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Record W2518430156 · doi:10.1021/acs.macromol.6b01411

Enhancing and Tuning the Response of Environmentally Sensitive Hydrogels With Embedded and Interconnected Pore Networks

2016· article· en· W2518430156 on OpenAlexafffund
Teodora Gancheva, Nick Virgilio

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsMaterials sciencePorositySwellingChemical engineeringPolymerFabricationAnnealing (glass)Lower critical solution temperatureMonomerPoly(N-isopropylacrylamide)Polymer chemistryMicrostructureComposite materialCopolymer

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Porous and temperature-sensitive poly( N -isopropylacrylamide) (PNIPAam) hydrogels with tunable and enhanced response properties were prepared by using porous poly(ε-caprolactone) (PCL) molds. The molds were obtained from melt-processed cocontinuous polymer blends of ethylene propylene diene monomer (EPDM) and PCL. Quiescent annealing of the blends resulted in microstructure coarsening, and subsequent extraction of the EPDM phase yielded the molds. Ultimately, it allowed control over the average gel pore size from 20 to 300 μm. The gelling solution was injected within the molds, which were subsequently extracted, yielding hydrogels with fully interconnected pores. The porous gels display enhanced thermoresponsive properties in water: tunable, fully reversible and significantly faster swelling and deswelling responses following a temperature change across the PNIPAam lower critical solution temperature, as compared to nonporous gels. The fabrication process is compatible with a broad choice of gel chemistries, and allows the fabrication of complex 3D shapes of various sizes.

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 score0.019
Threshold uncertainty score0.333

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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

Citations28
Published2016
Admission routes2
Has abstractyes

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