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Record W2042096872 · doi:10.1063/1.481306

Density fluctuations and shear viscosity of molecular liquids: Carbon dioxide and nitrogen

2000· article· en· W2042096872 on OpenAlexaff
Kyunil Rah, Byung Chan Eu

Bibliographic record

VenueThe Journal of Chemical Physics · 2000
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsViscosityThermodynamicsIntermolecular forceTemperature dependence of liquid viscosityChemistryShear viscosityDiffusionRelative viscosityShear stressVolume viscosityReduced viscosityShear ratePhysicsMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

An expression for the shear viscosity of molecular liquids is derived from the statistical expression for the stress tensor by taking into consideration density fluctuations over the intermolecular force range. The viscosity formula consists of a low density term given in terms of the Chapman–Enskog viscosity and a density dependent term reminiscent of the Stokes–Einstein relation between the viscosity and the self-diffusion coefficient. According to this formula, the shear viscosity of molecular liquids can be calculated in terms of intermolecular site–site forces, the corresponding pair correlation functions, and the self-diffusion coefficient as well as the Chapman–Enskog viscosity at low density. By treating the viscosity expression as a semiempirical formula where the experimental and numerically simulated self-diffusion coefficients available in the literature are used, the shear viscosities of nitrogen and carbon dioxide, both of which are treated as a rigid linear rotator with two sites, are calculated and compared with experiment. Agreement between theory and experiment is found very good qualitatively and quantitatively.

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 score0.048
Threshold uncertainty score0.222

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 integrity0.0000.000
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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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

Citations29
Published2000
Admission routes1
Has abstractyes

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