A novel metal-protected plasma treatment for the robust bonding of polydimethylsiloxane
Bibliographic record
Abstract
We describe a method for the irreversible bonding of PDMS-based microfluidic components by exploiting the first reported "shelfable" plasma treatment of PDMS. Simultaneous plasma activation and protection of PDMS surfaces are achieved via RF magnetron sputtering of thin aluminium films in the presence of an argon plasma. In this process, Ar plasma exposure generates a hydrophilic, silanol-enriched polymer surface amenable to irreversible bonding to glass, PDMS or silicon substrates, while the aluminium film functions as a capping layer to preserve the surface functionality over several weeks of storage in ambient conditions. Prior to bonding, this protective aluminium layer is removed by immersion in an aqueous etchant, exposing the adhesive surface. Employing this technology, PDMS-glass and PDMS-PDMS microfluidic devices were fabricated and the adhesive strength was quantified by tensile and leakage testing. Bonding success rates in excess of 80% were demonstrated for both PDMS-glass and PDMS-PDMS assemblies sealed 24 h and 7 days following initial polymer surface activation. PDMS-glass microdevices performed optimally, displaying maximum adhesive strengths on the order of 5 MPa and burst flow rates of approximately 1 mL min(-1) (channel dimensions: l = 25 mm; w = 300 microm; h = 20 microm). These data demonstrate a significant improvement in performance over previously reported bonding technologies, resulting in the production of more robust, longer-lasting microfluidic systems that can withstand higher pressures and flow rates.
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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 | 0.001 | 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".