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Record W2073631829 · doi:10.1002/mren.201000034

Modeling the Effects of Reactor Backmixing on RAFT Polymerization

2010· article· en· W2073631829 on OpenAlexaff
Boris Bitsch, Christopher Barner‐Kowollik, Shiping Zhu

Bibliographic record

VenueMacromolecular Reaction Engineering · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRaftChemistryKineticsPolymerizationMonomerMolar mass distributionChain transferReversible addition−fragmentation chain-transfer polymerizationPolymerThermodynamicsRadical polymerizationChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The effects of reactor backmixing on the outcome of RAFT polymerizations are examined. The kinetic model for the RAFT reaction is based on the first principle of mass balances and the method of moments. Two reactor models were applied for the simulation of backmixing, i.e., a PFTR with loop and multiple CSTRs in series. The models predict the behavior of monomer conversion, molecular weights, their distributions as well as the polydispersities for various chain types at different levels of backmixing. Both models demonstrated that a significant impact of reactor backmixing on RAFT kinetics and polymer molecular weight development exists. 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations20
Published2010
Admission routes1
Has abstractyes

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