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Record W2011261311 · doi:10.1063/1.1785149

An improved renormalization group theory for real fluids

2004· article· en· W2011261311 on OpenAlexaff
Jianguo Mi, Yigui Li, Yiping Tang

Bibliographic record

VenueThe Journal of Chemical Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsHoneywell (Canada)
FundersMinistry of Education, IndiaMinistry of Earth SciencesNational Natural Science Foundation of China
KeywordsRenormalization groupCritical point (mathematics)Critical exponentBinodalPhysicsCritical phenomenaStatistical physicsThermodynamicsRenormalizationChain (unit)Phase (matter)Phase transitionPhase diagramMathematicsMathematical physicsQuantum mechanicsMathematical analysis

Abstract

fetched live from OpenAlex

On the basis of White's theory, an improved renormalization group (RG) theory is developed for chain bonding fluids inside the critical region. Outside the critical region, the statistical associating fluid theory based on the first-order mean sphere approximation [Fluid Phase Equilibria 171, 27 (2000)] is adopted and all the microscopic parameters are taken directly from its earlier application of real fluids. Inside the critical region, the RG transformation for long-range density fluctuation is derived in the k space, which illustrates explicitly the contributions from the mean-field term, the local density fluctuation, and the nonlocal density fluctuation. The RG theory is applied to describe physical behavior of ten n alkanes (C1-C10) both near to and far from the critical point. With no additional parameters for chain bonding fluids, good results are obtained for critical specific heat and phase coexistence curves and the resulting critical exponents are in good agreement with the reported nonclassic values.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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 designTheoretical or conceptual
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

Citations21
Published2004
Admission routes1
Has abstractyes

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