Soft Thermoplastic Elastomer for Easy and Rapid Spin‐Coating Fabrication of Microfluidic Devices with High Hydrophilization and Bonding Performances
Bibliographic record
Abstract
Abstract Spin‐coating process of a commercially available soft thermoplastic elastomer (sTPE) is demonstrated for the realization of microfluidic prototypes. It is a reliable, wrinkling‐free, and high‐yield holding and transfer methodology for single and multilayered devices fabrication. Complex microfluidic systems including embedded microlenses array, dilutor‐like chip for stable capillary filling, and pneumatically drive valve confirms the performances and quality of the process. The dissolved sTPE, a soft styrenic‐block polymer‐based material, is flexible in its raw state as poly(dimethylsiloxane) and thermoformable as classical thermoplastics. Depending on the solvent evaporation rate, two stable morphologies of the block nanodomains are revealed: either a nanocylinder or hexagonal nanospheres pattern. Both are evenly distributed over the whole surface and account for a uniform and convenient surface at a “microfluidic scale device.” Plasma hydrophilization allows a stable and hydrophilic surface (≈20° after 7 d). The biocompatibility is assessed through the cell culture proliferation assay of progenitor endothelial cells. This sTPE solution displays fast and reliable fabrication and assembly performances, it can achieve strong and stable hydrophilicity within a low level of initial equipment investment.
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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) | 0.000 | 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) | 0.003 | 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".