Fully 3D-Printed Microfluidic Sensor Using Substrate Integrated Waveguide Technology for Liquid Permittivity Characterization
Bibliographic record
Abstract
This paper proposes a fully 3D-printed electromagnetic (EM) microfluidic sensor using rectangular substrate integrated waveguide (SIW) for liquid complex permittivity characterization. The proposed microfluidic sensor is fabricated with a novel additive manufacturing process in which dielectric and conductive inks are simultaneously 3D-printed, allowing high print quality, rapid prototyping, and arbitrary geometry. The fabrication process removes the need for post-printing sintering and cleaning steps that require harmful chemicals. The sensor structure is composed of upper and lower metal plates and a series of cylindrical metal side vias. Since the electric field is high at the center of the SIW cavity in$\textit {TE}_{101}$mode, a cylindrical dielectric sample container in the form of a microwell is built into the center of the SIW cavity to maximize the perturbation of the liquid under test (LUT). The application of LUT samples to the microwell results in a change in the resonance frequency and peak attenuation from which the LUT sample is characterized. Ethanol-water mixtures are used as LUTs for validation. The proposed sensor has been verified numerically and experimentally, reducing the resonant frequency from 3.750 GHz to 3.862 GHz by increasing the ethanol volume fraction from 0% to 100%. The sensor showed good sensitivity of 0.345% and a stable frequency change was observed over five measurement repetitions. To the best of our knowledge, this article presents the first fully 3D-printed SIW microfluidic sensor and demonstrates its ability to detect and characterize the liquid complex permittivity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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".