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Record W2795469189 · doi:10.1021/acs.iecr.8b00451

Monomer Structure and Solvent Effects on Copolymer Composition in (Meth)acrylate Radical Copolymerization

2018· article· en· W2795469189 on OpenAlexafffund
Thomas R. Rooney, Robin A. Hutchinson

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCopolymerPolymer chemistryReactivity (psychology)MethacrylateAcrylateMonomerStyreneSolventChemistryMacromonomerButyl acrylateHydrogen bondMethyl methacrylateMethacrylic acidMaterials scienceOrganic chemistryMoleculePolymer

Abstract

fetched live from OpenAlex

Solvent effects on reactivity ratios (ri) and overall composition-averaged copolymer propagation rate coefficients (kp,cop) are generally not observed during methacrylic ester radical copolymerization. However, hydroxyl-bearing comonomers, such as 2-hydroxyethyl methacrylate (HEMA), lead to significant deviations from expectation for both ri and kp,cop, an effect that is highly dependent on solvent choice and rooted in hydrogen bond interactions. The current understanding of the influence of hydrogen bonding on organic solution (meth)acrylic ester radical (co)polymerization kinetics is reviewed by summarizing trends in structure/reactivity for methacrylate homopropagation rate coefficients (kp) and methacrylate macromonomer relative reactivity during copolymerization. In addition, the peculiarities that characterize the apparent enhanced reactivity of hydroxyl-bearing monomers during copolymerization are outlined. Finally, a modeling framework to systematically capture the effects of solvent and hydrogen bonding on copolymer composition, through specific intramolecular hydrogen bond associations between monomer and growing chain, is presented for several methacrylate, acrylate, and styrene copolymerizations.

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 score0.012
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.300
Teacher spread0.270 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

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