Facile engineering and interfacing of styrenic block copolymers devices for low‐cost, multipurpose microfluidic applications
Bibliographic record
Abstract
Abstract Soft thermoplastic elastomers (sTPE) and specifically styrenic block copolymers (SBC) are making rapid progress in the prototyping and mass production of microfluidic chips. However, these new materials lack guidelines and protocols for chips fabrication, curbing their widespread applications compared to polydimethylsiloxane. In this work, the prototyping potential of a commercially available SBC material, Flexdym, for continuous flow applications is explored. This SBC material exhibits both reversible and permanent self‐adhesion depending on the time and bonding temperature, allowing for rapid and adaptive chip fabrication. Replicates are embossed in 2 min, assembled and sealed in 10 min. Under continuous flow, stud interfaces fabricated with this method can withstand 1 bar with reversible bonding and up to 3 bar after permanent bonding. The integration of an acoustic transducer in an SBC chip to induce acoustic streaming enables rapid mixing and local enrichment of polystyrene microparticles up to 8× the injected concentration. The reversible bonding feature of SBC chips allows to culture endothelial cells in open channels and then close and perfuse through channel to stain the cell. Our finding suggests that TPE‐based materials offer numerous possibilities for prototyping microfluidic chips for analytical and biomedical applications when working with continuous flow at high pressure is required.
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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) | 0.001 | 0.000 |
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".