral contributions of the surface waters from those in the diffuse layer. Deconvolution reveals that at very low ionic strength, the surface water structure is similar to that of a neutral silica surface near the point-of-zero-charge with waters in different hydrogen-bonding environments oriented in opposite directions. This similarity suggests that the known metastability of silica colloids against aggregation under both conditions could arise from this distinct surface water structure. Upon the addition of salt, significant restructuring of water is observed, leading to a net decrease in order at the surface.

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 score0.012
Threshold uncertainty score0.885

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.242
Teacher spread0.232 · 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

Citations80
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry LettersSame topicSpectroscopy and Quantum Chemical StudiesFrench-language works237,207