PDMS Organ-On-Chip Design and Fabrication: Strategies for Improving Fluidic Integration and Chip Robustness of Rapidly Prototyped Microfluidic In Vitro Models
Bibliographic record
Abstract
The PDMS-based microfluidic organ-on-chip platform represents an exciting paradigm that has enjoyed a rapid rise in popularity and adoption. A particularly promising element of this platform is its amenability to rapid manufacturing strategies, which can enable quick adaptations through iterative prototyping. These strategies, however, come with challenges; fluid flow, for example, a core principle of organs-on-chip and the physiology they aim to model, necessitates robust, leak-free channels for potentially long (multi-week) culture durations. In this report, we describe microfluidic chip fabrication methods and strategies that are aimed at overcoming these difficulties; we employ a subset of these strategies to a blood-brain-barrier-on-chip, with others applied to a small-airway-on-chip. Design approaches are detailed with considerations presented for readers. Results pertaining to fabrication parameters we aimed to improve (e.g., the thickness uniformity of molded PDMS), as well as illustrative results pertaining to the establishment of cell cultures using these methods will also be presented.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".