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Record W2016798115 · doi:10.1081/pre-120014693

MATHEMATICAL MODELING OF PARTICLE MORPHOLOGY DEVELOPMENT INDUCED BY RADICAL CONCENTRATION GRADIENTS IN SEEDED STYRENE HOMOPOLYMERIZATION

2002· article· en· W2016798115 on OpenAlexaff
W. P. Krywko, Kimberley B. McAuley, Michael F. Cunningham

Bibliographic record

VenuePolymer Reaction Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Theoretical and Applied Studies in Material Sciences and Geometry
Canadian institutionsQueen's University
Fundersnot available
KeywordsSeedingMorphology (biology)StyreneParticle (ecology)Chemical engineeringMaterials scienceChemical physicsChemistryCopolymerThermodynamicsPhysicsPolymerComposite materialBiologyEcology

Abstract

fetched live from OpenAlex

The development of concentration gradients in the seeded emulsion homopolymerization of styrene has been studied using a mathematical model. Under ideal conditions, emulsion polymer particles are expected to be relatively uniform across their radius. When this is true, kinetic models can be reliably used to predict the reaction rates, molecular weight, and composition (for copolymerizations). However for large particles, large radial gradients in the polymer radical concentration can develop during polymerization. While gradients in the monomer concentration are not observed, the radical concentration gradients result in non-uniform reaction rates across the particle radius, rendering conventional models unreliable. This study examines the effect of seed particle diameter and various kinetic parameters on the development of particle morphology.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.205
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

Citations18
Published2002
Admission routes1
Has abstractyes

Explore more

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