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Record W2335048484 · doi:10.1021/je300526w

Solubility Measurements and Saturated Liquid Properties of Ternary Systems (Methane + Decane + Octadecane) at 295 K

2012· article· en· W2335048484 on OpenAlexaff
Hossein Nourozieh, Mohammad Kariznovi, Jalal Abedi

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2012
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOctadecaneThermodynamicsTernary operationDecaneMethaneTernary numeral systemChemistrySolubilityBinary systemPhase (matter)Binary numberMaterials sciencePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Experimental vapor–liquid equilibrium (VLE) data have been measured for the ternary system (methane + decane + octadecane) at 295 K over the pressure range (1 to 8) MPa using a PVT apparatus that was designed in-house. Three different (decane + octadecane) binary mixtures were prepared, and the solubility and phase equilibria of these prepared mixtures with methane were studied. The experimental information of saturated liquid phase composition, density, and viscosity was reported at each pressure. The VLE data were correlated with the Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) equations of state (EOS's). The adjustment of binary interaction parameters and the volume translation technique has been employed to correlate the experimental compositions and densities. The adjusted binary parameters from the VLE data of binary pairs (methane + decane) and (methane + octadecane) were used to correlate the generated ternary VLE data. The calculated ternary VLE compositions were found to be in good agreement with the experimental data using the binary parameters from the VLE data of binary pairs for both EOS's. According to the results for the saturated liquid densities, more accurate predictions of the experimental data were obtained using the PR EOS than the SRK EOS.

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)0.000
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 score0.133
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.235
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.

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

Citations6
Published2012
Admission routes1
Has abstractyes

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