Rapid-response hybrid piezo-triboelectric pressure sensor using all-fabric materials for enhanced sensing and power generation

  • Su Bin Choi
  • , Sushmitha Veeralingam
  • , Tran Duc Khanh
  • , Jun Sang Choi
  • , Kampara Roopa Kishore
  • , Seung Boo Jung
  • , Jong Woong Kim

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents a hybrid electronic pressure sensor that integrates triboelectric and piezoelectric effects using nylon-6,6 and polyvinylidene fluoride (PVDF) fabrics embedded with MXene and MoS₂ nanosheets. The hybrid triboelectric nanogenerator (TENG) design addresses the fundamental trade-off between energy harvesting efficiency and pressure sensing performance found in single-mechanism sensors. By leveraging the high voltage output of the triboelectric effect at low pressures and the linear response of the piezoelectric effect to applied pressure, this sensor achieves a broad sensing range of up to 150 kPa, high sensitivity of 3.18 V/kPa, and an ultra-fast response time of 0.38 ms. The incorporation of MXene nanofillers enhances charge transport by forming conductive pathways within the polymer matrix, while the PVDF/MoS₂/MXene (PMMX) layer further improves frictional and piezoelectric responses. MXene's high electronegativity and MoS₂’s piezoelectric properties contribute to increased sensitivity and charge transfer efficiency. When tested on various body parts, the sensor effectively detects human motion and supports energy harvesting. Furthermore, integration with a one-dimensional convolutional neural network (1D-CNN) achieves 99.18 % accuracy in gesture classification, demonstrating its potential for smart wearable applications. By combining high efficiency, rapid response, broad sensing range, and machine learning compatibility, this hybrid sensor provides a versatile and sustainable solution for next-generation flexible and wearable electronics.

Original languageEnglish
Article number111000
JournalNano Energy
Volume140
DOIs
StatePublished - Jul 2025

Keywords

  • Deep learning
  • Hybridelectronic
  • Molybdenum disulfide
  • MXene
  • Pressure sensor
  • Triboelectric nanogenerator

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