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Record W3142650523 · doi:10.1063/5.0046801

Viscoelastic behavior of covalently crosslinked hydrogels under large shear deformations: An approach to eliminate wall slip

2021· article· en· W3142650523 on OpenAlexaff
Milad Kamkar, Mohsen Janmaleki, Elnaz Erfanian, Amir Sanati‐Nezhad, Uttandaraman Sundararaj

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSelf-healing hydrogelsViscoelasticityRheometerRheologySlip (aerodynamics)Materials scienceComposite materialNonlinear systemShear (geology)Polymer chemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Linear and nonlinear viscoelastic properties of hydrogels significantly contribute to functionality, long-term performance, and stability of the hydrogels. With respect to the nonlinear viscoelastic response of chemically crosslinked hydrogels, the vast majority of publications have reported the type III response (weak strain overshoot). Herein, to measure the true mechanical response of hydrogels subjected to large shear deformations, we developed a technique by chemically bonding and sandwiching two surfaces of a hydrogel to treated glass slides attached to the oscillating rheometer's metal plates. Employing this method, for the first time, we were able to completely alleviate errors attributed to the wall slip in the rheological measurements of soft materials, enabling the accurate evaluation of nonlinear behavior of hydrogels. The results show that these hydrogels follow a type II (strain hardening) response. It is argued that the observed type III response of hydrogels, widely reported in the literature, originates from the wall-slip condition, rather than the inherent viscoelasticity of the hydrogels. This insight has important implications for the future development of hydrogel-based or other soft materials.

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 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 score0.079
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.277
Teacher spread0.250 · 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

Citations31
Published2021
Admission routes1
Has abstractyes

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