Ultrasensitive Strain Sensor Utilizing a AgF–AgNW Hybrid Nanocomposite for Breath Monitoring and Pulmonary Function Analysis
Source abstract
Abstract
Breath monitoring and pulmonary function analysis have been the prime focus of wearable smart sensors owing to the COVID-19 outbreak. Currently used lung function meters in hospitals are prone to spread the virus and can result in the transmission of the disease. Herein, we have reported the first-ever wearable patch-type strain sensor for enabling real-time lung function measurements (such as forced volume capacity (FVC) and forced expiratory volume (FEV) along with breath monitoring), which can avoid the spread of the virus. The noninvasive and highly sensitive strain sensor utilizes the synergistic effect of two-dimensional (2D) silver flakes (AgFs) and one-dimensional (1D) silver nanowires (AgNWs), where AgFs create multiple electron transmission paths and AgNWs generate percolation networks in the nanocomposite. The nanocomposite-based strain sensor possesses a high optimized conductivity of 7721 Sm–1 (and a maximum conductivity of 83,836 Sm–1), excellent stretchability (>1000%), and ultrasensitivity (GFs of 35 and 87 when stretched 0–20 and 20–50%, respectively), thus enabling reliable detection of small strains produced by the body during breathing and other motions. The sensor patching site was optimized to accurately discriminate between normal breathing, quick breathing, and deep breathing and analyze numerous pulmonary functions, including the respiratory rate, peak flow, FVC, and FEV. Finally, the observed measurements for different pulmonary functions were compared with a commercial peak flow meter and a spirometer, and a high correlation was observed, which highlights the practical feasibility of continuous respiratory monitoring and pulmonary function analysis.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Gas Sensing Nanomaterials and Sensors · Advanced Chemical Sensor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung80424, Taiwan
Sustainability Science and Engineering Program, Tunghai University, Taichung407224, Taiwan
Department of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung80145, Taiwan
Funding
Ministry of Science and Technology, Taiwan
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