Amplifying the Sensitivity of Electrospun Polyvinylidene Fluoride Piezoelectric Sensors Through Electrical Polarization Process for Low-Frequency Applications
Bibliographic record
Abstract
Piezoelectric sensors convert mechanical stress into electrical charge via the piezoelectric effect, and when fabricated as fibers, they offer flexibility, lightweight properties, and adaptability to complex shapes for self-powered wearable sensors. Polyvinylidene fluoride (PVDF) nanofibers have garnered significant interest due to their potential applications in various fields, including sensors, actuators, and energy-harvesting devices. Achieving optimal piezoelectric properties in PVDF nanofibers requires the careful optimization of polarization. Applying a high electric field to PVDF chains can cause significant mechanical deformation due to electrostriction, leading to crack formation and fragmentation, particularly at the chain ends. Therefore, it is essential to explore methods for polarizing PVDF at the lowest possible voltage to prevent structural damage. In this study, a Design of Experiments (DoE) approach was employed to systematically optimize the polarization parameters using a definitive screening design. The main effects of the input parameters on piezoelectric properties were identified. Heat treatment and the electric field were significant factors affecting the sensor’s sensitivity and β-phase fraction. At the highest temperature of 120 °C and the maximum applied electric field of 3.5 kV/cm, the % β-phase (F(β)) exceeded 95%. However, when reducing the electric field to 1.5 kV/cm and 120 °C, the % F(β) ranged between 87.5% and 90%. The dielectric constant (ɛ′) of polarized PVDF was determined to be 30 at an electric field frequency of 1 Hz, compared to a value of 25 for non-polarized PVDF. The piezoelectric voltage coefficient (g33) for polarized PVDF was measured at 32 mV·m/N at 1 Hz, whereas non-polarized PVDF exhibited a value of 3.4 mV·m/N. The findings indicate that, in addition to a high density of β-phase dipoles, the polarization of these dipoles significantly enhances the sensitivity of the PVDF nanofiber mat.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".