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Record W2000716232 · doi:10.1021/ie049067b

Gas-Phase Ethylene/Hexene Copolymerization with Metallocene Catalyst in a Laboratory-Scale Reactor

2005· article· en· W2000716232 on OpenAlexaff
Bo Kou, Kimberley B. McAuley, Cheng‐Chih Hsu, D. W. Bacon, K. Zhen Yao

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsMetallocenePolymerizationHexeneBranching (polymer chemistry)CatalysisCopolymerEthylenePost-metallocene catalyst1-HexenePolymer chemistryMaterials scienceChemical engineeringChemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Gas-phase ethylene and hexene copolymerization using a silica-supported ( n -BuCp) 2 ZrCl 2 metallocene catalyst has been investigated in a 2 L laboratory reactor. Replicate experimental runs were conducted to confirm the reproducibility of measured responses, which included polymerization rate, reactant concentrations, and copolymer properties. Comparisons of polymerization rate profiles and catalyst activity were made using a number of designed experimental runs. The experiments revealed that triisobutyl aluminum scavenger was the most important cause of low catalyst activity, and a low initial polymerization rate that was followed by a rate increase. The effects of other influencing factors, including residence time, temperature, pressure, concentration of reactants, catalyst, and cocatalyst, were also investigated. As expected, hydrogen concentration and hexene concentration had significant effects on molecular weight and short-chain branching, respectively. In addition, hexene enhanced the polymerization rate and catalyst activity, while cocatalyst and hydrogen both led to a lower polymerization rate. The results from this study provide important quantitative information that will be used for parameter estimation in fundamental models.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.299
Teacher spread0.257 · 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

Citations17
Published2005
Admission routes1
Has abstractyes

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