One-step in-mould modification of PDMS surfaces and its application in the fabrication of self-driven microfluidic channels
Bibliographic record
Abstract
Poly(dimethylsiloxane) (PDMS) has become the material of choice for fabricating microfluidic channels for lab-on-a-chip applications. Key challenges that limit the use of PDMS in microfluidic applications are its hydrophobic nature, and the difficulty in obtaining stable surface modifications. Although a number of approaches exist to render PDMS hydrophilic, they suffer from reversion to hydrophobicity and, frequently, surface cracking or roughening. In this study, we describe a one-step in-mould method for the chemical modification of PDMS surfaces, and its use to assess the ability of different surfactants to render PDMS surfaces hydrophilic. Thin films of ionic and non-ionic surfactants were patterned into an array format, transferred onto silicone pre-polymer, and subsequently immobilized onto the PDMS surface during vulcanization. The hydrophilicity of the resulting surfaces was assessed by contact angle measurements. The wettability was observed to be dependent on the chemical structure of the surfactants, their concentration and interactions with PDMS. The morphology of modified PDMS surfaces and their change after wetting and drying cycles were visualized using atomic force microscopy. Our results show that while all surfactants tested can render PDMS surfaces hydrophilic through the in-mould modification, only those modified with PEG-PDMS-PEG copolymer surfactants were stable over wetting/dying cycles and heat treatments. Finally, the in-mould functionalization approach was used to fabricate self-driven microfluidic devices that exhibited steady flow rates, which could be tuned by the device geometry. It is anticipated that the in-mould method can be applied to a range of surface modifications for applications in analytical separations, biosensing, cell isolation and small molecule discovery.
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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".