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Record W1993886117 · doi:10.1002/mren.200700026

“Living” Free Radical Polymerization in Tubular Reactors. I. Modeling of the Complete Molecular Weight Distribution Using Probability Generating Functions

2007· article· en· W1993886117 on OpenAlexafffund
Mariano Asteasuain, Matheus Soares, Marcelo Kaminski Lenzi, Michael F. Cunningham, Claudia Sarmoria, José Carlos Pinto, Adriana Brandolin

Bibliographic record

VenueMacromolecular Reaction Engineering · 2007
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersUniversidad Nacional del SurConselho Nacional de Desenvolvimento Científico e TecnológicoConsejo Nacional de Investigaciones Científicas y TécnicasNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMolar mass distributionNitroxide mediated radical polymerizationRadical polymerizationPolymerizationTransformation (genetics)Materials scienceComputer scienceDistribution (mathematics)Polymer chemistryBiological systemThermodynamicsChemistryPolymerMathematicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract This is the first of a series of works aiming at developing a tool for designing “living” free radical polymerization processes in tubular reactors, in order to achieve tailor‐made MWDs. A mathematical model of the nitroxide‐mediated controlled free radical polymerization is built and implemented to predict the complete MWD. It is shown that this objective may be achieved accurately and efficiently by means of the probability generating function (pgf) transformation. Comparison with experimental data is good. The potential of the resulting model for optimization activities involving the complete MWD is also shown. 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.

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations32
Published2007
Admission routes2
Has abstractyes

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