Thesis supervisor: Attila Kohut
Location of studies (in Hungarian): University of Szeged Abbreviation of location of studies: SZTE
Description of the research topic:
In the so-called spark discharge nanoparticle generators (SDG) several microseconds long, high-voltage, high-current spark discharges are initiated between two electrodes in an atmospheric pressure gas. Due to the spark plasma, material is removed from the electrodes which leads to nanoparticle (NP) formation in the gas phase. In a new-generation SDG, the spark energy, the relative erosion of the electrodes and the spark repetition rate can be controlled real-time via computer algorithms. Since the above parameters play vital role in determining the properties of the generated NPs, their automated, programmed variation has great potential in tuning the particle properties. The research aims at exploring the achievable size- and composition range of multicomponent plasmonic nanoparticles (e.g. the combination of Au, Ag, Cu) generated in a novel SDG and investigating the possibility of a real-time control over these properties. We will also assess the application of machine learning algorithms to set up a framework for semi-automatic NP generations with predefined properties. The potential use of first principle computational methods to predict the properties of the generated particles will also be explored.
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