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Record W2316875293 · doi:10.1021/ie402799u

Targeting Copolymer Composition Distribution via Model-Based Monomer Feeding Policy in Semibatch RAFT Mini-Emulsion Copolymerization of Styrene and Butyl Acrylate

2014· article· en· W2316875293 on OpenAlexaff
Xiaohui Li, Wenjun Wang, Feiyin Weng, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRaftCopolymerChain transferEmulsion polymerizationStyreneMonomerPolymer chemistryButyl acrylateEmulsionAcrylateMaterials sciencePolymerChemical engineeringPolymerizationReversible addition−fragmentation chain-transfer polymerizationRadical polymerizationComposite material

Abstract

fetched live from OpenAlex

Copolymer composition distribution (CCD) is an important parameter of chain microstructure that has significant impact on the material properties of polymer products. Controlled/living radical polymerization (CLRP) has provided a great opportunity in producing polymers with predesigned CCDs through a model-based monomer feeding policy (MMFP) in homogeneous systems. In this work, the MMFP has been expanded to heterogeneous systems. Reversible addition–fragmentation transfer (RAFT) mini-emulsion polymerizations of styrene (St) and butyl acrylate (BA) were carried out with 3-benzyltrithiocarbonyl propionic acid (BCPA) used as a RAFT agent. A kinetic model was developed and correlated to the batch St/BA RAFT mini-emulsion experimental data for parameter estimation. The MMFP was then developed by combining the kinetic model with a semibatch reactor model and applied to the semibatch St/BA RAFT mini-emulsion copolymerization for the synthesis of targeted CCDs. The model agreed well with the polymerization kinetics. A series of St/BA copolymers with predesigned CCDs were successfully synthesized.

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)0.001
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 score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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

Citations29
Published2014
Admission routes1
Has abstractyes

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