TY - JOUR
T1 - A Micro-Airflow Sensor System Enabled by Triboelectric Nanogenerator for Lab Safety and Human-Computer Interaction
AU - Wang, Xucong
AU - Li, Yingzhe
AU - Liu, Chaoran
AU - You, Weilong
AU - Zou, Haiyang
AU - Yue, Chenxi
AU - Cheng, Jiagen
AU - Yang, Weihuang
AU - Li, Shaoxian
AU - Lazarouk, Serguei
AU - Labunov, Vladimir
AU - Wang, Gaofeng
AU - Lin, Hongjian
AU - Dong, Linxi
PY - 2024/3/1
Y1 - 2024/3/1
N2 - The airflow sensor enabled by triboelectric nanogenerator (TENG) is significant for intelligent lab safety and human-computer interaction applications. However, the reported airflow/wind sensor focuses on enhancing the sensing materials and structures, lack of high resolution, and smart signal analysis. Herein, we present a self-powered micro-airflow sensor and its artificial intelligence (AI) system, applied for lab safety and human-computer interaction. The as-fabricated sensor has a high sensitivity of 0.6258~\mu \text{A} /(m/s) and a linearity of 0.9968. Attributing to the Venturi effect, the minimum detection velocity of the sensor is 0.13 m/s. Given the sensor performance, we develop a real-time pipeline gas leak location system with an AI user interface, which achieves a potential low detect error \le 2.9 cm. In addition, we successfully explore other applications, including human exit-entry counting, ventilation alarm, and breath-based smart aid communication. Above all, the airflow sensor exhibits tremendous potential in the AI and Internet of Things. © 2001-2012 IEEE.
AB - The airflow sensor enabled by triboelectric nanogenerator (TENG) is significant for intelligent lab safety and human-computer interaction applications. However, the reported airflow/wind sensor focuses on enhancing the sensing materials and structures, lack of high resolution, and smart signal analysis. Herein, we present a self-powered micro-airflow sensor and its artificial intelligence (AI) system, applied for lab safety and human-computer interaction. The as-fabricated sensor has a high sensitivity of 0.6258~\mu \text{A} /(m/s) and a linearity of 0.9968. Attributing to the Venturi effect, the minimum detection velocity of the sensor is 0.13 m/s. Given the sensor performance, we develop a real-time pipeline gas leak location system with an AI user interface, which achieves a potential low detect error \le 2.9 cm. In addition, we successfully explore other applications, including human exit-entry counting, ventilation alarm, and breath-based smart aid communication. Above all, the airflow sensor exhibits tremendous potential in the AI and Internet of Things. © 2001-2012 IEEE.
KW - Human-computer interaction
KW - micro-airflow detection
KW - pipeline gas leak location
KW - self-powered sensor
KW - smart signal processing system
UR - http://www.scopus.com/inward/record.url?scp=85182952662&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85182952662&origin=recordpage
U2 - 10.1109/JSEN.2024.3350707
DO - 10.1109/JSEN.2024.3350707
M3 - RGC 21 - Publication in refereed journal
SN - 1530-437X
VL - 24
SP - 6880
EP - 6887
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 5
ER -