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Record W2145666765 · doi:10.1002/smll.201102113

Synthesis of Monodisperse, Covalently Cross‐Linked, Degradable “Smart” Microgels Using Microfluidics

2012· article· en· W2145666765 on OpenAlexaff
Leah R. B. Kesselman, Siawash Shinwary, P. Ravi Selvaganapathy, Todd Hoare

Bibliographic record

VenueSmall · 2012
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDispersityMicrofluidicsCovalent bondPolymerMaterials scienceDynamic covalent chemistryPolymerizationNanotechnologyChemical engineeringSolventHydrazidePolymer chemistryChemistryOrganic chemistryMoleculeSupramolecular chemistry

Abstract

fetched live from OpenAlex

The development of a robust method for the synthesis of highly monodisperse microgels cross-linked with degradable covalent bonds offers the potential for fabricating microgels with the highly controllable porosities, cell interactions, and degradation half-lives required for biomedical applications. A microfluidic chip is designed that enables the on-chip mixing and emulsification of two reactive polymer solutions (hydrazide and aldehyde-functionalized carbohydrates) to form monodisperse, hydrazone cross-linked microgels in the size range of ≈40-100 μm. The device can be run continuously for at least 30 h without a significant drift in particle size. The resulting microgels have a homogeneous bulk composition and can swell and deswell as the solvent conditions change in predictable ways based on the chemistry of the reactive polymers used, thereby enabling improved control over both the chemistry and morphology of the resulting microgels relative to other reported approaches. The in situ gelation chemistry used facilitates rapid microgel formation within the droplets without requiring the use of UV light or heating to initiate polymerization, thus making this approach of particular potential utility in cell encapsulation or drug delivery (as demonstrated).

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 categoriesMeta-epidemiology (narrow)
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 score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.258
Teacher spread0.226 · 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.

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

Citations53
Published2012
Admission routes1
Has abstractyes

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