Editors' Choice—Development of Screen-Printed Flexible Multi-Level Microfluidic Devices with Integrated Conductive Nanocomposite Polymer Electrodes on Textiles
Bibliographic record
Abstract
We present a flexible plastisol-based microfluidic process integrated with conductive nanoparticle composite polymer (C-NCP) electrodes for flexible active microfluidic devices on textile substrates. First, we characterize the stretchability and flexibility of both plastisol films and microfluidic channels. A maximum elongation increase of 37.5% is observed for plastisol films, and a maximum elongation increase of 17.5% is observed for microfluidic channels. We also demonstrate multiple levels of microfluidic channels. Using a new integrated fabrication process, a device that measures the conductivity of fluid between two electrodes is fabricated on a textile and successfully demonstrated and characterized. For this new fabrication process, flexible screen-printable Ag C-NCP, with resistivity of 2.12 × 10 −6 Ω·m, is used for device electrodes. Commercial Ag epoxy, with resistivity of 1 × 10 −6 ∼ 10 × 10 −6 Ω·m, is also used to fabricate a second set of electrodes for comparison. The device is tested with saline solutions at different salt concentrations, and the current through each saline solution is measured at different voltages using both Ag C-NCP electrodes and Ag epoxy electrodes. The current increases linearly for a given voltage as the salt concentration increases, for devices with both Ag C-NCP electrodes and Ag epoxy electrodes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".