Use of flame activation of surfaces to bond PDMS to variety of substrates for fabrication of multimaterial microchannels
Bibliographic record
Abstract
Abstract Silicones are widely used in industry as sealants, insulation, gaskets, coatings, seals and molds. One variety of silicone, poly dimethyl siloxane (PDMS), has been widely used for rapid prototyping of microfluidic and nanofluidic devices. The bonding of PDMS to other substrates is required to create a sealed microfluidic network. This can be achieved either by using dry bonding (e.g. oxygen plasma treatment) or wet bonding (e.g. microcontact printing) processes. Flame treatment has been used for surface activation of other polymeric materials (e.g. Poloylefin) and wood. This can increase the wettability and improve the adhesion of the coating (e.g. paints, inks and adhesives) with these substrates. In this paper, we present a universal method to bond silicones to other substrates by using flame treatment. We find that the flame treatment could allow one to simply bond PDMS to a variety of substrates including glass, silicon, epoxy (SU8), silicones, polyethylene and even metals such as aluminum. We fully characterize this bonding and find that it is due to the changes in surface property as well as topography on the surface.
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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".