Coupling the COST reference plasma jet to a microfluidic device: a new diagnostic tool for plasma-liquid interactions
Bibliographic record
Abstract
Abstract Plasma-liquid interaction processes are central to plasma applications in medicine, environment, and material processing. However, a standardized platform that allows the study of the production and transport of plasma-generated reactive species from the plasma to the liquid is lacking. We hypothesize that use of microfluidic devices would unlock many possibilities to investigate the transport of reactive species in plasma-treated liquids and, ultimately, to measure the effects of these species on biological systems, as microfluidics has already provided multiple solutions in medical treatment investigations. Our approach combines a capacitively coupled RF plasma jet known as the COST reference plasma jet with simple 3D printed microfluidic devices. This novel pairing is achieved by carefully controlling capillary effects within the microfluidic device at the plasma-liquid interaction zone. The generation and transport of reactive species from the plasma to the liquid inside the microfluidic device are analyzed using a colorimetric hydrogen peroxide concentration assay. A capillary flow model is provided to explain the two main regimes of operations observed in the device and their merits are discussed. Overall, the proposed plasma-microfluidic prototype shows great potential for the fundamental study of plasma-liquid interactions and opens the way to the use of standard microfluidic devices with plasma sources developing a plasma column or a plasma plume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".