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Record W4233043506 · doi:10.26434/chemrxiv.12673736

Structure of the Silica/Divalent Electrolyte Interface: Molecular Insight into Charge Inversion with Increasing pH

2020· preprint· en· W4233043506 on OpenAlexafffund
Mokhtar Rashwan, Benjamin Rehl, Adrien Sthoer, Akemi Darlington, Md. Shafiul Azam, Hongbo Zeng, Qingxia Liu, Eric Tyrode, Julianne M. Gibbs

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Alberta
FundersCanadian Centre for Clean Coal/Carbon and Mineral Processing TechnologiesNatural Sciences and Engineering Research Council of CanadaStiftelsen för Strategisk ForskningCanada First Research Excellence FundAlfred P. Sloan FoundationUniversity of AlbertaTechnische Universität München
KeywordsChemistryIonElectrolyteAdsorptionDivalentHydroxideInorganic chemistryColloidCalcium hydroxideSurface chargeMoleculeInfrared spectroscopyAnalytical Chemistry (journal)Physical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The molecular origin of overcharging at mineral oxide surfaces remains a cause of contention within the geochemistry, physics, and colloidal chemistry communities owing to competing “chemical” vs “physical” interpretations. Here, we combine vibrational sum frequency spectroscopy and streaming potential measurements to obtain molecular and macroscopic insights into the pH-dependent interactions of calcium ions with a fused silica surface. In 100 mM CaCl2 electrolyte, we observe evidence of charge neutralization at pH~10.5, as deducted from a minimum in the interfacial water signal. Concurrently, adsorption of calcium hydroxide cations is inferred from the appearance of a spectral feature at ~3610 cm-1. However, the interfacial water signal increases at higher pH, while adsorbed calcium hydroxide appears to remain constant, indicating that overcharging results from hydrated Ca2+ ions present within the Stern layer. These findings suggest that both specific adsorption of hydrolyzed ions and ion-ion correlations of hydrated ions govern silica overcharging with increasing pH.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.008
GPT teacher head0.233
Teacher spread0.226 · 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 designSimulation or modeling
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
Published2020
Admission routes2
Has abstractyes

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