Exploring compartmentalized jet polymerization for novel rod-shaped microgels and their potential in tissue engineering applications
Bibliographic record
Abstract
Anisometric rod-shaped microgels are promising building blocks for tissue engineering, offering injectability, porosity, macroscopic anisotropy, and biochemical functionality-key features for directing cell adhesion, growth, alignment, and interaction. The continuous production of thin or highly porous elongated microgels is therefore desirable, preferably offering control over their stiffness, size, and aspect ratio. We present advancements in compartmentalized jet polymerization, a microfluidic technique that generates microgels that are ten times narrower than the channel width by forming a polymer jet and crosslinking alternating segments with a pulsed laser. Originally limited to diameters of ∼8 μm, we have now refined the method to produce microgels as small as ∼3 μm. Additionally, we developed ultra-soft and ultra-porous microgels that swell to diameters of 50-120 μm with pore sizes in the range 2-5 μm. While the thin soft microgels can be employed in our Anisogel technology to combine injectability with magnetic alignment, the ultra-porous microgels would increase diffusion in our microporous annealed particle (MAP) scaffolds made from rod-shaped microgels. This paper focuses on the continuous production and characterization of rod microgels with properties that cannot be achieve with other methods. Furthermore, we report initial results of the microgels' potential and challenges to be used inside an Anisogel, which was so far only possible with stiffer magneto-responsive microgels produced by an in-mold polymerization batch process, and to form MAPs by cell-induced assembly of the ultra-porous rods. Further studies will be required to fully exploit the potential of these unique microgels for tissue engineering applications.
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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 | 0.001 |
| 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".