Sanghoon Park’s research while affiliated with Seoul National University and other places

This study explored the toxic gas detection using a set of two spark-induced plasma emission spectroscopy (SIPS) modules as an alternative to conventional chemical sensors. This opens up a new possibility for detecting toxic molecules (formaldehyde, acetaldehyde, acetic acid, toluene, and ammonia) in real-time (<3 s) at relatively high sensitivity (<5 ppm). An optimized electrical controller (Raspberry Pi), manufactured as a compact and economical 3-channel optical measuring device, was developed for handling the high-resolution time-resolved electrical signals from the plasma emissions. Subsequently, the findings of this research elucidate the usability of the 3-channel SIPS device for quantitative monitoring of toxic gases.

Air pollution related to fine dust is increasing globally with rapid industrialization and modernization. With the rising environmental and health concerns with respect to air quality, it is imperative to develop an advanced system for detecting harmful components from airborne particles. Based on spark-induced plasma spectroscopy (SIPS), a novel instrument was designed for measurements of fine dust atomic components in situ. All the functional parts related to plasma generation, measurement, and data visualization of time-resolved electric signals were integrated into a compact single module with embedded software. Quantitative measurements were conducted to investigate and verify the performance of the all-in-one module for key components of fine dust, namely, Fe, Mg, Si, Ca, Al, and K. Along with the enhanced limit of detection by ∼1.4 times the previous setup, the quantitative monitoring of fine dust in the atmospheric environment is illustrated via the compact SIPS module.

Accurate identification and monitoring of fine dust are emerging as a primary global issue for addressing the harmful effects of fine dust on public health. Identifying the source of fine dust is indispensable for ensuring the human lifespan as well as preventing environmental disasters. Here a simple yet effective spark-induced plasma spectroscopy (SIPS) unit combined with deep learning for real-time classification is verified as a fast and precise PM (particulate matter) source identification technique. SIPS promises portable use, label-free detection, source identification, and chemical susceptibility in a single step with acceptable speed and accuracy. In particular, the densely connected convolutional networks (DenseNet) are used with measured spark-induced plasma emission datasets to identify PM sources at above 98%. The identification performance was compared with other common classification methods, and DenseNet with dropouts (30%), optimized batch size (16), and cyclic learning rate training emerged as the most promising source identification method.