Surface Micromachined PDMS Microchannels
Bibliographic record
Abstract
A new surface micromachining technique for Poly dimethylsiloxane (PDMS) microchannels has been developed to address the leakage problem affecting traditional PDMS microchannel process with embedded tall structures or sudden topographical transitions. Bulk micromachined PDMS cannot be conformally bonded with the surfaces having tall topological features such as thick film electrodes, porous reactor beds and other structural features. Surface micromachining technique with PDMS as structural material and photoresist as a sacrificial material allows the creation of PDMS microchannels on substrates with significant topography. Adhesion of the structural layer with the substrate was characterized for different prepolymer ratios using standard tensile test and 1:3 (Curing agent: base) combination was found to be the best with maximum adhesion strength of 7.5 MPa. The effectiveness of this technique is demonstrated by the fabrication of microchannels with embedded 6μm thick Silver electrodes. The microchannels were leak proof and conformal contact between the PDMS and electrode was confirmed through SEM. The release time for microchannels was reduced to 1 min irrespective of the length of the microchannel. The extension of this technique for fabrication of multi layered microchannel structure was demonstrated through a microfluidic valve. The valve closure occurred at 6.37 kPa.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".