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Record W2321129758 · doi:10.1021/jz300255x

Specific Cation Effects on the Bimodal Acid–Base Behavior of the Silica/Water Interface

2012· article· en· W2321129758 on OpenAlexafffund
Md. Shafiul Azam, Champika Weeraman, Julianne M. Gibbs

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsSilanolElectrolyteChemistryAlkali metalSolvationInorganic chemistryDissociation (chemistry)IonAnalytical Chemistry (journal)Physical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Using nonresonant second harmonic generation spectroscopy, we have monitored the change in surface charge density of the silica/water interface over a broad pH range in the presence of different alkali chlorides. Planar silica is known to possess two types of surface sites with pKa values of ∼4 and ∼9, which are attributed to different solvation environments of the silanols. We report that varying the alkali chloride electrolyte significantly changes the effective acid dissociation constant (pKa(eff)) for the less acidic silanol groups, with the silica/NaClaq and silica/CsClaq interfaces exhibiting the lowest and highest pKa(eff) values of 8.3(1) and 10.8(1), respectively. Additionally, the relative populations of the two silanol groups are also very sensitive to the electrolyte identity. The greatest percentage of acidic silanol groups was 60(2)% for the silica/LiClaq interface in contrast to the lowest value of 20(2)% for the silica/NaClaq interface. We attribute these changes in the bimodal behavior to the influence of alkali ions on the interfacial water structure and its corresponding effect on surface acidity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.239
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations118
Published2012
Admission routes2
Has abstractyes

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