Fabrication and characterization of poly(methylmethacrylate) microfluidic devices bonded using surface modifications and solvents
Bibliographic record
Abstract
The fabrication of polymer microchips allows inexpensive, durable, high-throughput and disposable devices to be made. Poly(methylmethacrylate) (PMMA) microchips have been fabricated by hot embossing microstructures into the substrate followed by bonding a cover plate. Different surface modifications have been examined to enhance substrate and cover plate adhesion, including: air plasma treatment, and both acid catalyzed hydrolysis and aminolysis of the acrylate to yield carboxyl and amine-terminated PMMA surfaces. Unmodified PMMA surfaces were also studied. The substrate and cover plate adhesion strengths were found to increase with the hydrophilicity of the PMMA surface and reached a peak at 600 kN m(-2) for plasma treated PMMA. A solvent assisted system has also been designed to soften less than 50 nm of the surface of PMMA during bonding, while still maintaining microchannel integrity. The extent to which both surface modifications and solvent treatment affected the adhesion of the substrate to the cover plate was examined using nanoindentation methods. The solvent bonding system greatly increased the adhesion strengths for both unmodified and modified PMMA, with a maximum adhesion force of 5500 kN m(-2) achieved for unmodified PMMA substrates. The bond strength decreased with increasing surface hydrophilicity after solvent bonding, a trend that was opposite to what was observed for non-solvent thermal bonding.
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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 | 0.001 |
| 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) | 0.001 | 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".