3D Printing of Polyvinylidene Fluoride‐Based Piezoelectric Sensors for Noninvasive Continuous Blood Pressure Monitoring
Bibliographic record
Abstract
The successful fabrication and application of 3D‐printed piezoelectric sensors, made from polyvinylidene fluoride‐barium titanate nanocomposites, for continuous blood pressure (BP) monitoring, is presented. The sensors are fabricated using direct‐ink write 3D printing technique possessing excellent flexibility, rendering them ideal for wearable applications. Key printing parameters, such as nozzle size and pressure, are optimized to achieve a β‐phase of 70% and an overall crystallinity of 48.3%. The sensors are characterized under dynamic pressures and show excellent linearity with a coefficient of determination (R2) of 0.99 and a pressure sensitivity of 0.024 V kPa−1, and demonstrate durability over 3600 cycles. The performance of our piezoelectric sensors is compared with a standard BP monitoring device for a subject, and a strong correlation with a standard deviation of 3.87 and 0.63 mmHg for sytolic BP and diastolic BP, respectively, is found. The development of sensors through 3D printing, as demonstrated in this study, represents a significant scientific advancement in the field of personalized healthcare. This novel approach enables the creation of customized wearable devices tailored for real‐time health monitoring of BP and heart rate.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".