Optimization of Carbon Nanotube Based Chemical Sensors Through Micro-Raman Enabled Defect Analysis

摘要：本研究的目的是通过原位识别和操纵碳纳米管（CNT）传导通道中的缺陷来开发高效的基于碳纳米管（CNT）的化学传感器。这些缺陷在碳纳米管电化学灵敏度中起着核心作用，但目前还没有关于特定缺陷类型和/或分布如何影响实际碳纳米管器件的严格研究。该方法基于一种独特且高度集成的过程，涉及微拉曼光谱，电子显微镜，数值建模和微环境电气表征。这项研究解决了基于碳纳米管的化学传感器巨大潜力的主要障碍之一。知识价值包括：(1)开发和评估低成本的碳纳米管化学传感器，提高分析物的灵敏度、效率和选择性；(2)将基本材料和器件表征技术与数值模拟相结合的纳米计量学的进步。这种全面的三管齐下的方法从未被用于系统地检查实际工作装置中的碳纳米管传导通道。所提出的研究将提供对这些缺陷如何影响工作纳米器件性能的深入理解，并使表征纳米电子学的新方法成为可能。该项目的各种研究活动将涉及当地K-12学校的少数民族本科生和女孩，通过nsf赞助的LSAMP/REU和俄勒冈星期六学院项目，使学生能够学习研究技能，并在未来加入有竞争力的劳动力队伍。研究结果将通过期刊出版物、会议报告、研讨会和pi广泛传播。网站。

Abstract: The objective of this research is to develop highly efficient carbon nanotube (CNT) based chemical sensors through in-situ identification and manipulation of defects in the CNT conduction channel. These defects play a central role in CNT electrochemical sensitivity but there has been no rigorous study of how specific defect types and/or distributions affect actual CNT-based devices. The approach is based on a unique and highly integrated process involving micro-Raman spectroscopy, electron microscopy, numeric modeling and microenvironment electrical characterization.This research tackles one of the principle roadblocks to the enormous potential of CNT-based chemical sensors. The intellectual merit includes: (1) the development and evaluation of low-cost CNT-based chemical sensors with improved analyte sensitivity, efficiency and selectivity; and (2) the advancement of nanometrology by combining both fundamental material and device characterization techniques with numerical modeling. This comprehensive three-pronged approach has never been employed to systematically examine CNT conduction channels in actual working devices. The proposed research will provide an in-depth understanding of how these defects affect the performance of working nanodevices and enable a new methodology for characterizing nanoelectronics.The various research activities of this project will involve minority undergraduate students and girls from local K-12 schools through NSF-sponsored LSAMP/REU and Oregon Saturday Academy programs to enable students to learn research skills and to join a competitive workforce in the future. The research findings will be disseminated broadly through journal publications, conference presentations, seminars, and the PIs? websites.