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Record W2013961068 · doi:10.1002/mren.200800055

Gradient Copolymers by ATRP in Semibatch Reactors: Dynamic Monte Carlo Simulation

2009· article· en· W2013961068 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacromolecular Reaction Engineering · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComonomerCopolymerAcrylonitrileDispersityPolymer chemistryStyreneMethyl methacrylateMonte Carlo methodMaterials scienceMolar mass distributionPolymerizationMethacrylateChemical engineeringChemistryPolymerMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract We developed a dynamic Monte Carlo model for ATRP in semibatch reactors. Semibatch reactors can be used to produce gradient copolymers even if the difference between the reactivity ratios of the comonomers is not significant by using different comonomer feed policies. The model was used to predict average molecular weights, polydispersity index, copolymer composition and complete distributions of molecular weight, chemical composition, and comonomer sequence length at any polymerization time. Two case studies, poly[styrene ‐co‐ (methyl methacrylate)] and poly[acrylonitrile ‐co ‐(methyl methacrylate)], were chosen to demonstrate the effect of comonomer feed compositions on the final chemical composition distribution of the copolymer. 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.210
Teacher spread0.207 · 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