Piezoelectric layer embedded-microdiaphragm sensors for the determination of blood viscosity and density
Bibliographic record
Abstract
We introduce a lead zirconate titanate [PZT; Pb(Zr0.52Ti0.48)O3] microdiaphragm resonating sensor packaged in a polydimethylsiloxane chip. The proposed sensor can measure the density and viscosity of a liquid that is within the density and viscosity regime of blood (1.060 × 103 kg/m3, 3–4 cP). To verify the basic characteristics of the sensor, viscous solutions were prepared from glycerol and deionized water with a density in the range from 0.998 to 1.263 × 103 kg/m3 and a viscosity in the range from 1 to 1414 cP. We measured the frequency responses of the sensor before and after injecting the viscosity- and density-controlled liquid under the bottom of the microdiaphragm. The resonant frequencies in the (1,1) and (2,2) modes decreased linearly as a function of the liquid density in the range from 0.998 to 1.146 × 103 kg/m3 with a sensitivity of 28.03 Hz/kg·m−3 and 81.85 Hz/kg·m−3, respectively. The full width at half maximum had a logarithmic relationship with the liquid viscosity in the viscosity range from 1 to 8.4 cP. The quality factor (Q-factor) for the 50% glycerol/water mixture was determined to be greater than 20 for both the (1,1) and the (2,2) modes, indicating that the microdiaphragm resonating sensor is suitable for measuring the density and viscosity of a liquid within a density range from 0.998 to 1.1466 × 103 kg/m3 and a viscosity range from 1 to 8.4 cP. These density and viscosity ranges span the regime of possible changes of blood characteristics. The microdiaphragm resonating sensors were also tested with a real human serum to verify that the sensor is suitable for measuring the viscosity and density of blood. Therefore, the PZT microdiaphragm resonating sensor could be utilized for early diagnosis of diseases associated with changes in the physical properties of blood.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".