Liquid polystyrene: a room-temperature photocurable soft lithography compatible pour-and-cure-type polystyrene
Bibliographic record
Abstract
Materials matter in microfluidics. Since the introduction of soft lithography as a prototyping technique and polydimethylsiloxane (PDMS) as material of choice the microfluidics community has settled with using this material almost exclusively. However, for many applications PDMS is not an ideal material given its limited solvent resistance and hydrophobicity which makes it especially disadvantageous for certain cell-based assays. For these applications polystyrene (PS) would be a better choice. PS has been used in biology research and analytics for decades and numerous protocols have been developed and optimized for it. However, PS has not found widespread use in microfluidics mainly because, being a thermoplastic material, it is typically structured using industrial polymer replication techniques. This makes PS unsuitable for prototyping. In this paper, we introduce a new structuring method for PS which is compatible with soft lithography prototyping. We develop a liquid PS prepolymer which we term as "Liquid Polystyrene" (liqPS). liqPS is a viscous free-flowing liquid which can be cured by visible light exposure using soft replication templates, e.g., made from PDMS. Using liqPS prototyping microfluidic systems in PS is as easy as prototyping microfluidic systems in PDMS. We demonstrate that cured liqPS is (chemically and physically) identical to commercial PS. Comparative studies on mouse fibroblasts L929 showed that liqPS cannot be distinguished from commercial PS in such experiments. Researchers can develop and optimize microfluidic structures using liqPS and soft lithography. Once the device is to be commercialized it can be manufactured using scalable industrial polymer replication techniques in PS--the material is the same in both cases. Therefore, liqPS effectively closes the gap between "microfluidic prototyping" and "industrial microfluidics" by providing a common material.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".