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Structural Engineering for High Sensitivity, Ultrathin Pressure Sensors Based on Wrinkled Graphene and Anodic Aluminum Oxide Membrane.
Chen, Wenjun; Gui, Xuchun; Liang, Binghao; Yang, Rongliang; Zheng, Yongjia; Zhao, Chengchun; Li, Xinming; Zhu, Hai; Tang, Zikang.
Afiliação
  • Li X; Department of Electronic Engineering, The Chinese University of Hong Kong , Hong Kong SAR, China.
  • Tang Z; Institute of Applied Physics and Materials Engineering, University of Macau , Avenida da Universidade, Taipa, Macau 999078, China.
ACS Appl Mater Interfaces ; 9(28): 24111-24117, 2017 Jul 19.
Article em En | MEDLINE | ID: mdl-28657288
Nature-motivated pressure sensors have been greatly important components integrated into flexible electronics and applied in artificial intelligence. Here, we report a high sensitivity, ultrathin, and transparent pressure sensor based on wrinkled graphene prepared by a facile liquid-phase shrink method. Two pieces of wrinkled graphene are face to face assembled into a pressure sensor, in which a porous anodic aluminum oxide (AAO) membrane with the thickness of only 200 nm was used to insulate the two layers of graphene. The pressure sensor exhibits ultrahigh operating sensitivity (6.92 kPa-1), resulting from the insulation in its inactive state and conduction under compression. Formation of current pathways is attributed to the contact of graphene wrinkles through the pores of AAO membrane. In addition, the pressure sensor is also an on/off and energy saving device, due to the complete isolation between the two graphene layers when the sensor is not subjected to any pressure. We believe that our high-performance pressure sensor is an ideal candidate for integration in flexible electronics, but also paves the way for other 2D materials to be involved in the fabrication of pressure sensors.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article