Surface micromachining of polydimethylsiloxane for microfluidics applications
Bibliographic record
Abstract
Polydimethylsiloxane (PDMS) elastomer has emerged as one of the most frequently applied materials in microfluidics. However, precise and large-scale surface micromachining of PDMS remains challenging, limiting applications of PDMS for microfluidic structures with high-resolution features. Herein, surface patterning of PDMS was achieved using a simple yet effective method combining direct photolithography followed by reactive-ion etching (RIE). This method incorporated a unique step of using oxygen plasma to activate PDMS surfaces to a hydrophilic state, thereby enabling improved adhesion of photoresist on top of PDMS surfaces for subsequent photolithography. RIE was applied to transfer patterns from photoresist to underlying PDMS thin films. Systematic experiments were conducted in the present work to characterize PDMS etch rate and etch selectivity of PDMS to photoresist as a function of various RIE parameters, including pressure, RF power, and gas flow rate and composition. We further compared two common RIE systems with and without bias power and employed inductively coupled plasma and capacitively coupled plasma sources, respectively, in terms of their PDMS etching performances. The RIE-based PDMS surface micromachining technique is compatible with conventional Si-based surface and bulk micromachining techniques, thus opening promising opportunities for generating hybrid microfluidic devices with novel functionalities.
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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) | 0.004 | 0.002 |
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".