A simple and low-cost approach for irreversible bonding of polymethylmethacrylate and polydimethylsiloxane at room temperature for high-pressure hybrid microfluidics
Bibliographic record
Abstract
Microfluidic devices have been used progressively in biomedical research due to the advantages they offer, such as relatively low-cost, rapid and precise processing, and an ability to support highly automated analyses. Polydimethylsiloxane (PDMS) and polymethylmethacrylate (PMMA) are both biocompatible materials widely used in microfluidics due to their desirable characteristics. It is recognized that combining these two particular materials in a single microfluidic device would enable the development of an increasingly in-demand array of new applications, including those requiring high flow rates and elevated pressures. Whereas complicated and time-consuming efforts have been reported for bonding these two materials, the robust adhesion of PDMS and PMMA has not yet been accomplished, and remains a challenge. In this study, a new, simple, efficient, and low-cost method has been developed to mediate a strong bond between PMMA and PDMS layers at room temperature in less than 5 min using biocompatible adhesive tape and oxygen plasma treatment. The PDMS-PMMA bond was hydrolytically stable, and could tolerate a high influx of fluid without any leakage. This study addresses the limitations of existing approaches to bond these materials, and will enable the development of highly sought high-pressure and high-throughput biomedical applications.
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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 | 0.001 |
| Open science | 0.000 | 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".