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Record W3177488557 · doi:10.1002/pol.20210390

<scp>Aza‐Michael</scp>silicone cure is accelerated by<scp>β‐hydroxyalkyl</scp>esters

2021· article· en· W3177488557 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Polymer Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSynthesis and properties of polymers
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcrylateSiliconePolymer chemistryAmine gas treatingSolventChemistryCatalysisElastomerMichael reactionMoleculePolymerOrganic chemistryMonomer

Abstract

fetched live from OpenAlex

Abstract The aza‐Michael reaction is proving to be a practical, catalyst free method by which a variety of polymers, including silicones, can be cured. However, its adoption may be compromised by slow cure rates; for many applications is it not practical to accelerate cure by heating. OH groups on the amine, acrylate partner or solvent are known to lead to accelerated rates of aza‐Michael reactions. The impact of the location of OH groups on reaction partners is demonstrated using both small molecules and small molecules plus telechelic silicones. While all OH groups are shown to increase reaction rates, a special enhancement is provided by β‐hydroxyalkyl acrylate esters, which have significantly higher rates of reaction than simple acrylates per se, and yet higher reactivities in hydroxylic media. Using this motif, in the absence of solvents, silicone elastomer cure based on the β‐hydroxyalkyl acrylate motif is facile and complete in less than 30 min at room temperature.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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