journal article·2017-02-22

Novel Self-Heated Gas Sensors Using on-Chip Networked Nanowires with Ultralow Power Consumption

Ha Minh Tan, Chu Manh Hung, Trinh Minh Ngoc, Hugo Nguyen, Nguyen Duc Hoa, Nguyen Van Duy, Nguyen Van Hieu

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Abstract

The length of single crystalline nanowires (NWs) offers a perfect pathway for electron transfer, while the small diameter of the NWs hampers thermal losses to tje environment, substrate, and metal electrodes. Therefore, Joule self-heating effect is nearly ideal for operating NW gas sensors at ultralow power consumption, without additional heaters. The realization of the self-heated NW sensors using the “pick and place” approach is complex, hardly reproducible, low yield, and not applicable for mass production. Here, we present the sensing capability of the self-heated networked SnO 2 NWs effectively prepared by on-chip growth. Our developed self-heated sensors exhibit a good response of 25.6 to 2.5 ppm NO 2 gas, while the response to 500 ppm H 2, 100 ppm NH 3, 100 ppm H 2 S, and 500 ppm C 2 H 5 OH is very low, indicating the good selectivity of the sensors to NO 2 gas. Furthermore, the detection limit is very low, down to 82 parts-per-trillion. As-obtained sensing performance under self-heating mode is nearly identical to that under external heating mode. While the power consumption under self-heating mode is extremely low, around hundreds of microwatts, as scaled-down the size of the electrode is below 10 μm. The selectivity of the sensors can be controlled simply by tuning the loading power that enables simple detection of NO 2 in mixed gases. Remarkable performance together with a significantly facile fabrication process of the present sensors enhances the potential application of NW sensors in next generation technologies such as electronic noses, the Internet of Things, and smartphone sensing.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Advanced Chemical Sensor Technologies · Analytical Chemistry and Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

International Training Institute for Materials Science, Hanoi University of Science and Technology, No 1 Dai Co Viet Road, Hai Ba Trung, 10000 Hanoi, Vietnam

Department of Engineering Sciences, Division of Microsystem Technology, Uppsala University, Lägerhyddsvägen 1, 751 21 Uppsala, Sweden

Funding

National Foundation for Science and Technology Development

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