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Record W2768152303 · doi:10.1039/c7sm01361b

Microfluidic production of degradable thermoresponsive poly(<i>N</i>-isopropylacrylamide)-based microgels

2017· article· en· W2768152303 on OpenAlexafffund
Daryl Sivakumaran, Eva Mueller, Todd Hoare

Bibliographic record

VenueSoft Matter · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDispersityPoly(N-isopropylacrylamide)HydrazideMicrofluidicsPolymerMaterials scienceLower critical solution temperaturePolymer chemistryChemical engineeringMicrofluidic chipNanotechnologyChemistryCopolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Highly monodisperse and hydrolytically degradable thermoresponsive microgels on the tens-to-hundreds of micron size scale have been fabricated based on simultaneous on-chip mixing and emulsification of aldehyde and hydrazide-functionalized poly(N-isopropylacrylamide) precursor polymers. The microfluidic chip can run for extended periods without upstream gelation and can produce monodisperse (<10% particle size variability) microgels on the size range of ∼30-90 μm, with size tunable according to the flow rate of the oil continuous phase. Fluorescence analysis indicates a uniform distribution of each reactive pre-polymer inside the microgels while micromechanical testing suggests that smaller microfluidic-produced microgels exhibit significantly higher compressive moduli compared to bulk hydrogels of the same composition, an effect we attribute to improved mixing (and thus crosslinking) of the precursor polymer solutions within the microfluidic device. The microgels retain the reversible volume phase transition behavior of conventional microgels but can be hydrolytically degraded back into their oligomeric precursor polymer fragments, offering potential for microgel clearance following use in vivo.

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 score0.028
Threshold uncertainty score0.677

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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