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Record W2792958214 · doi:10.1002/mren.201700074

Predicting Average Molecular Weights and Branching Level for Self‐Condensing Vinyl Copolymerization in a CSTR

2018· article· en· W2792958214 on OpenAlexafffund
Bradley D. Buren, Yutian R. Zhao, Judit E. Puskás, Kimberley B. McAuley

Bibliographic record

VenueMacromolecular Reaction Engineering · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContinuous stirred-tank reactorBranching (polymer chemistry)DispersityPolymer chemistryResidence time (fluid dynamics)IsobutyleneCopolymerChemistryThermodynamicsTacticityMaterials scienceChemical engineeringPhysical chemistryPolymerPolymerizationOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract A continuous stirred‐tank reactor (CSTR) model is developed to produce arborescent polyisobutylene via carbocationic copolymerization of isobutylene and inimer using multidimensional method of moments. The model is used to predict dynamic changes in average branching level ( B kin ) and number‐average and weight‐average molecular weights ( ). Simulations of this self‐condensing vinyl copolymerization (SCVCP) show a tendency toward higher polydispersity and higher B kin compared to batch reactor simulations conducted using the same recipes and residence times. At high inimer feed concentration and/or long residence time, the model predicts that a CSTR does not reach steady‐state operation due to increasing toward infinity. There is a narrow operating range in which inimer feed concentrations can be adjusted to achieve a desired steady‐state . If SCVCP is to be conducted in a CSTR, it will be important to ensure that residence times and inimer feed concentrations are selected within the stable operating window.

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 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 score0.274
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 head0.006
GPT teacher head0.211
Teacher spread0.204 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations11
Published2018
Admission routes2
Has abstractyes

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