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Record W2105775946 · doi:10.1002/ceat.201000251

Simulation of Polymerization Kinetics and Molecular Weight Development in the Microwave‐Activated Emulsion Polymerization of Styrene using EMULPOLY<sup>®</sup>

2010· article· en· W2105775946 on OpenAlexaff
Gabriel Jaramillo‐Soto, M. Ramírez‐Cupido, José Alfredo Tenorio‐López, Eduardo Vivaldo‐Lima, Alexander Penlidis

Bibliographic record

VenueChemical Engineering & Technology · 2010
Typearticle
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y Tecnología
KeywordsEmulsion polymerizationStyreneMicrowaveKineticsPolymerizationMonomerEmulsionPolymer chemistryMaterials scienceReaction rate constantChemistryChemical engineeringCopolymerPolymerOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract The emulsion polymerization of styrene, activated by microwave irradiation and conductive heating, was modeled using the EMULPOLY® simulation package of the University of Waterloo. Microwave‐activated initiation was modeled as adding a hypothetical second initiator. The kinetic rate constants and model parameters used in the simulation were taken from the simulator database, with the exception of the microwave activation constant, kir, and the efficiency, fr, of the hypothetical initiator. Model predictions of conversion, number‐ and weight‐average molecular weights, for microwave‐ and thermally activated systems agreed well with the experimental data reported in the literature. The effects of microwave power supplied to the system on monomer conversion, molecular weight values and particle number were analyzed.

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)0.000
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.208
Teacher spread0.203 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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