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Record W1994228614 · doi:10.1021/ie020790g

On the Correlation of the Activity Coefficients in Aqueous Electrolyte Solutions Using the K-MSA Model

2003· article· en· W1994228614 on OpenAlexaff
Cyrus Ghotbi, Gisele Azimi, Vahid Taghikhani, Juan H. Vera

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsMcGill University
Fundersnot available
KeywordsActivity coefficientElectrolyteIonic bondingChemistryThermodynamicsIonWork (physics)Aqueous solutionIonic strengthPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of this work is to extend the Kelvin mean spherical approximation (K-MSA) model to correlate the mean and individual ionic activity coefficients for symmetric and asymmetric electrolyte solutions at different concentrations and at different temperatures. Revised values of the parameters for the mean and individual ionic activity coefficients of 1:1 electrolytes and new values of the parameters for the mean ionic activity coefficients of asymmetric electrolytes are presented. The effect of the short-range electrostatic term ( P n ) at different size ratios of the ions is examined. Assuming a constant anion diameter and a composition-dependent diameter for the cation, the K-MSA model gives realistic cation-hydrated diameters, in comparison with their crystallographic Pauling diameters. Notably, for both symmetric and asymmetric electrolytes, the like and unlike ionic radial distribution function at contact value are positive over all of the concentration and temperature ranges studied. The results obtained from the K-MSA model compare favorably with those obtained from the Boublik−Mansoori−Carnahan−Starling−Leland mean spherical approximation and Pitzer models for the mean ionic activity coefficients and with those obtained with the Khoshkbarchi−Vera model for the individual ionic activity coefficients.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.137
GPT teacher head0.314
Teacher spread0.177 · 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.

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
Published2003
Admission routes1
Has abstractyes

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