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Record W3045286596 · doi:10.1021/acs.jced.0c00175

Density and Viscosity of CO<sub>2</sub> + Ethanol Binary Systems Measured by a Capillary Viscometer from 308.15 to 338.15 K and 15 to 45 MPa

2020· article· en· W3045286596 on OpenAlexaff
Teng Zhu, Houjian Gong, Mingzhe Dong

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2020
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersNatural Science Foundation of Shandong ProvinceChina University of Petroleum, BeijingNational Natural Science Foundation of China
KeywordsViscometerViscosityThermodynamicsMole fractionChemistryBinary numberMixing (physics)Capillary actionAnalytical Chemistry (journal)Reduced viscosityBinary systemWork (physics)ChromatographyPhysicsMathematics

Abstract

fetched live from OpenAlex

Density and viscosity of CO2 + ethanol binary systems were measured by a high-pressure capillary viscometer at different ethanol mole fractions of 0, 0.067, 0.135, 0.203, 0.271, and 1 with temperature from 308.15 to 338.15 K and pressure from 15 to 45 MPa. The density and viscosity of the unitary and binary systems decrease when temperature increases and are found to increase with increasing pressure. Meanwhile, increasing ethanol mole fraction will increase the viscosity but make density reach to the maximum point. Based on these experimental data, volumes of mixing and viscosity deviation were calculated, which all showed negative values. Besides, the absolute value of deviation increases with increasing temperature or ethanol concentration but shows a negative relationship with pressure. Further, the perturbed-chain statistical associating fluid theory equation of state was introduced to investigate the correlation between densities in different systems by adjusting the binary interaction parameter (kij). The viscosities of pure CO2, pure ethanol, and binary mixtures were correlated by Heidaryan function, Baylaucq function, and Song mixing rule, respectively. All these models show very good agreement with the experimental data obtained in this work.

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 categoriesMeta-epidemiology (narrow)
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 score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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.

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

Citations26
Published2020
Admission routes1
Has abstractyes

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