A Flexible Piezoelectric PVDF/MXene Pressure Sensor for Roughness Discrimination

Xinwang Wang, Yiming Lu, Jiashun Jiang, Chunyu Lv, Hailing Fu, Mengying Xie*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 \beta -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^{-{1}} 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.

Original languageEnglish
Pages (from-to)7176-7184
Number of pages9
JournalIEEE Sensors Journal
Volume24
Issue number5
DOIs
Publication statusPublished - 1 Mar 2024

Keywords

  • Convolutional neural network (CNN)
  • electrospinning
  • piezoelectric
  • pressure sensor
  • roughness discrimination

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