Sulfonated-polydimethylsiloxane (PDMS) microdevices with enhanced electroosmotic pumping and stability
Bibliographic record
Abstract
Polydimethylsiloxane (PDMS) microfluidic devices offer several advantages in terms of cost and ease of fabrication compared with those fabricated from both glass and silicon materials. PDMS, however, has some potential disadvantages compared with other materials including high hydrophobicity, which makes filling the micron-sized channels difficult, and minimal surface charge resulting in reduced electroosmotic flow (EOF). Here, we describe the oxidation of the PDMS surface to form silanol groups using both air plasma and a discharge from a Tesla coil, and subsequent modification to form sulfonated-PDMS surfaces. The flow performance of freshly prepared and aged sulfonated-PDMS chips was determined at pH 5 and compared with those of unmodified and oxidized PDMS chips. The electroosmotic mobility (µeo) for a sulfonated-PDMS microdevice was determined at various pH values (pH 3~8) and compared with that for an oxidized PDMS chip. The lower pKa of a sulfonic acid modified surface compared with a silanol modified surface generated a stronger EOF over the entire pH range studied. Chemical force titrations were used to characterize the changes in functional groups present on the surface of freshly prepared and aged sulfonated-PDMS surfaces. These experiments show that the sulfonated-PDMS is a superior material for use in microfluidic applications because (i) it supports EOF over a much wider range of pH than similar polymer materials and (ii) is less susceptible to degradation of its EOF rate owing to air aging effects and surface reorganization.Key words: polydimethylsiloxane (PDMS), sulfonated surface modification, electroosmotic flow (EOF), aging effect, chemical force titrations.
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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 | 0.000 |
| 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".