Noncontact and Nonintrusive Microwave-Microfluidic Flow Sensor for Energy and Biomedical Engineering
Bibliographic record
Abstract
Abstract A novel flow sensor is presented to measure the flow rate within microchannels in a real-time, noncontact and nonintrusive manner. The microfluidic device is made of a fluidic microchannel sealed with a thin polymer layer interfacing the fluidics and microwave electronics. Deformation of the thin circular membrane alters the permittivity and conductivity over the sensitive zone of the microwave resonator device and enables high-resolution detection of flow rate in microfluidic channels using non-contact microwave as a standalone system. The flow sensor has the linear response in the range of 0–150 µl/min for the optimal sensor performance. The highest sensitivity is detected to be 0.5 µl/min for the membrane with the diameter of 3 mm and the thickness of 100 µm. The sensor is reproducible with the error of 0.1% for the flow rate of 10 µl/min. Furthermore, the sensor functioned very stable for 20 hrs performance within the cell culture incubator in 37 °C and 5% CO 2 environment for detecting the flow rate of the culture medium. This sensor does not need any contact with the liquid and is highly compatible with several applications in energy and biomedical engineering, and particularly for microfluidic-based lab-on-chips, micro-bioreactors and organ-on-chips platforms.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".