A Digital‐to‐Channel Microfluidic Interface via Inkjet Printing of Silver and UV Curing of Thiol–Enes
Bibliographic record
Abstract
Abstract Microfluidic sample manipulation is a key enabler in modern chemical biology research. Both discrete droplet‐based digital microfluidic (DMF) assays and continuous flow in‐channel assays are well established, each featuring unique advantages from the viewpoint of automation and parallelization. However, there are marked differences in the applicable microfabrication materials and methods, which limit the interfacing of DMF sample preparation with in‐channel separation systems, such as the gold standard microchip electrophoresis. Simultaneously, there is an increasing demand for low‐cost and user‐friendly manufacturing techniques to foster the adaptation of microfluidic technology in routine laboratory analyses. This work demonstrates integration of DMF with in‐channel separation systems using only low‐cost and accessible (non‐cleanroom) manufacturing techniques, i.e., inkjet printing of silver for patterning of the driving electrodes and UV curing of off‐stoichiometric thiol–ene (OSTE) polymers both for dielectric coating of the electrode arrays and replica molding of the microchannel network. As a dielectric, OSTE performs similar to Parylene C (a gold standard dielectric in electrowetting), whereas its tunable surface and bulk properties facilitate straightforward bonding of the microchannel with the dielectric layer. In addition, a new chip design that facilitates efficient droplet transfer from the DMF part to the microchannel inlet solely by electrowetting is showcased.
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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) | 0.000 | 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".