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Record W2618767034 · doi:10.1021/acsami.7b04700

Hydrazide-Derivatized Microgels Bond to Wet, Oxidized Cellulose Giving Adhesion Without Drying or Curing

2017· article· en· W2618767034 on OpenAlexafffund
Dong Yang, Emil Gustafsson, Taylor C. Stimpson, A. Eßer, Robert Pelton

Bibliographic record

VenueACS Applied Materials & Interfaces · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaBASF
KeywordsHydrazideCelluloseMaterials scienceAdhesionPolymer chemistryCovalent bondCationic polymerizationAdhesiveChemical engineeringCuring (chemistry)HydrazoneOrganic chemistryChemistryComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Hydrazide-derivatized poly(N-isopropylacrylamide-co-acrylic acid) microgels gave strong adhesion to wet, TEMPO oxidized, regenerated cellulose membranes without a drying or heating step. Adhesion was attributed to hydrazone covalent bond formation with aldehyde groups present on the cellulose surfaces. This is one of only three chemistries we have found that gives significant never-dried adhesion between wet cellulose surfaces. By contrast, for cellulose joints that have been dried and heated before wet testing, the hydrazide-hydrazone chemistry offers no advantages over standard paper industry wet strength resins. The design rules for the hydrazide-microgel adhesives include: cationic microgels are superior to anionic gels; the lower the microgel cross-link density, the higher the adhesion; longer PEG-based hydrazide tethers offer no advantage over shorter attachments; and, adhesion is independent of microgel diameter. Many of these rules were in agreement with predictions of a simple adhesion model where the microgels were assumed to be ideal springs. We propose that the unexpected, high cohesion between neighboring microgels in multilayer films was a result of bond formation between hydrazide groups and residual NHS-carboxyl esters from the preparation of the hydrazide microgels.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2017
Admission routes2
Has abstractyes

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