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Record W2747560975 · doi:10.1002/masy.201600139

Zirconocene Aluminohydride‐Methylaluminoxane Clathrates for Ethylene Polymerization in Slurry

2017· article· en· W2747560975 on OpenAlexaff
Benjamín Padilla‐Gutiérrez, Carolina Ventura‐Hunter, Maricela García‐Zamora, Scott Collins, Alba Nidia Estrada‐Ramirez, Odilia Pérez‐Camacho

Bibliographic record

VenueMacromolecular Symposia · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNuclear materials and radiation effects
Canadian institutionsUniversity of Victoria
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsClathrate hydrateMethylaluminoxaneCatalysisPolyethylenePolymerizationEthyleneMaterials sciencePolymer chemistryChemical engineeringMetalloceneChemistryHydrateOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

The main aim of this work is to obtain heterogeneous, zirconocene aluminohydride/methylaluminoxane (MAO) polymerization catalysts, without using inorganic carriers like silica. The syntheses of zirconocenium ion‐based clathrates, formed from aluminohydride zirconocene complexes activated with MAO, are reported here. Several different approaches were examined for the synthesis of these clathrate compositions; in one approach the catalyst ( n BuCp 2 ZrH 3 AlH 2 /MAO) was first prepared in toluene solution, and the clathrate phase then generated by addition of silicone oil. An alternate approach involved reaction of silicone oil or another clathrate‐forming additive (e.g. KCl) with MAO to form a solidified clathrate, and then introducing the zirconocene aluminohydride complex ( n BuCp 2 ZrH 3 AlH 2 ). The clathrate catalysts were probed in the polymerization of ethylene in hydrocarbon slurry, without using additional co‐catalyst (MAO), or at very low concentrations of modified MAO (MMAO 7, 13 wt‐% in iso‐octane). The catalytic activities of the solid clathrate catalysts were compared as well as the morphology and properties of the polyethylene synthesized in slurry.

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 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 score0.015
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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