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Record W1986028256 · doi:10.1139/v03-090

Pressure and temperature dependence of the excess thermodynamic properties of binary dimethyl carbonate + <i>n</i>-octane mixtures

2003· article· en· W1986028256 on OpenAlexvenueno aff
Luis Lugo, Marı́a J. P. Comuñas, Enriqueta R. López, Josefa Garcı́a, Josefa Fernández

Bibliographic record

VenueCanadian Journal of Chemistry · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicThermodynamic properties of mixtures
Canadian institutionsnot available
FundersXunta de Galicia
KeywordsChemistryIsobaric processThermodynamicsDimethyl carbonateMolar volumeOctaneHeat capacityIsothermal processGibbs free energyMolarMole fractionAtmospheric pressureVolume (thermodynamics)Organic chemistryPhysical chemistryMethanol

Abstract

fetched live from OpenAlex

In this work we report several excess thermodynamic properties for the dimethyl carbonate + n-octane system in an effort to better understand their behavior over wide temperature and pressure ranges. From previous experimental pVTx data for this system, the changes in the excess molar Gibbs energies, in the excess molar entropies, and in the excess molar enthalpies due to pressure have been determined over a wide temperature range and for pressures up to 25 MPa. A correlation of the excess volume as a function of pressure was used for each composition and temperature, together with a new, recently proposed equation for the excess molar volume as a function of temperature, pressure, and composition. Excess molar enthalpies and excess molar Gibbs energies at 298.15 K and for pressures up to 25 MPa were calculated using literature data at atmospheric pressure. Furthermore, the excess isothermal compressibility, the excess isobaric expansivity, and the excess internal pressure were calculated. The expression for the internal pressure of an ideal mixture suggested recently by Marczak has been used.Key words: excess thermodynamic properties, dimethyl carbonate, n-octane, high pressure.

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 score0.034
Threshold uncertainty score0.541

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 head0.005
GPT teacher head0.169
Teacher spread0.164 · 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
Published2003
Admission routes1
Has abstractyes

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