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Record W4223491242 · doi:10.3390/polym14071448

Mathematical Description of the RAFT Copolymerization of Styrene and Glycidyl Methacrylate Using the Terminal Model

2022· article· en· W4223491242 on OpenAlexaff
José Alfredo Tenorio‐López, Juan José Benvenuta‐Tapia, Norma García-Navarro, Eduardo Vivaldo‐Lima, Pascale Champagne, Enrique Saldívar‐Guerra

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsCopolymerGlycidyl methacrylateRaftStyreneTerminal (telecommunication)Polymer chemistryMaterials scienceMethacrylatePolymer scienceComputer scienceComposite materialPolymer

Abstract

fetched live from OpenAlex

A mathematical model for the kinetics, composition and molar mass development of the bulk reversible addition-fragmentation chain transfer (RAFT) copolymerization of glycidyl methacrylate (GMA) and styrene (St), at several GMA molar feed fractions at 103 °C, in the presence of 2-cyano isopropyl dodecyl trithiocarbonate as the RAFT agent and 1,1'-azobis(cyclohexane carbonitrile), as the initiator, is presented. The copolymerization proceeded in a controlled manner and dispersities of the copolymers remained narrow even at high conversions. Experimental data and calculated profiles of conversion versus time, composition versus conversion and molar mass development for the RAFT copolymerization of St and GMA agreed well for all conditions tested, including high-conversion regions. The kinetic rate constants associated with the RAFT- related reactions and diffusion-controlled parameters were properly estimated using a weighted nonlinear multivariable regression procedure. The mathematical model developed in this study may be used as an aid in the design and upscaling of industrial RAFT polymerization processes.

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 categoriesnone
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 score0.144
Threshold uncertainty score0.448

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 head0.026
GPT teacher head0.246
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2022
Admission routes1
Has abstractyes

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