A Versatile Bonding Method for PDMS and SU-8 and Its Application towards a Multifunctional Microfluidic Device
Bibliographic record
Abstract
This paper reports a versatile and irreversible bonding method for poly(dimethylsiloxane) (PDMS) and SU-8. The method is based on epoxide opening and dehydration reactions between surface-modified PDMS and SU-8. A PDMS replica is first activated via the low-cost lab equipment, i.e., the oxygen plasma cleaner or the corona treater. Then both SU-8 and plasma-treated PDMS samples are functionalized using hydrolyzed (3-aminopropyl)triethoxysilane (APTES). Ultimately, the samples are simply brought into contact and heated to enable covalent bonding. The molecular coupling and chemical reactions behind the bonding occurring at the surfaces were characterized by water contact angle measurement and X-ray photoelectron spectroscopy (XPS) analysis. The reliability of bonded PDMS-SU-8 samples was examined by using tensile strength and leakage tests, which revealed a bonding strength of over 1.4 MPa. The presented bonding method was also applied to create a metal-SU-8-PDMS hybrid device, which integrated SU-8 microfluidic structures and microelectrodes. This hybrid system was used for the effective trapping of microparticles on-chip, and the selective releasing and identification of predefined trapped microparticles. The hybrid fabrication approach presented here, based on the PDMS-SU-8 bonding, enables multifunctional integration in complex 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) | 0.001 | 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 | 0.001 | 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".