期刊
IEEE INTERNET OF THINGS JOURNAL
卷 6, 期 1, 页码 831-843出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2018.2861330
关键词
Capacitive micromachined ultrasonic transducer (CMUT); frequency counter; front-end integrated circuit (IC); gravimetric sensor; Internet of Things (IoT); multichannel gas sensor; volatile organic compound (VOC)
资金
- National Science Foundation, National Nanotechnology Coordinated Infrastructure [ECCS-1542015]
- National Science Foundation [1160483]
Detection of volatile organic compounds (VOCs), challenged by their diversity and similarity, is gaining much attention due to concerns about adverse health effects they cause, along with intensifying development efforts in wireless sensor nodes. Precise identification of volatiles may be subject to the sensitivity and selectivity of a sensor itself and the proximity of the sensor to the source, necessitating power-efficient and portable/wearable sensing systems. The metal-oxide sensors, commonly employed for detection of VOCs, are not power efficient, due to the required heating element, and lack the selectivity, thus reporting only the total VOC level. In this paper, we present a complete low-power wireless gas-sensing system based a capacitive micromachined ultrasonic transducer array, which is known to have several advantages such as high mass sensitivity, easy implementation of a multielement structure, and high selectivity upon polymer coating. We took a holistic approach to designing the sensing elements and the custom integrated circuit (IC) as well as to operating the system, resulting in a small self-contained sensor node (38-mm diameter and 16-mm height). The chemical-sensing capability of the system has been validated with ethanol, achieving 120-ppb limit-of-detection while the sensor array, including the IC and the power management unit, consuming 80-mu W average power with power cycling by actively taking measurements for 3 s per minute. The presented system will eventually provide a ubiquitous tool to identify VOCs with the help of multivariate data analysis.
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