MétaCan
Menu
Back to cohort
Record W2115830557 · doi:10.1002/mren.201000036

Mathematical Modeling of the Microstructure of Poly(propylene) Made with Ziegler‐Natta Catalysts in the Presence of Electron Donors

2010· article· en· W2115830557 on OpenAlex
Ahmad Alshaiban, João B. P. Soares

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacromolecular Reaction Engineering · 2010
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTacticityNattaMonte Carlo methodMaterials sciencePolymerPolymer chemistryPolymerizationMolecular dynamicsPhysical chemistryChemistryComputational chemistryComposite materialMathematics

Abstract

fetched live from OpenAlex

Abstract Two different modeling techniques, the method of moments and Monte Carlo simulation, were compared for propylene polymerization with coordination catalysts including a new mechanistic step, site transformation by electron donors. We used the models to show how the molecular weight and tacticity distributions of several poly(propylene) chain populations were affected by changing the concentration of hydrogen, electron donor, and propylene in the reactor, under steady‐state or dynamic operating conditions. The Monte Carlo simulation describes the molecular weight and tacticity distributions for the whole polymer and chain populations with distinct microstructural characteristics. We have also applied the Monte Carlo model to simulate the pentad sequence distributions and its equivalent 13 C NMR spectra. magnified image

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.

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 score0.026
Threshold uncertainty score0.347

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 head0.004
GPT teacher head0.189
Teacher spread0.184 · 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