Modeling and Numerical Studies of Three‐Dimensional Conically Shaped Microwells Using Non‐Uniform Photolithography
Bibliographic record
Abstract
Abstract Conical microwells have found a wide range of applications such as in cancer diagnostics, cell spheroid formation, and three‐dimensional oncology models. Numerous microengineered fabrication techniques have been developed for the formation of such conically shaped microwells. Among many, non‐uniform photolithography (NUPL) in a PDMS microfluidic channel with a glass substrate, can be used to create polymeric microwells with tapered‐bottom and parabolic curvatures. Here, the parabolic well formation of microwells is numerically investigated using NUPL to better understand its key mechanisms. Temporal and spatial free‐radical diffusion are incorporated into the modeling of photopolymerization. In addition, the 3D‐shape tuning ability of NUPL is modeled for the synthesis of microwells through a variation of UV light intensity induced by the presence of opaque materials. The model and simulation results predict the time‐evolution of microwell formation with parabolic well features. The effects of the conical shape‐tuning parameters are numerically determined, i.e., the aspect ratio of the well diameter to channel height and the non‐uniformity of UV light intensity on the microwell depth and parabolic curvature. Numerical work provides meaningful insights into NUPL to optimize and design polymeric microwells with shape features tuned to their application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".