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Using alkylaluminium activators to tailor short chain branching distributions of ethylene/1-hexene copolymers produced with in-situ supported metallocene catalysts

2000· article· en· W2006777789 on OpenAlexaff
Colin Li Pi Shan, Kyung-Jun Chu, João B. P. Soares, Alexander Penlidis

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2000
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComonomerCopolymerMetalloceneBranching (polymer chemistry)EthylenePolymer chemistry1-HexenePolymerHexeneMaterials sciencePolyethyleneMolar mass distributionPost-metallocene catalystChemistryCatalysisPolymerizationOrganic chemistry

Abstract

fetched live from OpenAlex

Copolymerizations of ethylene and 1-hexene were carried out with an in-situ supported metallocene catalyst. Copolymers were produced with different alkylaluminium activators and the effect on molecular weight distribution (MWD) and short chain branching distribution (SCBD) was examined. It was found that different activator types produce polymers with unimodal and narrow MWDs but with very different SCBDs. Each activator exhibits unique comonomer incorporation characteristics which can produce bimodal SCBDs with the use of a single activator. Therefore, by using individual and mixed activator systems, it is possible to control the SCBDs of the resulting copolymers while maintaining narrow MWDs.

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 categoriesMeta-epidemiology (narrow)
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 score0.021
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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

Citations26
Published2000
Admission routes1
Has abstractyes

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