Characterization of a 3D Printed Mold for a Cell Culturing Microfluidic Device
Bibliographic record
Abstract
The application of channels with multiple thicknesses is a major area of interest within fields of tissue engineering and microfluidics. 3D printing facilitates cost-effective fabrication of PDMS-based microfluidics using 3D printed molds. In this work, the limitations and the accuracy of using 3D printed templates for microfluidic applications with commercial SLA and FDM 3D printers, are presented. A 3D microfluidic cell culturing device that contains multiple thicknesses is proposed and the accuracy of printed parts in three dimensions is demonstrated. The reusable molds can be printed in less than two hours, with the average cost of 0.35 US$, which lead to fast and cheap fabrication compared to conventional fabrication methods which require clean room facilities. The surface roughness of these 3D printed molds are 0.25 and 1.12 for flexible and clear resins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".