Synthesis of Monodisperse, Covalently Cross‐Linked, Degradable “Smart” Microgels Using Microfluidics
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".