Mass fabrication of PDMS microfluidic devices by injection molding and applications in sensitive 3D spheroid and explant culture
Bibliographic record
Abstract
This study investigates the use of industrial-grade polydimethylsiloxane (PDMS) for mass production of microfluidic chips through Liquid Silicone Rubber Injection Molding (LSR-IM) technologies. Injection molding of PDMS is ubiquitous in the automotive and aerospace industries. Yet, it is not currently applied to large-scale production of microfluidic devices. Here, we manufactured PDMS microfluidics devices using industrial IM systems and measured the values of several physicochemical properties of PDMS important in microfluidics. For the two grades of injection-moldable PDMS studied, properties were found close to those of the standard Sylgard 184 PDMS, except Young's modulus, which varied from 66% to 250% of the Sylgard 184 value and the absorption of small molecules, which was lower or equal to that within Sylgard 184. Yet, the main benefits of LSR-IM production lie in its systematic process, resulting in an increase in reproducibility of both PDMS surface and bulk properties by decreasing the variance within and between production batches of several variables: Young's modulus (30-fold), oxygen permeation (10-fold), mean fluorescence intensity of Nile Red (5-fold). Emphasis on biocompatibility of LSR-IM was evaluated by culturing complex and sensitive 3D biology models (tumor spheroids and explants) in injection-molded PDMS devices. We observed no significant differences in cell proliferation relative to samples cultured on conventional Sylgard 184 PDMS devices. Overall, these findings open a direct path from benchtop prototyping to industrialization of PDMS-based microfluidic devices for applications in healthcare.
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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".