MétaCan
Menu
Back to cohort
Record W2058878020 · doi:10.1021/je900830s

Density of Carbon Dioxide Expanded Ethanol at (313.2, 328.2, and 343.2) K

2010· article· en· W2058878020 on OpenAlexaff
Bernhard Seifried, Feral Temelli

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2010
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryEthanolCarbon dioxideAnalytical Chemistry (journal)Atmospheric temperature rangeOptical densityMaximum densityThermodynamicsChromatographyOrganic chemistryOptics

Abstract

fetched live from OpenAlex

The density of ethanol saturated and expanded with carbon dioxide (CO 2 ) was determined at (313.2, 328.2, and 343.2) K and up to pressures close to the mixture critical point using a novel device, consisting of a high-pressure view cell equipped with a sinker attached to a spring balance and a microscopic optical measuring device. The density of CO 2 -expanded ethanol increased with pressure up to a maximum value at each temperature studied. A further increase in pressure caused a pronounced decrease in density until the mixture critical point was reached. The increase in density was temperature-dependent, with a less pronounced increase at higher temperatures. In the temperature and pressure range studied, the maximum increase in density of CO 2 -expanded ethanol was (6.3, 4.8, and 3.7) % at (313.2, 328.2, and 343.2) K, respectively. At all temperatures investigated the density of CO 2 -expanded ethanol exhibited the maximum value at pressures corresponding to a CO 2 density of about 190 kg·m −3, which translates into a reduced CO 2 density of 0.4. The experimental density data for CO 2 -expanded ethanol were correlated to pressure and temperature; furthermore, a new correlation for CO 2 -expanded ethanol density based on the reduced density of CO 2 and temperature was developed.

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 score0.126
Threshold uncertainty score0.616

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 science0.0000.000
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Chemical & Engineering DataSame topicPhase Equilibria and ThermodynamicsFrench-language works237,207