Materials selection and manufacturing of thermoplastic elastomer microfluidics
Bibliographic record
Abstract
In this paper we outline some of the manufacturing advantages and challenges of working with thermoplastic elastomers as an alternative to traditional polydimethysiloxane (PDMS) for flexible and reversibly bonded microfluidic systems. Unlike PDMS, thermoplastic elastomers can be processed with many industrial polymer manufacturing technologies such as extrusion, injection molding, hot embossing and others, potentially permitting much more scalable production and cheaper costs per part. Unlike a more rigid thermoplastic, these thermoplastic elastomers are typically much easier to bond, either reversibly or permanently due to their inherent compliance and subsequent low pressures necessary to seal channels and reservoirs. Unlike PDMS however, where one material (Sylgard 184) dominates the literature, there have not been many in depth investigations into thermoplastic elastomers and their relative performance and applicability to microfabrication. We show a comparison between several categories of thermoplastic elastomer to demonstrate what issues may be encountered and to demonstrate that even for labs with minimal equipment, academic prototyping with these materials is not necessarily any more challenging than PDMS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".