MétaCan
Menu
Back to cohort
Record W1989577213 · doi:10.1039/c4cp02937b

Ab initio metadynamics study on hydronium ion dynamics at acid-functionalized interfaces: effect of surface group density

2014· article· en· W1989577213 on OpenAlexaff
Swati Vartak, Anatoly Golovnev, Ata Roudgar, Michael Eikerling

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHydroniumMetadynamicsChemistryIonAb initioAb initio quantum chemistry methodsIonic bondingDensity functional theoryPhysical chemistryChemical physicsComputational chemistryMolecular dynamicsOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

This article presents an ab initio metadynamics study of elementary hydronium ion transitions at dense arrays of surface groups with sulfonic acid head groups. Calculations simulate minimally hydrated conditions of the interfacial ionic system. The specific focus is on the influence of the surface group density on hydronium ion transport. Results reveal a high sensitivity of the activation free energy of hydronium translocations to the surface group density. A spontaneous concerted transition with low activation barrier is found at a surface group separation of 6.8 Å. When hydroniums translocate concertedly, the activation barrier of the transition drops by more than a factor of two to the value of 0.25 eV. An approach is presented to determine interaction constants of hydronium ions and anionic surface groups as well as the surface group flexibility from the analysis of frequency spectra. These properties are discussed in the context of a recently developed soliton theory of interfacial proton transport.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.251
Teacher spread0.243 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Chemistry Chemical PhysicsSame topicSpectroscopy and Quantum Chemical StudiesFrench-language works237,207