Efficient production of cell-encapsulated microgels using flicking technique
Bibliographic record
Abstract
In this study, a simple flicking-base method was developed for the mass production of uniform alginate microgels . A comprehensive investigation was conducted to determine the relationship between the microencapsulation parameters and the size as well as the sphericity of the microgels. Fractional factorial design (FFD) was employed to determine the main factors affecting microgel size, identifying flow rate, alginate concentration, and motor speed as the most influential. Furthermore, the simultaneous effect of these critical factors on microgel size and sphericity was modeled using central composite design (CCD). The resulting quadratic empirical models offered insights into the jet break process in flicking-based microencapsulation and enabled the selection of factors and their levels to generate appropriately sized and shaped alginate microgels. Spherical microgels with a minimum size of 528 ± 20 μm were obtained at a flow rate of 5.8 ml/h, alginate concentration of 2 %, and motor speed of 17.1 Hz. Human mesenchymal stem cells were successfully encapsulated in the microgels with high cell viability exceeding 90 %. This study highlights the flicking method as a promising technique for efficiently producing uniform cell-encapsulated alginate microgels. The simplicity and cost-effectiveness of this method make it an available option for practical laboratory research , enabling the efficient preparation of microgels with adjustable size and shape. Furthermore, this microencapsulation approach has potential applications in tissue engineering , pharmaceutical disease models, cell-based transplantation, and regenerative medicine .
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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".