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Record W4301396442 · doi:10.1002/cjce.24698

Mathematical modelling for 1,6‐hexanediol diacrylate photopolymerization in presence of oxygen

2022· article· en· W4301396442 on OpenAlexafffundvenue
Anh‐Duong Dieu Vo, Kaveh Abdi, Jurre F. U. Tak, Luuk van der Velden, Robin X. E. Willemse, Marjolein N. van der Linden, Piet D. Iedema, Kimberley B. McAuley

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsQueen's University
FundersElectronic Components and Systems for European LeadershipMitacsEuropean Commission
KeywordsPhotopolymerBifunctionalMonomerOxygenPolymerizationMaterials scienceFourier transform infrared spectroscopyPolymer chemistryChemical engineeringPolymerChemistryComposite materialOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract A dynamic model is proposed for the photopolymerization of 1,6‐hexanediol diacrylate (HDDA) with the bifunctional initiator bis‐acylphosphine oxide (BAPO) in the presence of oxygen. The model tracks time‐varying concentrations of monomer, oxygen, and different radical end groups using ordinary differential equations. An analytical expression is derived for the mass‐transfer of oxygen. Oxygen‐related parameters are estimated using real‐time Fourier‐transform infrared reflection (FTIR) vinyl conversion data, which were collected during polymerization of HDDA films with different thicknesses, initiator concentrations, and light intensities. The resulting model and parameter estimates provide good predictions for experiments involving thin films up to 12 μm, with BAPO levels ranging from 1 to 4 wt.% and relatively low light intensities (200–1000 W/m 2 ).

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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