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Record W2155333309 · doi:10.1002/aic.14204

Synthesis of ethylene/1‐octene copolymers with controlled block structures by semibatch living copolymerization

2013· article· en· W2155333309 on OpenAlexaff
Weifeng Liu, Song Guo, Hong Fan, Wenjun Wang, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueAIChE Journal · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersNational Key Research and Development Program of ChinaZhejiang UniversityNational Natural Science Foundation of China
KeywordsComonomerCopolymer1-OcteneEthyleneMethylaluminoxaneMaterials sciencePolymer chemistryOctenePolymerPolymerizationChemical engineeringCatalysisMetalloceneChemistryOrganic chemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

A kinetic model was developed for the living copolymerization of ethylene/1‐octene using the fluorinated FI‐Ti catalyst system, bis[N‐(3‐methylsalicylidene)‐2,3,4,5,6‐pentafluoroanilinato] TiCl 2 /dried methylaluminoxane is presented. The model was first validated by batch polymerization experiments. Kinetic parameters were estimated from the model correlations with online ethylene consumption rates and end‐of‐batch copolymer molecular weight. The model was then used to calculate the microstructural properties of ethylene/1‐octene copolymers with controlled composition profiles (uniform, diblock, and step triblock), which were synthesized using sequential comonomer feeding policies in semibatch copolymerization. The synthesized block copolymers had the exact composition distributions and molecular weights as the model simulated. It was demonstrated that the polymer chain microstructure in the living copolymerization of olefins could be precisely regulated by using semibatch comonomer feeding policies. © 2013 American Institute of Chemical Engineers AIChE J , 59: 4686–4695, 2013

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 categoriesInsufficient 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 score0.016
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.000

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 head0.003
GPT teacher head0.197
Teacher spread0.193 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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