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Transmission of charged nanoparticles through the DMA adverse axial electric field and its improvement

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journal contribution
posted on 2019-09-27, 14:25 authored by Runlong Cai, Yixian Zhou, Jingkun Jiang

Charged particles can be classified according to their electrical mobility using electrical methods. These particles are often transported against an adverse electric field from a region of high electric potential to a grounded region, e.g., in the aerosol sample outlet of a differential mobility analyzer (DMA). Electrostatic losses due to the adverse electric field can be reduced using a tube made of electrostatic dissipative (ESD) materials. The transmission of charged particles through an adverse axial electric field inside the ESD tube is studied considering particle losses due to electrostatic migration and Brownian motion. The electric field inside the ESD tube is solved analytically. Assuming Hagen-Poiseuille flow, plug flow, or turbulent flow, the transmission efficiency of the charged particles is evaluated using both a simplified analytical model and Monte-Carlo simulation. Transmission efficiencies of 1.48-nm ions are measured at various flow rates and for various tube lengths. The measured transmission efficiencies agree with the results from both the analytical model and Monte-Carlo simulation. The ideal tube length for relatively high transmission efficiencies is discussed. Both the analytical model and Monte-Carlo simulation show that the recommended tube length for the test DMA is longer than a threshold value corresponding to an adverse particle electrostatic migration velocity of less than ∼20% of the average air flow velocity. Based on these findings, the sample outlet of a miniature cylindrical DMA is improved using an ESD tube. The measured penetration efficiency of 1.48-nm ions at a sheath flow rate of 25 L min−1 and an aerosol flow rate of 1.5 L min−1 is improved by 50%.

Copyright © 2019 American Association for Aerosol Research

Funding

Financial supports from National Key R&D Program of China (2017YFC0209503), the National Natural Science Foundation of China (21876094), and State Key Joint Laboratory of Environment Simulation and Pollution Control (17K10ESPCT) are acknowledged.

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