Inexpensive and rapid fabrication of PDMS microfluidic devices for biological testing applications using low cost commercially available 3D printers
Bibliographic record
Abstract
Abstract Polydimethylsiloxane (PDMS) elastomers have been extensively used in the development of microfluidic devices, capable of miniaturizing biomolecular and cellular assays to the microlitre and nanolitre range, thereby increasing the throughput of experimentation. PDMS has been widely used due to its optical clarity and biocompatibility, among other desirable physical and chemical properties. Despite the widespread use of PDMS in microfluidic devices, the fabrication process typically via soft lithography technology requires specialized facilities, instruments, and materials only available in a limited number of laboratories. To expand microfluidic research capabilities to a greater scientific population, we developed and characterized a simple and robust method of fabricating relatively inexpensive PDMS microfluidic devices using readily available reagents and commercially available three-dimensional (3D) printers. The moulds produced from the 3D printers resolve designed microfluidic channel features accurately with high resolution (>100 µm). The critical physical and chemical post-processing modifications we outline here are required to generate functional and optically clear microfluidic devices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".