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Record W4317435366 · doi:10.1002/cjce.24854

Progress in the catalyst for ethylene/α‐olefin copolymerization at high temperature

2023· article· en· W4317435366 on OpenAlexvenueno aff
Feng Li, Weifeng Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsnot available
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsComonomerPolyolefinCopolymerPost-metallocene catalystMaterials sciencePolymerizationMetallocenePolymer chemistryOlefin fiberElastomerEthylenePolymerCatalysisChemical engineeringOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Ethylene/α‐olefin copolymers are one of the most widely‐used polyolefin materials. With the continuous improvement of polyolefin catalysts, high‐performance polyolefin materials were synthesized by adjusting the chain microstructure, changing the comonomer type and comonomer insertion amount, among which the ethylene/α‐olefin random copolymer elastomer (POE) and olefin block copolymer elastomer (OBC) are the most famous and well accepted by the market. The excellent properties of POE and OBC first depend on their polymer chain microstructure. The chain microstructure of polyolefins is fundamentally determined by the catalysts, polymerization conditions, comonomer feed policies, and reaction engineering. High‐performance ethylene/α‐olefin copolymer elastomers are currently prepared by high‐temperature solution polymerization process, which needs to be carried out at a temperature above the melting point of the polymer and is beneficial to speed up the polymerization reaction rate and control the polyolefin chain microstructure. However, the high‐temperature solution polymerization process launched more stringent requirements for the olefin coordination polymerization catalyst. Systematic reports on catalysts for high‐temperature solution copolymerization of ethylene and α‐olefins are lacking. In this review, we screened some catalysts suitable for the controllable copolymerization and high‐temperature solution copolymerization of ethylene/α‐olefin based on the catalyst's heat resistance, copolymerization activity, comonomer insertion ability, molecular weight, and distribution of the copolymer, including traditional Z–N catalysts, metallocene catalysts, and post‐metallocene catalysts. And the future development of catalysts for high‐temperature solution copolymerization of olefins, catalysts for precise control of polyolefin chain microstructures, and catalysts for olefin copolymerization with polar monomers at high temperature are envisaged.

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 score0.008
Threshold uncertainty score0.316

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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

Citations28
Published2023
Admission routes1
Has abstractyes

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