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Record W2099386221 · doi:10.1039/c3ra43841d

Thiol-responsive hydrogel scaffolds for rapid change in thermoresponsiveness

2013· article· en· W2099386221 on OpenAlexafffund
Samuel Aleksanian, Yifen Wen, Nicky Chan, Jung Kwon Oh

Bibliographic record

VenueRSC Advances · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsConcordia University
FundersCanada Research ChairsRoyal SocietyFonds Québécois de la Recherche sur la Nature et les TechnologiesRoyal Society of ChemistryCentre québécois sur les matériaux fonctionnels
KeywordsSelf-healing hydrogelsAtom-transfer radical-polymerizationThiolThermoresponsive polymers in chromatographyPolymerizationKineticsChemistryPolymer chemistryMaterials scienceChemical engineeringPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

A facile strategy to fabricate thiol-responsive thermoresponsive hydrogels able to rapidly change their volume in response to temperature is reported. The strategy utilizes crosslinking atom transfer radical polymerization to synthesize well-defined hydrogels of thermoresponsive oligo(ethylene oxide)-based polymethacrylates with a uniform network crosslinked with dynamic disulfides. Thiol-responsive cleavage of disulfide linkages to the corresponding pendant thiols allows for the generation of hydrophilic dangling chains in the hydrogels as well as the increase in hydrophilicity of the hydrogel network. The degraded hydrogels exhibit a rapid change of thermoresponsiveness (deswelling kinetics) with a slight sacrifice in mechanical properties. Evaluating the hydrogels from a biomedical perspective, rapid thermoresponsive hydrogels are non-cytotoxic and exhibit enhanced release of encapsulated model drugs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2013
Admission routes2
Has abstractyes

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