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Record W2405175993 · doi:10.1021/acsmacrolett.5b00362

Injectable Interpenetrating Network Hydrogels via Kinetically Orthogonal Reactive Mixing of Functionalized Polymeric Precursors

2015· article· en· W2405175993 on OpenAlexafffund
Trevor Gilbert, Niels M. B. Smeets, Todd Hoare

Bibliographic record

VenueACS Macro Letters · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsSelf-healing hydrogelsMaterials scienceInterpenetrating polymer networkSwellingPolymerContext (archaeology)KineticsMonomerChemical engineeringDynamic mechanical analysisPolymer chemistryComposite material

Abstract

fetched live from OpenAlex

The enhanced mechanics, unique chemistries, and potential for domain formation in interpenetrating network (IPN) hydrogels have attracted significant interest in the context of biomedical applications. However, conventional IPNs are not directly injectable in a biological context, limiting their potential utility in such applications. Herein, we report a fully injectable and thermoresponsive interpenetrating polymer network formed by simultaneous reactive mixing of hydrazone cross-linked poly( N -isopropylacrylamide) (PNIPAM), and thiosuccinimide cross-linked poly( N -vinylpyrrolidone) (PVP). The resulting IPN gels rapidly (<1 min) after injection without the need for heat, UV irradiation, or small-molecule cross-linkers. The IPNs, cross-linked by kinetically orthogonal mechanisms, showed a significant synergistic enhancement in shear storage modulus compared to the individual component networks as well as distinctive pore morphology, degradation kinetics, and thermal swelling; in particular, significantly lower hysteresis was observed over the thermal phase transition relative to single-network PNIPAM hydrogels.

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 score0.025
Threshold uncertainty score0.930

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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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

Citations40
Published2015
Admission routes2
Has abstractyes

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