Modular microporous hydrogels formed from microgel beads with orthogonal thermo-chemical responsivity: Microfluidic fabrication and characterization
Bibliographic record
Abstract
Despite the significant advances in designing injectable bulk hydrogels, the inability to control the pore interconnectivity and decoupling it from the matrix stiffness has tremendously limited the applicability of stiff, flowable hydrogels for 3D cellular engineering, e.g., in hard tissue engineering. To overcome this persistent challenge, here, we introduce a universal method to convert thermosensitive macromolecules with chemically-crosslinkable moieties into annealable building blocks, forming 3D microporous beaded scaffolds in a bottom-up approach. In particular, we show gelatin methacryloyl (GelMA), a widely used biomaterial in tissue engineering, may be converted into physically-crosslinked microbeads using a facile microfluidic approach, followed by flow of the microbead suspension and chemical crosslinking in situ to fabricate microporous beaded GelMA (B-GelMA) scaffolds with interconnected pores, promoting cell functionality and rapid (within minutes) 3D seeding in stiff scaffolds, which are otherwise impossible in the bulk gel counterparts. This novel approach may set the stage for the next generation modular hydrogels with orthogonal porosity and stiffness made up of a broad range of natural and synthetic biomaterials. •This method combines well-known flow focusing microfluidic devices with facile post-processing steps to fabricate microporous scaffolds.•Temperature-driven physical crosslinking of the microbeads enables the facile purification of gel building blocks without further chemical reactions.•This method provides a simple approach to fabricate microporous scaffolds, which overcomes some of the challenges of newly emerging beaded scaffolds, including oxygen-mediated impaired crosslinking.
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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) | 0.000 | 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".