Fabrication of Robust Hydrogel Coatings on Polydimethylsiloxane Substrates Using Micropillar Anchor Structures with Chemical Surface Modification
Bibliographic record
Abstract
A durable hydrophilic and protein-resistant surface of polydimethylsiloxane (PDMS) based devices is desirable in many biomedical applications such as implantable and microfluidic devices. This paper describes a stable antifouling hydrogel coating on PDMS surfaces. The coating method combines chemical modification and surface microstructure fabrication of PDMS substrates. Three-(trimethoxysilyl)propyl methacrylates containing C═C groups were used to modify PDMS surfaces with micropillar array structures fabricated by a replica molding method. The micropillar structures increase the surface area of PDMS surfaces, which facilitates secure bonding with a hydrogel coating compared to flat PMDS surfaces. The adhesion properties of the hydrogel coating on PDMS substrates were characterized using bending, stretching and water immersion tests. Long-term hydrophilic stability (maintaining a contact angle of 55° for a month) and a low protein adsorption property (35 ng/cm(2) of adsorbed BSA-FITC) of the hydrogel coated PDMS were demonstrated. This coating method is suitable for PDMS modification with most crosslinkable polymers containing C═C groups, which can be useful for improving the anti-biofouling performance of PDMS-based biomedical microdevices.
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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 | 0.001 |
| 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 | 0.001 | 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".