MétaCan
Menu
Back to cohort
Record W2802140027 · doi:10.1002/mren.201800008

Modeling of Semibatch Solution Radical Copolymerization of Butyl Methacrylate and 2‐Hydroxyethyl Acrylate

2018· article· en· W2802140027 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacromolecular Reaction Engineering · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsAcrylateMethacrylateCopolymerPolymer chemistryButyl acrylateMonomer2-Hydroxyethyl MethacrylateSolventAlkylMaterials science(Hydroxyethyl)methacrylateChemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Nonfunctional monomer feedstocks containing alkyl meth(acrylate) components such as butyl acrylate (BA) and butyl methacrylate (BMA) are replaced or augmented with functional monomers such as 2‐hydroxyethyl methacrylate (HEMA) and 2‐hydroxyethyl acrylate (HEA) to produce reactive polymer chains of lowered molar mass for application in solvent‐borne automotive coatings. The introduction of such polar and functional reactants affects the radical copolymerization kinetics and introduces solvent dependencies. A series of BMA/HEA experiments are performed to determine the influence of these changing kinetic parameters under starved‐feed semibatch operating conditions. A comparison with BMA/BA copolymerizations shows that the influence of hydrogen bonding is small, with the semibatch system well controlled to HEA contents of up to 50 wt%. Thus, the experiments are well represented by a comprehensive generalized copolymerization model that considers relevant methacrylate and acrylate side‐reactions and uses the chain growth parameters measured in previous kinetic investigations.

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.

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 score0.373
Threshold uncertainty score0.708

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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