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Record W3081755710 · doi:10.1002/mren.202000023

Mapping the Structure–Property Space of Bimodal Polyethylene Using Response Surface Methods. Part 2: Experimental Investigation of Polymer Microstructure and Yield Estimations

2020· article· en· W3081755710 on OpenAlexaff
Paul J. DesLauriers, Jeff S. Fodor, Saeid Mehdiabadi, Venugopal Hegde, João B. P. Soares

Bibliographic record

VenueMacromolecular Reaction Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceYield (engineering)PolymerPolymerizationBranching (polymer chemistry)PolyethyleneMicrostructureBiological systemThermodynamicsChemical engineeringPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract A proof of concept for a quick and easy determination of polymer microstructure and yield estimations made with dual catalyst systems through optimally designed experiments and response surface methodology has been experimentally established. Acceptable accuracy (predicted R 2 > 0.9780) has been achieved on all the primary target responses (blend molar masses and short chain branching). These primary responses are further deconvoluted into underlying Flory distributions for resolution into component properties and subsequently modeled and predicted accurately (predicted R 2 = 0.7440 to 0.9897) for a given set of polymerization conditions. These models are also used to explore their ability to be used for polymerization kinetics evaluation using uptake curves for yield responses. Reasonable predictive ability for yield estimation is also observed (predicted R 2 = 0.9346). This methodology has the makings of a new simplified exploratory pathway for inexpensive kinetic investigation and product prediction for dual metallocene catalyst systems.

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 score0.045
Threshold uncertainty score0.432

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 head0.038
GPT teacher head0.255
Teacher spread0.217 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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