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Record W3195850709 · doi:10.1002/mren.202100023

Incorporating Hydrophobic Cellulose Nanocrystals inside Latex Particles via Mini‐Emulsion Polymerization

2021· article· en· W3195850709 on OpenAlexafffund
Amir Pakdel, Emily D. Cranston, Marc A. Dubé

Bibliographic record

VenueMacromolecular Reaction Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulsion polymerizationPolymerizationChemical engineeringPolymer chemistryMonomerAcrylateMaterials scienceEmulsionAdhesiveAcrylic acidSodium dodecyl sulfateParticle sizeNucleationPrecipitation polymerizationHydroxypropyl celluloseNanocompositePolymerRadical polymerizationChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Hydrophobic cellulose nanocrystals (CNCs) are encapsulated inside poly(butyl acrylate/vinyl acetate/acrylic acid) latex particles via mini‐emulsion polymerization (MEP). To achieve a genuine MEP, the effects of the concentration of surfactants (sodium dodecyl sulfate/Disponil A3065) and a hydrophobic agent (octadecyl acrylate (ODA)) are optimized in the presence of 0.5 wt% CNC. Using a combination of surfactant and ODA concentrations leading to a particle nucleation method restricted to the monomer droplets, the effects of CNC loading up to 1.5 wt% on the polymerization process and final nanocomposite properties are studied. Despite an increase in particle size and a lower rate of polymerization at higher CNC loadings, the droplet nucleation mechanism remains dominant up to 1.25 wt% CNC loading. Pressure‐sensitive adhesive (PSA) performance for nanocomposites produced using hydrophobic CNCs in MEP decreases whereas performance improves considerably when using hydrophilic and partially hydrophobic CNCs in conventional emulsion polymerization. These results shed light on emulsion polymerization technique selection and CNC surface properties for greener industrial production of water‐based PSAs.

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 score0.117
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
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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