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Complex plasma is a state of soft matter where micrometer-sized particles are immersed in a weakly ionized gas. The particles acquire negative charges of the order of several thousand elementary charges in the plasma, and they can form gaseous, liquid and crystalline states. Direct optical observation of individual particles allows to study their dynamics on the kinetic level even in large many-particle systems. Gravity is the dominant force in ground-based experiments, restricting the research to vertically compressed, inhomogeneous clouds, or two-dimensional systems, and masking dynamical processes mediated by weaker forces. An environment with reduced gravity, such as provided on the International Space Station (ISS), is therefore essential to overcome this limitations. We will present the research goals for the next generation complex plasma facility COMPACT to be operated onboard the ISS. COMPACT is envisaged as an international multi-purpose and multi-user facility that gives access to the full three-dimensional kinetic properties of the particles.
AbstractInfrared IR absorption spectra of melamine-formaldehyde (MF) microparticles confined in an rf plasma are studied at different plasma conditions. Several absorption peaks have been analysed in dependence of plasma power and their temporal evolution. For comparison, the IR absorption spectra of heated MF microparticles without plasma exposition are used to determine the general influence of the temperature on the IR spectra. Measuring the temperature of the particles inside the plasma shows that the temperature is not the only process changing the particles’ IR spectra. Chemical changes of the MF particles with increasing plasma power influence the absorption peak structure.
Fast 3D particle reconstruction using a convolutional neural network: application to dusty plasmas
(2021)
AbstractWe present an algorithm to reconstruct the three-dimensional positions of particles in a dense cloud of particles in a dusty plasma using a convolutional neural network. The approach is found to be very fast and yields a relatively high accuracy. In this paper, we describe and examine the approach regarding the particle number and the reconstruction accuracy using synthetic data and experimental data. To show the applicability of the approach the 3D positions of particles in a dense dust cloud in a dusty plasma under weightlessness are reconstructed from stereoscopic camera images using the prescribed neural network.