MétaCan
Menu
Back to cohort
Record W2792810628 · doi:10.1002/macp.201700551

Polyolefins Made with Dual Metallocene Catalysts: How Microstructure Affects Polymer Properties

2018· article· en· W2792810628 on OpenAlexaff
Voradon Voraruth, Anuar Caldera, João B. P. Soares, Siripon Anantawaraskul

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
FundersKasetsart University
KeywordsComonomerPolyethyleneMetalloceneMaterials scienceBranching (polymer chemistry)PolymerCopolymerPost-metallocene catalystLinear low-density polyethyleneEthylenePolymer chemistryComposite materialCatalysisPolymerizationOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Slow crack growth resistance correlates with the lifetime of polyethylene pipes. Since this measurement may take considerable time, alternative ways to estimate it help expedite the development of new polyethylene resins. The primary structural parameter (PSP2), calculated from the distribution of molecular weight and short chain branching of polyethylenes, can predict the stress crack growth resistance of polyethylene resins without needing to measure it over long periods of time. In this investigation, a model is developed to calculate PSP2 of ethylene homopolymers and ethylene/1‐hexene copolymers made with two metallocene catalysts. For each metallocene, the effect of the following parameters on PSP2 is investigated: (1) polymer mass fraction, (2) polymer average molecular weight, and (3) average comonomer fraction. The simulation trends agree with previously published experimental results. The generated 3D surface response plots are useful guides for the design of polyethylenes having optimized slow crack growth resistance.

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 score0.002
Threshold uncertainty score0.820

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 studies0.0000.001
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 head0.189
Teacher spread0.181 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMacromolecular Chemistry and PhysicsSame topicPolymer crystallization and propertiesFrench-language works237,207