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Record W4221046066 · doi:10.1021/acsapm.2c00006

Modeling of Thermo-Responsive Stiffening of Poly(oligo(ethylene glycol)methacrylate)–Cellulose Nanocrystal Hydrogels

2022· article· en· W4221046066 on OpenAlexafffund
Rasool Nasseri, Kam Chiu Tam

Bibliographic record

VenueACS Applied Polymer Materials · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsStiffeningMaterials scienceEthylene glycolMethacrylatePolymer chemistryChemical engineeringPolymerSolventDynamic mechanical analysisComposite materialMonomerChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Understanding the phase transition-induced stiffening of thermo-responsive hydrogels is crucial for their practical applications. Here, we used theoretical models to describe the stiffening during the isochoric thermal transitions of a series of model thermo-responsive hydrogels consisting of hydrazide-functionalized poly(oligo(ethylene glycol)methyl ether methacrylate) (POEGMA-H) and oxidized cellulose nanocrystals. Beyond the transition temperature, theoretical models showed that the magnitude of the force required to extend a collapsed chain in a poor solvent was determined by the exposed surface area of the chain and interfacial tension between the polymer and solvent. Storage modulus measurements of the hydrogels with different molar percentage of OGEMA300 monomers confirmed the role of the exposed surface area of the chain in the solvent on the thermal stiffening of thermo-responsive hydrogels. The reduction in the magnitude of thermal stiffening of hydrogels in the presence of isopropyl alcohol confirmed the effect of interfacial tension on the thermal stiffening of thermo-responsive hydrogels. The ideas presented in this study will facilitate the development of stimuli-stiffening hydrogels for biomedical applications.

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 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 score0.004
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.014
GPT teacher head0.232
Teacher spread0.217 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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