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Record W2315845137 · doi:10.1021/je4000394

Equilibrium Properties of (Carbon Dioxide + <i>n</i>-Decane <i>+ n</i>-Octadecane) Systems: Experiments and Thermodynamic Modeling

2013· article· en· W2315845137 on OpenAlexaff
Hossein Nourozieh, Bita Bayestehparvin, Mohammad Kariznovi, Jalal Abedi

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2013
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOctadecaneSolubilityChemistryCarbon dioxideThermodynamicsDecaneEquation of stateViscosityPhase (matter)Binary systemOrganic chemistryBinary number

Abstract

fetched live from OpenAlex

Solubility of carbon dioxide in n -octadecane and in three binary mixtures of n -octadecane and n -decane (0.25, 0.5, and 0.75 mol fractions of n -decane) has been measured at 323 K and at the pressures (1 to 6) MPa. Prior to phase equilibrium measurements, the density and viscosity of n -octadecane and of prepared binary mixtures were measured. Then, the phase behavior measurements were undertaken with an in-house designed PVT apparatus. The solubility of carbon dioxide in liquid hydrocarbons and its effect on the physical properties were examined. Increase in the pressure caused an increase in the carbon dioxide solubility and consequently, a decline in the viscosity of saturated liquid phase and an increase in the density of gas-expanded liquid were observed. The carbon dioxide solubility was lower in the heavier liquid mixture. The experimental data were well modeled with Soave–Redlich–Kwong and Peng–Robinson equations of state. Both equations of state have almost the same predictions for solubility while the Peng–Robinson equation of state is superior for density prediction.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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

Citations38
Published2013
Admission routes1
Has abstractyes

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