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Graphene-Doped Piezoelectric Transducers by Kriging Optimal Model for Detecting Various Types of Laryngeal Movements.

Authors :
Lee, Ming-Chan
Pan, Cheng-Tang
Juan, Shuo-Yu
Wen, Zhi-Hong
Xu, Jin-Hao
Janesha, Uyanahewa Gamage Shashini
Lin, Fan-Min
Source :
Micromachines; Oct2024, Vol. 15 Issue 10, p1213, 22p
Publication Year :
2024

Abstract

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table U * 7 7 4 was applied, considering weight percentage (1–13 wt%), the distance between needle and disk collector (2.1–3.9 mm), and applied voltage (14.5–17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 µS/cm for pure PVDF to 187.5 µS/cm. The fiber diameter decreased from 21.4 µm in PVDF/1% graphene to 9.1 µm in PVDF/13% graphene. Adding 5 wt% graphene increased the β-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. The results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2072666X
Volume :
15
Issue :
10
Database :
Complementary Index
Journal :
Micromachines
Publication Type :
Academic Journal
Accession number :
180488615
Full Text :
https://doi.org/10.3390/mi15101213