Moisture-resistant, stretchable NO x gas sensors based on laser-induced graphene for environmental monitoring and breath analysis
The accurate, continuous analysis of healthcare-relevant gases such as nitrogen oxides (NO ) in a humid environment remains elusive for low-cost, stretchable gas sensing devices. This study presents the design and demonstration of a moisture-resistant, stretchable NO gas sensor based on laser-induce...
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Veröffentlicht in: | Microsystems & nanoengineering 2022, Vol.8, p.78 |
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Hauptverfasser: | , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The accurate, continuous analysis of healthcare-relevant gases such as nitrogen oxides (NO
) in a humid environment remains elusive for low-cost, stretchable gas sensing devices. This study presents the design and demonstration of a moisture-resistant, stretchable NO
gas sensor based on laser-induced graphene (LIG). Sandwiched between a soft elastomeric substrate and a moisture-resistant semipermeable encapsulant, the LIG sensing and electrode layer is first optimized by tuning laser processing parameters such as power, image density, and defocus distance. The gas sensor, using a needlelike LIG prepared with optimal laser processing parameters, exhibits a large response of 4.18‰ ppm
to NO and 6.66‰ ppm
to NO
, an ultralow detection limit of 8.3 ppb to NO and 4.0 ppb to NO
, fast response/recovery, and excellent selectivity. The design of a stretchable serpentine structure in the LIG electrode and strain isolation from the stiff island allows the gas sensor to be stretched by 30%. Combined with a moisture-resistant property against a relative humidity of 90%, the reported gas sensor has further been demonstrated to monitor the personal local environment during different times of the day and analyze human breath samples to classify patients with respiratory diseases from healthy volunteers. Moisture-resistant, stretchable NO
gas sensors can expand the capability of wearable devices to detect biomarkers from humans and exposed environments for early disease diagnostics. |
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ISSN: | 2055-7434 |