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Record W2327544174 · doi:10.1021/ie400958v

Calculations of Complex Phase Equilibrium by Semigrand Canonical Ensemble

2013· article· en· W2327544174 on OpenAlexaff
Yiping Tang, Ensheng Zhao

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsHoneywell (Canada)
Fundersnot available
KeywordsCanonical ensemblePhase equilibriumPhase (matter)ThermodynamicsStatistical physicsChemistryComputer sciencePhysicsMathematicsStatisticsMonte Carlo method

Abstract

fetched live from OpenAlex

The recently developed method of semigrand canonical (sGC) ensemble ( Tang, Y. J. Chem. Phys. 2012, 136, 034505) was employed to calculate phase equilibria for binary mixtures. The calculations cover vapor–liquid equilibria (VLE), liquid–liquid equilibria (LLE), solid–liquid equilibria (SLE), and solid–solid equilibria (SSE). The thermodynamic models in this study include cubic and noncubic equations of state, the activity coefficient model, and the alloy-based model. On the basis of extensive calculations and comparisons with classical methods, we conclude that the sGC method is universal for calculating complex phase equilibria. In particular, it is very robust in cases of multiple vdW loops with and without intersections, which are often difficult and require special treatments for classical methods. The phase stability characteristics of the sGC method are also addressed.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.083
GPT teacher head0.332
Teacher spread0.249 · 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 designSimulation or modeling
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

Citations0
Published2013
Admission routes1
Has abstractyes

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