Hydrogel microwells with customizable bottom design: A one‐step approach to spheroid formation
Bibliographic record
Abstract
Abstract Conical microwells featuring a variety of bottom‐shape features have received increased recognition because of their enhanced surface characteristics that improve the in vitro‐in vivo correlation in a wide range of biological applications, such as in three‐dimensional cell culture models, specifically cell spheroidal formation, and drug screening. Conventional and microfluidics‐based emerging fabrication techniques for the formation of such conically shaped microwells with uniform spatiotemporal control require complex multistep procedures and costly equipment, or they face challenges in developing slanted V‐shaped well bottoms. Herein, we developed a microfluidics‐based method to produce three‐dimensional microwells with slanted V‐shaped well bottoms by exploring the 3D‐shape tuning ability using a non‐uniform photolithographic technique (NUPL), through a variation in the UV light intensity profile induced by the presence of magnetic nanoparticles, which makes an opaque precursor solution. We also characterize the change in the microwell's bottom profile through variation of UV dose. Finally, the effects of conical shape tuning parameters, that is, the non‐uniformity of UV light intensity and aspect ratio (diameter/height), on the microwell depth and bottom shape is investigated. Using NUPL, we demonstrate the facile and single‐step synthesis of conical microwells with highly slanted sidewalls that are used to create chondrocyte spheroids as a proof of concept.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".