Your browser doesn't support javascript.
loading
System Integration of an Optimally Designed Virtual Impactor with a QCM Sensor as a One-Stop PM2.5 Classification and Detection Platform.
Wang, Yong; Mei, Xuze; Xu, Zhonggui; Qian, Jingui.
Afiliação
  • Wang Y; Department of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China.
  • Mei X; Department of Mechanical Engineering, Hangzhou City University, Hangzhou 310015, China.
  • Xu Z; School of Advanced Materials and Mechatronic Engineering, Hubei Minzu University, Enshi 445000, China.
  • Qian J; Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China.
ACS Omega ; 9(5): 5751-5760, 2024 Feb 06.
Article em En | MEDLINE | ID: mdl-38343940
ABSTRACT
Excessive exposure to airborne particulate matter (PM), especially PM2.5 particles, adversely affects human health. Conventional PM2.5 detection instruments based on light scattering are generally bulky, expensive, and easily affected by particle size and composition. Here, we report a low-cost and compact one-stop PM2.5 detection platform by integrating a three-dimensional (3D) printed virtual impactor with a QCM sensor. To reduce eddy and airflow impact on the side wall and improve the VI lifetime, a computational fluid dynamics simulation is used to optimize the VI structure. Results show that when the included angle between the minor flow channel and the inner side of the major flow channel is 40-45° and the included angle between the inlet channel and the outside wall of the major flow channel is 125°, the VI has a relatively small particle loss, eddy, and good collection efficiency. Finally, the system detection performance is experimentally evaluated with a sensitivity of 0.08 Hz/min per µg/m3, showing a comparable performance with the commercial instrument.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: ACS Omega Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: ACS Omega Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China