• 文献标题:   Hetero-Dimensional 2D Ti3C2Tx MXene and 1D Graphene Nanoribbon Hybrids for Machine Learning-Assisted Pressure Sensors
  • 文献类型:   Article
  • 作  者:   LEE HJ, YANG JC, CHOI J, KIM J, LEE GS, SASIKALA SP, LEE GH, PARK SHK, LEE HM, SIM JY, PARK S, KIM SO
  • 作者关键词:   mxene, graphene nanoribbon, hybridization, pressure sensor, machine learning, healthcare monitoring
  • 出版物名称:   ACS NANO
  • ISSN:   1936-0851 EI 1936-086X
  • 通讯作者地址:  
  • 被引频次:   38
  • DOI:   10.1021/acsnano.1c02567 EA MAY 2021
  • 出版年:   2021

▎ 摘  要

Hybridization of low-dimensional components with diverse geometrical dimensions should offer an opportunity for the discovery of synergistic nanocomposite structures. In this regard, how to establish a reliable interfacial interaction is the key requirement for the successful integration of geometrically different components. Here, we present 1D/2D heterodimensional hybrids via dopant induced hybridization of 2D Ti3C2Tx MXene with 1D nitrogen-doped graphene nanoribbon. Edge abundant nanoribbon structures allow a high level nitrogen doping (similar to 6.8 at%), desirable for the strong coordination interaction with Ti3C2Tx MXene surface. For piezoresistive pressure sensor application, strong adhesion between the conductive layers and at the conductive layer/elastomer interface significantly diminishes the sensing hysteresis down to 1.33% and enhances the sensing stability up to 10 000 cycles at high pressure (100 kPa). Moreover, large-area pressure sensor array reveals a high potential for smart seat cushion-based posture monitoring application with high accuracy (>95%) by exploiting machine learning algorithm.