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Record W4394995675 · doi:10.1021/acs.biomac.4c00138

The Effect of Cellulose Nanocrystal Reassembly on Latex-Based Pressure-Sensitive Adhesive Performance

2024· article· en· W4394995675 on OpenAlexafffund
Parisa Bayat, Kelly M. Meek, Maryam Movafagh, Emily D. Cranston, Michael F. Cunningham, Pascale Champagne, Timothy Morse, Michael V. Kiriakou, Sean R. George, Marc A. Dubé

Bibliographic record

VenueBiomacromolecules · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsNational Research Council CanadaQueen's UniversityUniversity of British ColumbiaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdhesiveMaterials scienceComposite materialEmulsion polymerizationPolymerizationMethyl methacrylateShear strength (soil)PolymerAdhesionCelluloseAcrylatePolymer chemistryChemical engineeringCopolymerLayer (electronics)

Abstract

fetched live from OpenAlex

Different cellulose nanocrystal (CNC) forms (dried vs never-dried) can lead to different degrees of CNC reassembly, the formation of nanofibril-like structures, in nanocomposite latex-based pressure-sensitive adhesive (PSA) formulations. CNC reassembly is also affected by CNC sonication and loading as well as the protocol used for CNC addition to the polymerization. In this study, carboxylated CNCs (cCNCs) were incorporated into a seeded, semibatch, 2-ethylhexyl acrylate/methyl methacrylate/styrene emulsion polymerization and cast as pressure-sensitive adhesive (PSA) films. The addition of CNCs led to a simultaneous increase in tack strength, peel strength, and shear adhesion, avoiding the typical trade-off between the adhesive and cohesive strength. Increased CNC reassembly resulted from the use of dried, redispersed, and sonicated cCNCs, along with increased cCNC loading and addition of the cCNCs at the seed stage of the polymerization. The increased degree of CNC reassembly was shown to significantly increase the shear adhesion by enhancing the elastic modulus of the PSA films.

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)0.001
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 score0.006
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.266
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2024
Admission routes2
Has abstractyes

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