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Record W2009977585 · doi:10.1021/ie900579h

Polypropylene and Ethylene−Propylene Copolymer Reactor Alloys Prepared by Metallocene/Ziegler−Natta Hybrid Catalyst

2009· article· en· W2009977585 on OpenAlexaff
Lie Lu, Hong Fan, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMetalloceneCopolymerMaterials scienceEthylenePost-metallocene catalystMethylaluminoxanePolypropyleneZiegler–Natta catalystPolymer chemistryCatalysisEthylene propylene rubberNattaPropeneChemical engineeringPolymerizationOrganic chemistryChemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Polypropylene/ethylene−propylene rubber (PP/EPR) reactor alloys were prepared with a metallocene/Ziegler−Natta hybrid catalyst system ( rac -Et(Ind) 2 ZrCl 2 /TiCl 4 /MgCl 2 ) using a process composed of three stages: propylene homopolymerization, metallocene activation, and ethylene−propylene copolymerization. A series of alloy samples were produced and characterized at various copolymerization conditions by varying the methylaluminoxane (MAO)/Zr ratio and monomer composition. It was shown that the metallocene/Ziegler−Natta hybrid system exhibited the features of both metallocene and Ziegler−Natta catalysts during copolymerization. The hybrid catalyst had better ability in incorporating α-olefin than the Ziegler−Natta catalyst owing to the action of metallocene active sites. DSC and IR analyses suggested that EPR in the alloys became random with increased MAO/Zr ratio. In addition, reducing the ethylene content in the feed decreased the activity and promoted the production of random copolymers. An operation window of reaction conditions was identified for the preparation of well-dispersed spherical PP/EPR reactor alloy particles containing up to about 40 wt % EPR.

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)0.001
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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.045
GPT teacher head0.286
Teacher spread0.240 · 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

Citations24
Published2009
Admission routes1
Has abstractyes

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