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Record W2914757532 · doi:10.1021/acs.jpcb.8b09285

Comparison of the Osmotic Virial Equation with the Margules Activity Model for Solid–Liquid Equilibrium

2019· article· en· W2914757532 on OpenAlexafffund
Leila Zargarzadeh, Janet A.W. Elliott

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVirial coefficientThermodynamicsActivity coefficientOsmotic coefficientChemistrySulfolaneVirial theoremAqueous solutionPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The main goal of this paper is to compare the general polynomial forms of the osmotic virial equation and the Margules model for the liquid activity coefficients in binary systems. The coefficients/parameters in each model can be calculated based on best fits to experimental phase equilibrium data. Here, the activity coefficient models were combined with the equation of solid-liquid equilibrium. We obtain the coefficients/parameters for the osmotic virial equation and the one- and two-parameter Margules models and compare the accuracy of each model for fitting the experimental data for five water/solute systems: water/glycerol, water/acetic acid, water/propanoic acid, water/mono-ethylene glycol, and water/sulfolane. In obtaining the osmotic virial coefficients, we present a method to fit the equation to the entire range of data including both the ice-formation region and the solute-precipitation region. In expression of the concentration effect of the solute, we showed that the integration constant that arises from the Gibbs-Duhem equation is dependent on the osmotic virial coefficients. The osmotic virial equation is of great interest for its ability to empirically model a very wide range of aqueous solutions and as such is one of the most widely used solution models in biology.

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 score0.263
Threshold uncertainty score0.267

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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