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Record W1963196292 · doi:10.1002/mren.201400038

Mathematical Modeling of Polyether Production from 1,3‐Propanediol: Accounting for Linear Oligomers

2015· article· en· W1963196292 on OpenAlexafffund
Wei J. Cui, Kimberley B. McAuley, Rupert E. v. H. Spence, Tuyu Xie

Bibliographic record

VenueMacromolecular Reaction Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsDuPont (Canada)Queen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonomerDimerOligomerPolymerizationThermodynamicsEtherChemistryProduction (economics)Mass transferPolymer chemistryMaterials scienceBiological systemChemical engineeringPolymerOrganic chemistryChromatographyPhysics

Abstract

fetched live from OpenAlex

A mathematical model is developed to simulate the production of bio‐based polytrimethylene ether glycol (PO3G) using 1,3‐propanediol. The effect of super‐acid catalyst is accounted for in the model, as is mass transfer of small species (water, monomer, and propanal) and linear oligomers (dimer to heptamer). This model correctly predicts dynamic trends in concentrations of linear oligomers, but the predicted maxima in these concentrations appear slightly earlier than in the experimental data. Predictions of the degree of polymerization (DP) are improved compared with those from a previous model, where evaporation of linear oligomers was not considered. Additional model improvements, including accounting for the effects of cyclic oligomers, are suggested.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.232
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2015
Admission routes2
Has abstractyes

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