Novel Substrate Integrated Waveguide Architectures for Microfluidic Biosensing and Environmental Detection
Bibliographic record
Abstract
This paper presents two novel substrate integrated waveguide (SIW) sensing-elements for microfluidic biosensing and environmental detection. The first proposed structure is a SIW interferometer for biological liquids characterization. The operation principle of the latter is based on the variation of the effective dielectric constant of a sensitive branch due to the introduction of aqueous solutions into a sensitive region of a total volume of ~1 µL. To achieve a destructive interference, the signal division and combination have been carried out using microstrip Wilkinson power dividers. The sensitive characteristics of the device have been tested by measuring two buffer solutions; Phosphate-buffered saline (PBS) and Roswell Park Memorial Institute medium (RPMI). In regards to the second proposed device, it consists of an H-plane SIW-based horn antenna demonstrated for relative humidity (RH) sensing. The sensitive characteristics of the proposed antenna without the use of a sensitive layer were tested in the range of 25%–75% RH. The combination of antenna and sensor functions in a single substrate integrated device offers multiple advantages and enables the development of simple, compact and cost-effective sensors. Passive RFID sensing is also possible with this technique without resorting to the use of additional sensors.
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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) | 0.000 | 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 | 0.000 | 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".