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Record W2791244318 · doi:10.1002/mren.201700068

In Situ Semibatch Emulsion Polymerization of 2‐Ethyl Hexyl Acrylate/<i>n</i>‐Butyl Acrylate/Methyl Methacrylate/Cellulose Nanocrystal Nanocomposites for Adhesive Applications

2018· article· en· W2791244318 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

VenueMacromolecular Reaction Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcMaster UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsEmulsion polymerizationMaterials scienceAdhesiveButyl acrylateMonomerPolymer chemistryAcrylateMethacrylateNanocompositeMethyl methacrylateEmulsionChemical engineeringEthyl acrylatePolymerizationPolymerComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Cellulose nanocrystals (CNCs) are safe, “green,” hydrophilic nanoparticles. CNCs are added in situ during a semibatch 2‐ethyl hexyl acrylate (EHA)/ n ‐butyl acrylate (BA)/methyl methacrylate (MMA) emulsion polymerization. As EHA is a more hydrophobic monomer, manipulation of the monomer feed composition allows for the evaluation of the effect of hydrophobicity on CNC distribution in the nanocomposite and ultimately on adhesive properties. The adhesive properties (loop tack, peel strength, and shear strength) of three different EHA/BA/MMA latex formulations are shown to simultaneously improve with increasing CNC loading. However, the hydrophobicity of the EHA leads to a nonuniform distribution of CNCs in the latex films. Comparison of the in situ polymerized nanocomposites to their blended counterparts is also made.

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)0.001
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 score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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