An optimally designed virtual impactor integrated with a quartz crystal microbalance sensor for submicron particulate matter classification and detection.

Abstract

Long-term exposure to particulate matter, especially submicron particulate matter (PM1), poses significant health risks by inducing oxidative stress and inflammation. This paper reports an optimally designed virtual impactor (VI) integrated with a quartz crystal microbalance (QCM) sensor for the classification and detection of PM1 particles. Computational fluid dynamics simulations were employed to optimize the included angles and outlet size of the VI's flow channels, minimizing eddy formation and reducing airflow impact on the sidewalls of the flow channels, thereby enhancing the durability of the VI. The VI was fabricated using 3D printing, and its optimization effectiveness was validated by assessing particle wall loss. The performance of the PM1 detection system was examined by classifying SiO2 particles ranging from 0.2 to 2 µm using the VI and detecting PM1 particles with the QCM sensor. Results showed that after classification, the majority of particles in the major flow channels were PM1. The frequency shift of the QCM sensor showed a linear correlation with the mass of particles deposited on its surface. Moreover, the system's performance was found to be comparable to that of commercial instruments.