This review presents the recent development of printed gas sensors based on functional inks.
A rapidly growing interdisciplinary research area combining aerogel and printing technologies that began only five years ago has been comprehensively reviewed.
Early diagnosis in exhaled breath is a key technology for next-generation personal healthcare monitoring. Current chemiresistive sensors, primarily based on metal oxide (MOx) thin films, have limited applicability in such portable systems due to their high power consumption, long recovery time, poor device-to-device consistency, and baseline drifts. To address these challenges for ammonia ($${{\rm{NH}}}_{3}$$ NH 3 ) detection in exhaled breath, a critical biomarker for a variety of kidney and liver problems, we present a formulation of a graphene–MOx functional ink-based sensing platform. We integrate our sensing layer directly onto miniaturized CMOS microhotplates (μHP) via inkjet printing, potentially enabling scalability and device-to-device performance repeatability. Using stage-by-stage temporal analysis, and a temperature-pulsed modulation (TM) strategy, we achieve ultrahigh responsivity (1500% at 10 ppm pure $${{\rm{NH}}}_{3}$$ NH 3 ), fast response and recovery time (28 and 43 s), ultralow power consumption (~6 mW), negligible baseline drift (<0.67%), excellent cross-device and cross-cycle consistency (<0.5% and <0.41% variation in responsivity) and long-term stability (<1% variation) in our graphene–zinc oxide (ZnO) formulation, outperforming conventional MOx chemiresistive sensors. We further mitigate the effect of humidity through our measurement protocols, while interference from acetone is compensated through the parallel deployment of an additional inkjet printed graphene–tungsten oxide ($${{\rm{WO}}}_{3}$$ WO 3 ) device as part of the sensor array. Our dual graphene–MOx formulations and their integration with ultralow power CMOS through inkjet printing represent a significant step towards reliable and portable multi-analyte breath diagnostics.
Selectivity for specific analytes and high‐temperature operation are key challenges for chemiresistive‐type gas sensors. Complementary hybrid materials, such as reduced graphene oxide (rGO) decorated with metal oxides enables realization of room‐temperature sensors with enhanced sensitivity. However, sensor training to identify target gases and accurate concentration measurement from gas mixtures still remain very challenging. This work proposes hybridization of rGO with CuCoOx binary metal oxide as a sensing material. Highly stable, room‐temperature NO2 sensors with a 50 ppb of detection limit is demonstrated using inkjet printing. A framework is then developed for machine‐intelligent recognition with good visibility to identify specific gases and predict concentration under an interfering atmosphere from a single sensor. Using ten unique parameters extracted from the sensor response, the machine learning‐based classifier provides a decision boundary with 98.1% accuracy, and is able to correctly predict previously unseen NO2 and humidity concentrations in an interfering environment. This approach enables implementation of an intelligent platform for printable, room‐temperature gas sensors in a mixed environment irrespective of ambient humidity.
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