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Research data supporting "Unencapsulated and Washable Two-Dimensional Material Electronic-Textile for NO2 Sensing In Ambient Air"


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Dataset

Change log

Authors

Oluwasanya, Pelumi 
Carey, Tian 
Yarjan, Yarjan 

Description

Dataset related to the manuscript entitled "Unencapsulated and Washable Two-Dimensional Material Electronic-Textile for NO2 Sensing In Ambient Air". Abstract: Materials adopted in electronic gas sensors, such as chemiresistive-based NO2 sensors, for integration in clothing fail to survive standard wash cycles due to the combined effect of aggressive chemicals in washing liquids and mechanical abrasion. Device failure can be mitigated by using encapsulation materials, which, however, reduces the sensor performance in terms of sensitivity, selectivity, and therefore utility. A highly sensitive NO2 electronic textile (e-textile) sensor was fabricated on Nylon fabric, which is resistant to standard washing cycles, by coating Graphene Oxide (GO), and GO/Molybdenum disulfide (GO/MoS2) and carrying out in situ reduction of the GO to Reduced Graphene Oxide (RGO). The GO/MoS2 e-textile was selective to NO2 and showed sensitivity to 20 ppb NO2 in dry air (0.05%/ppb) and 100 ppb NO2 in humid air (60% RH) with a limit of detection (LOD) of ~ 7.3 ppb. The selectivity and low LOD is achieved with the sensor operating at ambient temperatures (~ 20 °C). The sensor maintained its functionality after undergoing 100 cycles of standardised washing with no encapsulation. The relationship between temperature, humidity and sensor response was investigated. The e-textile sensor was embedded with a microcontroller system, enabling wireless transmission of the measurement data to a mobile phone. These results show the potential for integrating air quality sensors on washable clothing for high spatial resolution (< 25 cm2)—on-body personal exposure monitoring.

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Keywords

electronic textiles, gas sensing, graphene oxide, molybdenum disulfide, reduced graphene oxide, two-dimensional materials

Publisher

Sponsorship
Innovate UK project No. 103543 MPSENS, EPSRC grants EP/KO3099X/1 and EP/LO15889/1, European H2020 project 1D-NEON (Grant Agreement No. 685758)
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