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Record W2809064886 · doi:10.1039/c8tb01160e

Polyelectrolyte-based physical adhesive hydrogels with excellent mechanical properties for biomedical applications

2018· article· en· W2809064886 on OpenAlexaff
Wenxiang Li, Ruyan Feng, Rensheng Wang, Dan Li, Wenwen Jiang, Hanzhou Liu, Zhenzhong Guo, Michael J. Serpe, Liang Hu

Bibliographic record

VenueJournal of Materials Chemistry B · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsSoochow UniversityNatural Science Foundation of Hubei ProvinceNational Natural Science Foundation of China
KeywordsSelf-healing hydrogelsMaterials scienceAdhesivePolyelectrolyteNanotechnologyComposite materialPolymer sciencePolymer chemistryPolymer

Abstract

fetched live from OpenAlex

Physically crosslinked hydrogels were synthesized by copolymerization of acrylamide and acrylic acid monomers in the presence of cationic polyelectrolyte polydimethyldiallylammonium chloride. The fully physically crosslinked hydrogel showed good mechanical properties and good adhesion with a variety of substrates. These advantages can be attributed to the homogeneous distribution of crosslinking points due to hierarchical hydrogen bonds and electrostatic attractions in the hydrogel networks. Furthermore, these non-covalent bonds provided an effective pathway to dissipate energy. The mechanical properties of the hydrogels can be easily tuned by changing the chemical composition and ratio of monomers. We further showed that the transparent hydrogels were cytocompatible, and can be used for biomedical applications, including pH-triggered small molecule delivery and hydrogel-based hybrids for detecting doses of radiotherapy.

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 score0.050
Threshold uncertainty score0.665

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 head0.013
GPT teacher head0.241
Teacher spread0.227 · 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

Citations46
Published2018
Admission routes1
Has abstractyes

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