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Record W2119188462 · doi:10.1139/v01-159

DISQUAC predictions on thermodynamic properties of ternary and higher multicomponent mixtures. II. Results for <i>H</i><sup>E</sup> of ternary mixtures containing nonpolar components, or one polar compound, two polar compounds, or one alcohol and hydrocarbons, or CCl<sub>4</sub>

2001· article· en· W2119188462 on OpenAlexvenueno aff
Juan Antonio González, Nicolás Riesco, Isaiás García de la Fuenté, José Carlos Cobos

Bibliographic record

VenueCanadian Journal of Chemistry · 2001
Typearticle
Languageen
FieldChemical Engineering
TopicThermodynamic properties of mixtures
Canadian institutionsnot available
FundersConsejería de Educación, Junta de Castilla y LeónMinisterio de Economía y Competitividad
KeywordsUNIFACChemistryTernary operationThermodynamicsPolarGroup contribution methodTernary numeral systemEndothermic processAlcoholHydrocarbonActivity coefficientPhysical chemistryOrganic chemistryPhase equilibriumPhase (matter)

Abstract

fetched live from OpenAlex

The ability of the DISQUAC model for predicting excess enthalpies (HE) of ternary systems on the basis of binary parameters only, i.e., neglecting ternary interactions is analyzed. At this end, DISQUAC results for a set of 95 ternary systems are examined. The solutions studied are formed by only hydrocarbons (or CCl4); or by one polar compound (not alcohols) and two hydrocarbons (or CCl4); or by two polar compounds (not alcohols) and one hydrocarbon (or CCl4); or by one alcohol and two hydrocarbons (or CCl4). Most of the HEs analyzed are endothermic, and valid at 298.15 K and atmospheric pressure. The mean deviation between experimental values and DISQUAC results is 5.5% for the ternary systems and 6.5% for the constituent binaries (181 mixtures). The interaction parameters used are valid for the description of thermodynamic properties of binary systems: vapor–liquid equilibria (VLE), liquid–liquid (LLE), and solid–liquid equilibria (SLE), HE and excess heat capacities at constant pressure (CEP), as well as HE and VLE of ternary solutions. Predictions are, in most of the cases, independent of the mixture compounds, or the number of groups present in the system. Larger deviations underline typical shortcomings of the group contributions methods (e.g., Patterson's effect; branching). Note that results for the ternaries and for the constituent binaries are of the same order. This is not the case for the Dortmund version of UNIFAC. The mean deviations obtained using this model are 10.5% and 14% for the ternary and binary mixtures, respectively. Results from other models (original UNIFAC, Flory's theory, Nitta–Chao, UNIQUAC association model) for a number of systems are also compared to those obtained using DISQUAC.Key words: predictions, HE, ternary systems, binary parameters, geometrical methods, Flory, group contributions.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.216
Teacher spread0.194 · 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

Citations10
Published2001
Admission routes1
Has abstractyes

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