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Record W2802973581 · doi:10.1002/mren.201800014

Tailoring Uniform Copolymer Composition Distribution via Policy II RAFT Solution Copolymerization of Styrene and Butyl Acrylate

2018· article· en· W2802973581 on OpenAlexaff
Jie Jiang, Wenjun Wang, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueMacromolecular Reaction Engineering · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsCopolymerComonomerChain transferRaftStyreneButyl acrylatePolymer chemistryMaterials scienceAcrylateMonomerPolymerRadical polymerizationMolar mass distributionPolymerizationChemical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Controlled radical polymerization (CRP) is regarded as a powerful method to design various chain structures. Semibatch monomer feeding strategies in CRP are widely used for targeting the polymer products with desired copolymer composition distribution (CCD) and molecular weight distribution, which define the properties of polymer materials. In this work, the uniform CCD in reversible addition–fragmentation transfer (RAFT) solution copolymerization is targeted via model‐based semibatch Policy II. A RAFT solution copolymerization model is first developed and correlated with batch styrene (St) and butyl acrylate (BA) copolymerization experimental data. St/BA copolymers having three different uniform compositions are produced by the comonomer feeding Policy II. Heat and concentration analysis prove feasibility of the policy. Simulation results show that the uniform CCD can be achieved by both Policy II and Policy I, compared with starved feeding and constant feeding. Policy II gives higher and more steady rates than Policy I.

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 score0.363
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 head0.004
GPT teacher head0.208
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

Citations4
Published2018
Admission routes1
Has abstractyes

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