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Record W3175769285 · doi:10.1002/mren.202100006

Systematic Comparison of Slurry and Gas‐Phase Polymerization of Ethylene: Part I Thermodynamic Effects

2021· article· en· W3175769285 on OpenAlexaff
Arash Alizadeh, Vasileios Touloupidis, João B. P. Soares

Bibliographic record

VenueMacromolecular Reaction Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolyethyleneMaterials scienceCrystallinityPolymerizationDiluentPolymerChemical engineeringPhase (matter)EthylenePolymer chemistryAmorphous solidCoordination polymerizationOrganic chemistryChemistrySolution polymerizationComposite materialCatalysis

Abstract

fetched live from OpenAlex

Abstract The Sanchez–Lacombe model is used to investigate the multiphase and multicomponent thermodynamic equilibrium during ethylene polymerization and ethylene/1‐hexene copolymerization in slurry and gas‐phase reactors. The simulation study focuses on the interactions among ethylene, polyethylene, hydrogen, 1‐hexene, and n ‐hexane under typical polymerization conditions. When used as a diluent, n ‐hexane increases the concentrations of all reactants in the amorphous polymer phase due to the cosolubility effect. Moreover, n ‐hexane significantly swells the amorphous polyethylene. This means that a polyethylene particle with the same degree of crystallinity has a larger amorphous phase fraction in slurry than in gas‐phase reactors. Consequently, if the gas phase concentration is the same in both modes of polymerization, the concentration of all reactive species in semi‐crystalline polyethylene particles will be higher in slurry reactors. The thermodynamic equilibrium simulations agree with the reported experimental results and can explain why supported catalysts behave differently in slurry and gas‐phase polymerizations.

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 score0.070
Threshold uncertainty score0.416

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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