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Record W2431864768 · doi:10.1021/acs.macromol.6b00874

Elucidating Dynamics of Precoordinated Ionic Bridges as Sacrificial Bonds in Interpenetrating Network Hydrogels

2016· article· en· W2431864768 on OpenAlexfundno aff
Jun Yang, Ming‐Guo Ma, Xueming Zhang, Feng Xu

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersNational Science Fund for Distinguished Young ScholarsFundamental Research Funds for the Central UniversitiesMinistry of Education of the People's Republic of ChinaUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsSelf-healing hydrogelsMaterials scienceToughnessEthylene glycolViscoelasticityIonic bondingElastomerInterpenetrating polymer networkPolymerComposite materialStrain ratePEG ratioPolymer chemistryChemical engineeringChemistryIonOrganic chemistry

Abstract

fetched live from OpenAlex

Interpenetrating polymer network (IPN) hydrogels were developed by engineering ionic bridges between carboxylated cellulose nanofibrils (CNFs) and amine-terminated poly(ethylene glycol) (PEG) into a covalent poly(acrylamide) architecture network, and the role of precoordinated CNF–PEG dynamic complexes in the IPN hydrogels viscoelastic dynamics was explored. The results shown that the ionic complexes significantly improved the IPN hydrogels energy dissipation and elastic recovery properties, leading to strain-rate dependent mechanics and notable enhancement in tensile toughness. The uniaxial deformation over a range of strain rates demonstrated that fracture energy peaked at 0.05 s –1 before decreased with further increasing strain rate, consistent with the crack propagation rate result. This straightforward sacrificial bonding strategy validates the hypothesis that elastomers with high toughness and excellent recovery can be achieved by incorporating precoordinated supramolecular associating motifs, which confers control over mechanical properties in a reversible, dynamic, and modular fashion.

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)0.001
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 score0.023
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.278
Teacher spread0.266 · 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

Citations60
Published2016
Admission routes1
Has abstractyes

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