Chloroform compatible, thiol-ene based replica molded micro chemical devices as an alternative to glass microfluidic chips
Bibliographic record
Abstract
Polymeric microfluidic chips offer a number of benefits compared to their glass equivalents, including lower material costs and ease and flexibility of fabrication. However, the main drawback of polymeric materials is often their limited resistance to (organic) solvents. Previously, thiol-ene materials were shown to be more solvent resistant than most other commonly used polymers; however, they still fall short in "harsh" chemical environments, such as when chlorinated solvents are present. Here, we show that a simple yet effective treatment of thiol-ene materials results in exceptional solvent compatibility, even for very challenging chemical environments. Our approach, based on a temperature treatment, results in a 50-fold increase in the chloroform compatibility of thiol-enes (in terms of longevity). We show that prolonged heat exposure allows for the operation of the microfluidic chips in chloroform for several days with no discernable deformation or solvent-induced swelling. The method is applicable to many different thiol-ene-based materials, including commercially available formulations, and also when using other commonly considered "harsh" solvents. To demonstrate the utility of the solvent compatible thiol-enes for applications where chloroform is frequently employed, we show the continuous and uniform production of polymeric microspheres for drug delivery purposes over a period of 8 hours. The material thus holds great promise as an alternative choice for microfluidic applications requiring harsh chemical environments, a domain so far mainly restricted to glass chips.
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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 | 0.000 | 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".