期刊
MICROSYSTEMS & NANOENGINEERING
卷 7, 期 1, 页码 -出版社
SPRINGERNATURE
DOI: 10.1038/s41378-021-00324-4
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资金
- Foshan HKUST Projects [FSUST19-FYTRI05]
- Zhongshan Municipal City Introducing Innovation Projects from High-end Scientific Research Institutions [ZSST21EG07]
- Research Grants Council of Hong Kong [16213019]
The study introduces a flexible and low-cost humidity sensor with ultrafast response, high sensitivity, low hysteresis, and good stability. The sensor has been applied in precise human respiration monitoring, noncontact electrical safety warning, and noncontact pipe leakage detection.
The humidity sensor is an essential sensing node in medical diagnosis and industrial processing control. To date, most of the reported relative humidity sensors have a long response time of several seconds or even hundreds of seconds, which would limit their real application for certain critical areas with fast-varying signals. In this paper, we propose a flexible and low-cost humidity sensor using vertically aligned carbon nanotubes (VACNTs) as electrodes, a PDMS-Parylene C double layer as the flexible substrate, and graphene oxide as the sensing material. The humidity sensor has an ultrafast response of similar to 20 ms, which is more than two orders faster than most of the previously reported flexible humidity sensors. Moreover, the sensor has a high sensitivity (16.7 pF/% RH), low hysteresis (<0.44%), high repeatability (2.7%), good long-term stability, and outstanding flexibility. Benefiting from these advantages, especially the fast response, the device has been demonstrated in precise human respiration monitoring (fast breathing, normal breathing, deep breathing, asthma, choking, and apnea), noncontact electrical safety warning for bare hand and wet gloves, and noncontact pipe leakage detection. In addition, the facile fabrication of the flexible platform with the PDMS-Parylene C double layer can be easily integrated with multisensing functions such as pH sensing, ammonium ion sensing, and temperature sensing, all of which are useful for more pattern recognition of human activity.
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