• 文献标题:   Ultrathin MoOx/Graphene Hybrid Field Effect Transistor Sensors Prepared Simply by a Shadow Mask Approach for Selective ppb-Level NH3 Sensing with Simultaneous Superior Response and Fast Recovery
  • 文献类型:   Article
  • 作  者:   FALAK A, TIAN Y, YAN LQ, XU LH, SONG ZW, HU HF, DONG FL, ADAMU BI, ZHAO M, CHEN PP, WANG HF, CHU WG
  • 作者关键词:   field effect transistor sensor, generalized contaminationfree shadow mask approach, nh, 3 sensing, reducing, oxidizing gase, schottky barrier height, superior performance
  • 出版物名称:   ADVANCED MATERIALS INTERFACES
  • ISSN:   2196-7350
  • 通讯作者地址:   Natl Ctr Nanosci Technol
  • 被引频次:   1
  • DOI:   10.1002/admi.201902002
  • 出版年:   2020

▎ 摘  要

Both simultaneous achievement of high response, low limit of detection, and full recovery at room temperature (RT) for weak reducing gases like NH3 and facile and batchable fabrication approach are quite challenging for sensors. Herein, ultrathin MoOx layers are hybridized with mono layer graphene with different coverage percentages, into MoOx/GFET (graphene field effect transistor) devices for selective NH3 sensing fabricated by a facile, cost effective, and contamination-free shadow mask approach instead of conventional lithography processes. A response of -18.10% for 12 ppm NH3 with full recovery of 356 s, superior repeatability, low detection limit of 310 ppb, and strong selectivity is simultaneously achieved for MoOx/GFET sensors at RT. The superior sensing and recovery performance of MoOx/GFET sensors is predominantly attributed to the effective tuning of Schottky barrier height and the Coulomb interaction between charged polar donor molecules and positively polarized surface enhanced by the positive bias voltage. The energy band diagrams well explain the sensing mechanism for reducing/oxidizing gases. The idea proposed in this study offers a feasible solution for highly selective sensing of different gases by oxides/graphene hybrid FET based gas sensors with superior RT performances fabricated by a facile, contamination-free, batchable, and generalized approach.