Thermoplastic elastomers for microfluidics: Towards a high-throughput fabrication method of multilayered microfluidic devices
Bibliographic record
Abstract
Multilayer soft lithography of polydimethylsiloxane (PDMS) is a well-known method for the fabrication of complex fluidic functions. With advantages and drawbacks, this technique allows fabrication of valves, pumps and micro-mixers. However, the process is inadequate for industrial applications. Here, we report a rapid prototyping technique for the fabrication of multilayer microfluidic devices, using a different and promising class of polymers. Using styrenic thermoplastic elastomers (TPE), we demonstrate a rapid technique for the fabrication and assembly of pneumatically driven valves in a multilayer microfluidic device made completely from thermoplastics. This material solution is transparent, biocompatible and as flexible as PDMS, and has high throughput thermoforming processing characteristics. We established a proof of principle for valving and mixing with three different grades of TPE using an SU-8 master mold. Specific viscoelastic properties of each grade allow us to report enhanced bonding capabilities from room temperature bonding to free pressure thermally assisted bonding. In terms of microfabrication, beyond classically embossing means, we demonstrate a high-throughput thermoforming method, where TPE molding experiments have been carried out without applied pressure and vacuum assistance within an overall cycle time of 180 s. The quality of the obtained thermoplastic systems show robust behavior and an opening/closing frequency of 5 Hz.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".