• 文献标题:   Chemiresistive Graphene Sensors for Ammonia Detection
  • 文献类型:   Article
  • 作  者:   MACKIN C, SCHROEDER V, ZURUTUZA A, SU C, KONG J, SWAGER TM, PALACIOS T
  • 作者关键词:   graphene, chemiresistive sensor, porphyrin, co tpfpp clo4, nh3 sensor
  • 出版物名称:   ACS APPLIED MATERIALS INTERFACES
  • ISSN:   1944-8244 EI 1944-8252
  • 通讯作者地址:   MIT
  • 被引频次:   12
  • DOI:   10.1021/acsami.8b00853
  • 出版年:   2018

▎ 摘  要

The primary objective of this work is to demonstrate a novel sensor system as a convenient vehicle for scaled-up repeatability and the kinetic analysis of a pixelated testbed. This work presents a sensor system capable of measuring hundreds of functionalized graphene sensors in a rapid and convenient fashion. The sensor system makes use of a novel array architecture requiring only one sensor per pixel and no selector transistor. The sensor system is employed specifically for the evaluation of Co(tpfpp)ClO4 functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO4 treated graphene sensors were found to provide 4-fold increased ammonia sensitivity over pristine graphene sensors. Sensors were also found to exhibit excellent selectivity over interfering compounds such as water and common organic solvents. The ability to monitor a large sensor array with 160 pixels provides insights into performance variations and reproducibility critical factors in the development of practical sensor systems. All sensors exhibit the same linearly related responses with variations in response exhibiting Gaussian distributions, a key finding for variation modeling and quality engineering purposes. The mean correlation coefficient between sensor responses was found to be 0.999 indicating highly consistent sensor responses and excellent reproducibility of Co(tpfpp)ClO4 functionalization. A detailed kinetic model is developed to describe sensor response profiles. The model consists of two adsorption mechanisms-one reversible and one irreversible and is shown capable of fitting experimental data with a mean percent error of 0.01%.