AI-Driven Wearable Mask-Inspired Self-Healing Sensor Array for Detection and Identification of Volatile Organic Compounds |
| |
| Authors: | Mingrui Chen Min Zhang Ziyang Yang Cheng Zhou Daxiang Cui Hossam Haick Ning Tang |
| |
| Affiliation: | 1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 P. R. China;2. School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 P. R. China;3. Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, 3200003 Israel |
| |
| Abstract: | ![]()
Volatile organic compounds (VOCs) sensor arrays have garnered considerable attention due to their potential to provide real-time information for monitoring pollution levels and personal health associated concerning VOCs in the ambient environment. Here, an AI-driven wearable mask-inspired self-healing sensor array (MISSA), created using a simplified single-step stacking technique for detecting and identifying VOCs is presented. This wearable MISSA comprises three vertically placed breathable self-healing gas sensors (BSGS) with linear response behavior, consistent repeatability, and reliable self-healing abilities. For wearable and portable monitoring, the MISSA is combined with a flexible printed circuit board (FPCB) to produce a mobile-compatible wireless system. Due to the distinct layers of MISSA, it creates exclusive code bars for four distinct VOCs over three concentration levels. This grants precise gas identification and concentration prognoses with excellent accuracy of 99.77% and 98.3%, respectively. The combination of MISSA with artificial intelligence (AI) suggests its potential as a successful wearable device for long-term daily VOC monitoring and assessment for personal health monitoring scenarios. |
| |
| Keywords: | machine learning self-healing volatile organic compounds wearable sensors |
|
|
