PDMS as a sacrificial substrate for SU-8-based biomedical and microfluidic applications
Bibliographic record
Abstract
We describe a new fabrication process utilizing polydimethylesiloxane (PDMS) as a sacrificial substrate layer for fabricating free-standing SU-8-based biomedical and microfluidic devices. The PDMS-on-glass substrate permits SU-8 photo patterning and layer-to-layer bonding. We have developed a novel PDMS-based process which allows the SU-8 structures to be easily peeled off from the substrate after complete fabrication. As an example, a fully enclosed microfluidic chip has been successfully fabricated utilizing the presented new process. The enclosed microfluidic chip uses adhesive bonding technology and the SU-8 layers from 10 µm to 450 µm thick for fully enclosed microchannels. SU-8 layers as large as the glass substrate are successfully fabricated and peeled off from the PDMS layer as single continuous sheets. The fabrication results are supported by optical microscopy and profilometry. The peel-off force for the 120 µm thick SU-8-based chips is measured using a voice coil actuator (VCA). As an additional benefit the release step leaves the input and the output of the microchannels accessible to the outside world facilitating interconnecting to the external devices.
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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) | 0.001 | 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".