Advances in carbon nanotube-based gas sensors: Exploring the path to the future

被引:9
|
作者
Luo, Kun [1 ,2 ]
Peng, Haoran [1 ,2 ]
Zhang, Bo [1 ]
Chen, Liming [1 ]
Zhang, Panpan [1 ]
Peng, Zhijian [2 ]
Fu, Xiuli [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Integrated Circuits, Beijing 100876, Peoples R China
[2] China Univ Geosci Beijing, Sch Sci, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon nanotube; Gas sensor; Sensing mechanisms; Sensing platforms; CHEMICAL-VAPOR-DEPOSITION; SENSING PROPERTIES; CHEMIRESISTIVE SENSORS; SELECTIVE DETECTION; METHANE DETECTION; CHLORINE SENSOR; NITRIC-OXIDE; NH3; GAS; ELECTRONIC-PROPERTIES; HYDROGEN;
D O I
10.1016/j.ccr.2024.216049
中图分类号
O61 [无机化学];
学科分类号
070301 ; 081704 ;
摘要
In the era of the Internet of Things, sensors are pivotal for a smart and interconnected future. Gas sensors, particularly those based on carbon nanotubes (CNTs), emerge as essential tools supporting environmental protection policies with their highly sensitive gas detection capabilities. This review highlights the recent strides in CNTs based gas sensors. It begins with outlining the CNT fundamental traits, establishing the groundworks for explaining their working principles and integrated manufacturing. The discussion proceeds to key parameters for evaluating gas sensor performance. Strengths-Weaknesses-Opportunities-Threats analysis showed that CNTs based gas sensors demonstrate promising potential in various applications, leveraging their exceptional responsiveness, sustainability, and integration capabilities, despite facing challenges in scalability and market competition. The review then delves into the research progress and sensing mechanisms of CNTs based gas sensors in environmental monitoring, industrial safety, and health care, showcasing their exceptional attributes like high sensitivity, fast response, and low power consumption. Finally, the future development of CNTs based gas sensors may focus on enhancing sensing performance, achieving miniaturization, integration and multifunctionality, standardizing measurement processes, as well as utilizing machine learning to improve sensor selectivity and stability. These advances aim to enhance reliability, broaden application scopes, facilitate commercialization, and meet the evolving needs of practical applications.
引用
收藏
页数:40
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