Measuring number, mass, and size of exhaust particles with diffusion chargers: The dual Pegasor Particle Sensor

2016 
Abstract The Pegasor Particle Sensor (PPS) is an aerosol measurement instrument based on diffusion charging and electrical detection of the charge acquired by particles as these escape the electrically isolated body of the device. PPS utilizes an ion trap to collect free ions which failed to attach to particles. This escaping current produced is proportional to particle concentration. Apart from free ions, ion trap field intensity modulation may be used to collect a fraction of charged particles, which presents the opportunity to use PPS as a size selective sensor. This study first demonstrates a dual PPS concept capable of simultaneously measuring exhaust particle mean size, number and mass concentrations. Two PPS units were combined at different ion trap voltage, so that each sensor detected a different fraction of the particle size distribution. The difference between the two signals can be related to mean particle size, which allows a more precise estimation of particle number and mass concentrations. The parameters required for the data inversion algorithm, such as charging efficiency and ion trap penetration as a function of size and distribution width, were determined with soot particle calibration for each sensor. The sensitivity of the algorithm to input uncertainties such as distribution width and sample inlet flowrate was examined, with the latter having the larger impact. The application of the dual PPS with polydisperse soot as well as diesel exhaust demonstrates a good agreement with reference instruments for particle size, number and mass. The dual PPS offers significant improvement over the single PPS which is based on a fixed calibration. The prototype concept presented here can likely be optimized by using a single unit with two integrated measuring cells instead of two separate units, along with continuously measuring the inlet flowrate.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    30
    References
    20
    Citations
    NaN
    KQI
    []