No abstract

Self-organization refers to the spontaneous generation of spatially or temporally organized patterns in an otherwise disordered system. Self-organization is ubiquitous in plasma physics particularly in the low-pressure regime as observed in astrophysical jets or plasma loaded flux loops that form on the surface of the Sun. In recent times, self-organization in atmospheric pressure plasmas has captured the attention of researchers. Its occurrence has been observed in DBD discharges as well as DC 1 atm glows with liquid electrodes. The mechanism of pattern formation is still not well understood. Here we briefly review the current understanding of pattern formation in DC glows with liquid anode, surveying past work, application areas, theories on mechanisms of formation from the context of reaction diffusion systems, current experimental work and computational progress towards predicting pattern formation.

Plasma self-organization on anode surfaces in 1 atm DC glow discharges remains poorly understood. This effort aims to elucidate the nature of self-organization through the experimental study of resulting patterns on liquid anode surfaces with 13 different electrolytes and thus improves our understanding of the underlying physical processes that give rise to self-organization by investigating electrolyte sensitivity. Self-organization pattern formation and behavior were studied as a function of discharge current, solution ionic strength, and their chemical property evaluation. The response of the patterns to variation in these parameters was measured using an imaging camera and optical emission spectroscopy. Observed pattern characteristic length scales for all of the electrolytes were ranged from 2 to 13 mm and typically increased with current over the investigated range of 20–80 mA. Complex self-organized pattern structures not reported to date were also observed. The parameters associated with pattern formation and morphology complexity are discussed and summarized.

In plasma–liquid interactions, the phenomenon of induced liquid flow that originates at the plasma–liquid contact point is important in that it influences mass, charge, and heat transport from the source to the surrounding bulk fluid. Such stimulated flows have been observed in 1 atm glows with a liquid anode. Because the plasma contact point in such discharges is patterned, a natural question is what is the relationship between the observed self-organized patterns and the induced flow field? It is, therefore, of great interest to investigate the coupling mechanism between the self-organization patterns in an atmospheric pressure dc helium glow discharge with a liquid anode and the induced flow circulation. Particle imaging velocimetry is used to probe the flow fields in the plane normal and parallel to the plasma–liquid interface. A strong ascending flow with maximum speed up to 1.5 cm/s and circulation vortices nearby are observed in the plane normal to the interface centered at the plasma attachment. The experiment results suggested that the ascending flow is caused by water evaporation and the vortices are formed by viscous stress. With a self-organization pattern formed, the flow structures become non-static and the circulation vortices are observed to periodically form and decay. In the plane parallel to the interface, a strong swirl flow was found to exist only when the plasma attachment is self-organized. The analysis revealed that the driving mechanism could be the electrohydrodynamics force. Averaged flow velocity over time in the field of view was found to scale linearly with increasing input power and increasing liquid conductivity.

Large data sets give rise to the 'fourth paradigm' of scientific discovery and technology development, extending other approaches based on human intuition, fundamental laws of physics, statistics and intense computation. Both experimental and simulation data are growing explosively in plasma science and technology, motivating data-driven discoveries and inventions, which are currently in infancy. Here we describe recent progress in microparticle cloud imaging and tracking (mCIT, µCIT) for laboratory plasma experiments. Three types of microparticle clouds are described: from exploding wires, in dusty plasmas and in atmospheric plasmas. The experimental data sets are obtained with one or more imaging cameras at a rate up to 100k frames per second (fps). Analyses of the time-dependent microparticle trajectories give time-dependent two-dimensional or three-dimensional information about the particle motion and ambient environment. The massive image and particle track data motivate development of machine-learning (ML) techniques for information extraction. A physicsconstrained motion tracker, a Kohonen neural network (KNN) or self-organizing map (SOM), the feature tracking kit (FTK), and U-Net are described and compared with each other for particle tracking using the datasets. Particle density and signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform (FFT) is used to reveal how U-Net, a deep convolutional neural network (CNN) developed for non-plasma applications, achieves the improvements for noisy scenes. The fitting parameters for a simple polynomial track model are found to group into clusters that reveal the geometry information about the camera setup. The mCIT or µCIT techniques, when enhanced with data models, can be used to study the microparticle-or Debye-length scale plasma physics. The datasets are also available for ML code development and comparisons of algorithms.