Surface Modification of PDMS-Based Microfluidic Devices with Collagen Using Polydopamine as a Spacer to Enhance Primary Human Bronchial Epithelial Cell Adhesion
Bibliographic record
Abstract
Polydimethylsiloxane (PDMS) is a silicone-based synthetic material used in various biomedical applications due to its properties, including transparency, flexibility, permeability to gases, and ease of use. Though PDMS facilitates and assists the fabrication of complicated geometries at micro- and nano-scales, it does not optimally interact with cells for adherence and proliferation. Various strategies have been proposed to render PDMS to enhance cell attachment. The majority of these surface modification techniques have been offered for a static cell culture system. However, dynamic cell culture systems such as organ-on-a-chip devices are demanding platforms that recapitulate a living tissue microenvironment's complexity. In organ-on-a-chip platforms, PDMS surfaces are usually coated by extracellular matrix (ECM) proteins, which occur as a result of a physical and weak bonding between PDMS and ECM proteins, and this binding can be degraded when it is exposed to shear stresses. This work reports static and dynamic coating methods to covalently bind collagen within a PDMS-based microfluidic device using polydopamine (PDA). These coating methods were evaluated using water contact angle measurement and atomic force microscopy (AFM) to optimize coating conditions. The biocompatibility of collagen-coated PDMS devices was assessed by culturing primary human bronchial epithelial cells (HBECs) in microfluidic devices. It was shown that both PDA coating methods could be used to bind collagen, thereby improving cell adhesion (approximately three times higher) without showing any discernible difference in cell attachment between these two methods. These results suggested that such a surface modification can help coat extracellular matrix protein onto PDMS-based 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 | 0.000 | 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 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".