Department of Electrical Engineering and Computer Science , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge Massachusetts 02139 , United States.
Department of Chemistry , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge Massachusetts 02139 , United States.
ACS Appl Mater Interfaces. 2018 May 9;10(18):16169-16176. doi: 10.1021/acsami.8b00853. Epub 2018 Apr 20.
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)ClO functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO 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)ClO 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%.
这项工作的主要目的是展示一种新型传感器系统，作为大规模重复和像素化测试台动力学分析的便捷工具。这项工作提出了一种能够快速方便地测量数百个功能化石墨烯传感器的传感器系统。该传感器系统采用了一种新颖的阵列架构，每个像素只需要一个传感器，不需要选择器晶体管。该传感器系统专门用于评估 Co(tpfpp)ClO 对石墨烯传感器的功能化，以扩展以前的工作，用于检测氨气。研究发现，Co(tpfpp)ClO 处理的石墨烯传感器在检测氨气方面的灵敏度比原始石墨烯传感器提高了 4 倍。传感器还被发现对干扰化合物（如水和常见有机溶剂）具有优异的选择性。能够监测具有 160 个像素的大型传感器阵列，可以深入了解性能变化和可重复性，这是实际传感器系统开发中的关键因素。所有传感器都表现出相同的线性相关响应，响应变化呈现高斯分布，这是用于变化建模和质量工程目的的关键发现。传感器响应之间的平均相关系数被发现为 0.999，表明 Co(tpfpp)ClO 功能化具有高度一致的传感器响应和出色的可重复性。还开发了一个详细的动力学模型来描述传感器的响应特征。该模型由两种吸附机制组成——一种是可逆的，另一种是不可逆的——并被证明能够以平均误差 0.01%拟合实验数据。
