Improved Fully 3D-Printed SIW-Based Sensor for Non-Invasive Glucose Measurement
Bibliographic record
Abstract
This paper presents a fully 3D-printed microfluidic microwave sensor based on substrate-integrated waveguide (SIW) technology for detecting glucose levels in liquid, aimed at monitoring diabetic patients. The sensor’s design features a circular SIW cavity with an integrated sample holder placed in the cavity’s center, maximizing the electric field disturbance from the liquid under test (LUT). Operating in the TM010 mode at a resonance frequency around 5.740 GHz, the sensor detects glucose concentrations by measuring resonance frequency shifts in the S11 reflection response across glucose levels. A conductive sheath partially covers the sample holder to enhance sensitivity and improve the limit of detection (LOD) by increasing field penetration into the LUT. Fabricated using an additive manufacturing electronics (AMEs) method, the sensor is produced in a single pass without post-processing. The experimental validation confirms its high sensitivity of 1.218 MHz/(mg/dL) and a low limit of detection of 0.774 mg/dL in the glucose concentration range (10–200 mg/dL), reflecting typical Type 2 diabetes levels. The key advantages of the sensor include its compactness, enhanced sensitivity and limit of detection, innovative manufacturing, and cost-effectiveness, supporting its potential as a non-invasive glucose monitoring tool. This study establishes a proof of concept for the in vitro measurement of glucose, demonstrating the sensor’s ability to provide accurate and reliable results in a controlled environment.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".