A Flexible Piezoelectric PVDF/MXene Pressure Sensor for Roughness Discrimination

Abstract

The accurate assessment of surface roughness is critical for numerous applications ranging from quality control in manufacturing to material characterization. To achieve a functional capability of roughness perception, a flexible pressure sensor based on polyvinylidene fluoride-Ti3C2 (PVDF/MXene) nanocomposite is developed. The sensor consists of electrospun PVDF nanofibers embedded with 2-D MXene nanosheets. The MXene enhances the piezoelectric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -phase content of the PVDF up to 97.2% at optimal loading of 2.5 wt%. The PVDF/MXene nanocomposite exhibited high piezoelectric voltage sensitivity up to 0.059 V kPa <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{1}}$ </tex-math></inline-formula> under applied pressures. The wavelet transform analysis of signals obtained by scanning the sensor on sandpapers of varying roughness showed distinct time–frequency patterns corresponding to different surface roughness levels. Unsupervised dimensionality reduction using t-distributed stochastic neighbor embedding (t-SNE) revealed clustering of roughness data into distinct categories. A convolutional neural network (CNN) classifier achieved 98% accuracy in categorizing the surface roughness based on the sensor signal wavelet transforms. The piezoelectric nanocomposite sensor shows promise for surface metrology applications.