Thesis by Adrien Reynaud*
To protect public health, fine particle emissions produced by IC engines have been regulated by the European Union since the 1990s. In order to comply with these standards, particle filters are fitted to the exhaust systems of the vehicles concerned. To ensure they operate effectively, resistive sensors, which are both robust and inexpensive, are excellent candidates. This type of sensor measures the conductance of a particle deposit, forming a kind of bridge between two electrodes (figure). It can currently be used only to estimate a concentration in terms of mass per unit volume. But existing standards also govern the concentration in terms of number, which is a better indicator of the harmful effect of particles on health, taking greater account of ultrafine particles.
As a pre-requisite to the development of a sensor capable of detecting the number, this thesis project set out to gain a better understanding of the mechanisms behind particle deposits, including the finest. Those contained in exhaust gases were first of all categorized in terms of their size using two experimental techniquesa with a view to only sending the finest among them to the sensor.
As a result, it was possible to show that bridge construction also occurred with ultrafine particles (50 nm)(1). A numerical simulation analysis then highlighted a physical phenomenon hitherto not covered in the literature in the field, namely dielectrophoresis, which makes it possible to better explain mechanisms governing the construction of soot microstructures as a function of particle size and the principle of which could also be used to develop a sensor capable of detecting the finest particles.
These results represent a significant advance to better exploit the resistive sensor signal, with a view to obtaining information about the concentration in terms of number of ultrafine soot particles.
*Thesis entitled "Understanding and modeling of aerosol particle collection mechanisms via the combination of aerodynamic and electrical phenomena"
a - Electrostatic classification and aerodynamic classification
(1) A. Reynaud, M. Leblanc, S. Zinola, P. Breuil, J.-P. Viricelle, 2019, Sensors 19.
DOI : 10.3390/s19030705
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