Microelectromechanical-system-based condensation particle counter for real-time monitoring of airborne ultrafine particles

Seong Jae Yoo, Hong Beom Kwon, Ui Seon Hong, Dong Hyun Kang, Sang Myun Lee, Jangseop Han, Jungho Hwang, Yong Jun Kim

Research output: Contribution to journalArticlepeer-review

Abstract

We present a portable, inexpensive, and accurate microelectromechanical-system-based (MEMS-based) condensation particle counter (CPC) for sensitive and precise monitoring of airborne ultrafine particles (UFPs) at a point of interest. A MEMS-based CPC consists of two main parts: a MEMS-based condensation chip that grows UFPs to micro-sized droplets and a miniature optical particle counter (OPC) that counts single grown droplets with the light scattering method. A conventional conductive cooling-type CPC is miniaturized through MEMS technology and three-dimensional (3-D) printing techniques; the essential elements for growing droplets are integrated on a single glass slide. Our system is much more compact (75 mm×130 mm×50 mm), lightweight (205 g), and power-efficient (2.7 W) than commercial CPCs. In quantitative experiments, the results indicated that our system could detect UFPs with a diameter of 12.9 nm by growing them to micro-sized (3.1 μm) droplets. Our system measured the UFP number concentration with high accuracy (mean difference within 4.1 %), and the number concentration range for which our system can count single particles is 7.99-6850 cm-3. Thus, our system has the potential to be used for UFP monitoring in various environments (e.g., as an air filtration system, in high-precision industries utilizing clean rooms, and in indoor and outdoor atmospheres).

Original languageEnglish
Article numberamt-12-5335-2019
Pages (from-to)5335-5345
Number of pages11
JournalAtmospheric Measurement Techniques
Volume12
Issue number10
DOIs
Publication statusPublished - 2019 Oct 8

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by the Samsung Research Funding & Incubation Center of Samsung Electronics (grant no. SRFC-TA1803-05) and by the Technology Innovation Program (grant no. 10077651, Development of IoT fusion sensor system based on artificial intelligence) funded by the Ministry of Trade, Industry and Energy (MOTIE, Republic of Korea).

Funding Information:
Financial support. This work was supported by the Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-TA1803-05. This material is based upon work supported by the Ministry of Trade, Industry and Energy (MOTIE, Republic of Korea) under Industrial Technology Innovation Program Number 10077651.

Publisher Copyright:
© Author(s) 2019.

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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