Machine learning-enabled textile-based graphene gas sensing with energy harvesting-assisted IoT application

• Published in: Nano energy 2021-08, Vol.86, p.106035, Article 106035
• Main Authors: Zhu, Jianxiong, Cho, Minkyu, Li, Yutao, He, Tianyiyi, Ahn, Junseong, Park, Jaeho, Ren, Tian-Ling, Lee, Chengkuo, Park, Inkyu
• Format: Article
• Language: English
• Subjects: Extremely deformation; Graphene sensor; Inkjet-printing; Internet of things; Machine learning
• ISSN: 2211-2855
• DOI: 10.1016/j.nanoen.2021.106035

Flexible gas sensing is attracting more attention with the development of machine learning and Internet of Things (IoT). Herein, we report flexible and foldable high-performance hydrogen (H2) sensor on all textiles substrate-fabricated by inkjet–printing of reduced graphene oxide (rGO) and its application to wearable environmental sensing. The inkjet-printing process provides the advantages of the compatibility with various substrates, the capability of non-contact patterning and cost-effectiveness. The sensing mechanism is based on the catalytic effect of palladium (Pd) nanoparticles (NPs) on the wide bandgap rGO, which allows facile adsorption and desorption of the nonpolar H2 molecules. The graphene textile gas sensor (GT-GS) demonstrates about six times higher sensing response than the graphene polyimide membrane gas sensor due to the large surface area of the textile substrate. An analysis of the temperature influence on the GT-GS shows better H2 gas response at room temperature than at high temperature (e.g., 120 °C). In addition, with the machine learning-enabled technology and triboelectric-textile to power IoT (temperature and humidity for gas calibration), H2 is well identified for wearable applications with a robust mechanical performance (e.g., flexibility and foldability). Flexible gas sensing is attracting more attention with the development of machine learning and Internet of Things (IoT). We report flexible and foldable high-performance hydrogen (H2) sensor on all textiles substrate-fabricated by inkjet–printing of reduced graphene oxide (rGO) and its application to wearable environmental sensing. The graphene textile gas sensor demonstrates about six times higher sensing response than the graphene polyimide membrane gas sensor due to the large surface area. In addition, with the machine learning-enabled technology and triboelectric-textile to power IoT (temperature and humidity for gas calibration), H2 is well identified for wearable applications with a robust mechanical performance. [Display omitted] •With machine learning-assist and triboelectric-textile to power IoT, H2 can be identified to wearable applications.•The gas sensor demonstrated 6 times higher sensing performance compared to other graphene gas sensors.•All-textiles flexible and foldable gas sensor and textile-triboelectric power source were produced.•The inkjet-printing provides the compatibility with various substrates, non-contact patterning and cost-effectiveness.