Thiol–Ene Based Polymers as Versatile Materials for Microfluidic Devices for Life Sciences Applications
Bibliographic record
Abstract
While there is a steady growth in the number of microfluidics applications, the search for an optimal material that delivers the diverse characteristics needed for the numerous tasks is still nowhere close to being settled. Often overlooked and still underrepresented, the thiol-ene family of polymer materials has an enormous potential for applications in organs-on-a-chip, droplet productions, microanalytics, and point of care testing. In this review, the main characteristics of the thiol-ene materials are given, and advantages and drawbacks with respect to their potential in microfluidic chip fabrication are critically assessed. Select applications, which exploit the versatility of the thiol-ene polymers, are presented and discussed. It is concluded that, in particular, the rapid prototyping possibility combined with the material's resulting mechanical strength, solvent resistance, and biocompatibility, as well as the inherently easy surface functionalization, are strong factors to make thiol-ene polymers strong contenders for promising future materials for many biological, clinical, and technical lab-on-a-chip applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".