Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. Despite decades of research, many aspects of their behavior remain poorly understood within a unified framework. While numerous theoretical and numerical models describe specific phenomena, such as dust charging, transport, waves, and self-organization, fully predictive models across the wide range of spatial and temporal scales in both laboratory and natural systems remain elusive. Conventional plasma descriptions rely on coupled differential equations for particle densities, momenta, and energies, but their solutions are often limited by computational cost, numerical uncertainties, and incomplete knowledge of boundary conditions and transport processes. Recent advances in machine learning (ML), particularly deep neural networks, offer new opportunities to complement traditional physics-based modeling. Here we review ML and artificial intelligence (AI) approaches, termed bottom-up data-driven methods, for dusty plasma research. Central to this effort is Dust Neural nEtworks Technology (DustNET), a community-driven dataset initiative inspired by ImageNet, integrating experimental, simulation, and synthetic data to enable predictive modeling, uncertainty quantification, and multi-scale analysis. DustNET-trained models may also be deployed in real-time experimental settings under edge computing constraints. Combined with emerging multi-modal AI foundation models and autonomous agents, this framework provides a pathway toward a unified, physics-informed understanding of dusty plasmas across laboratory, industrial, space, and astrophysical environments.

The dynamical properties of two types of metal-coated dust microspheres confined in a gaseous plasma environment have been studied. It has been observed that such particles feature properties of active particles and that the particle activity changes over time and increases with the intensity of laser illumination. The dynamics of single particles as well as of small particle clusters have been analyzed quantitatively with various statistical methods to characterize their activity. Furthermore, clusters of metal-coated microspheres are observed to undergo a phase transition from an ordered to an unordered structure under increased laser illumination intensity due to the increased active motion of the particles.

We have performed experiments on dusty plasmas under the weightlessness conditions of parabolic flights where the dust particles form an extended homogeneous dust cloud. The three-dimensional (3D) dynamic state of the dust cloud is characterized. Therefore, the particle trajectories have been recorded using a four-camera stereoscopic camera system. From that, the 3D particle trajectories have been determined using both a machine-learning particle reconstruction technique and the deterministic shake-the-box algorithm. From the trajectories, characteristic fluid parameters, such as flow fields and finite-time Lyapunov exponent (FTLE)-based fluid structures have been calculated and analyzed. The FTLE analysis indicates that the fluid is characterized by an incompressible flow with small-scale behavior. Furthermore, it is demonstrated that the machine-learning based approach allows to reliably characterize the dynamic states by comparison with the shake-the-box algorithm.

Dusty plasmas are electrically quasi-neutral media that, along with electrons, ions, neutral gas, radiation, and electric and/or magnetic fields, also contain solid or liquid particles with sizes ranging from a few nanometers to a few micrometers. These media can be found in many natural environments as well as in various laboratory setups and industrial applications. As a separate branch of plasma physics, the field of dusty plasma physics was born in the beginning of 1990s at the intersection of the interests of the communities investigating astrophysical and technological plasmas. An additional boost to the development of the field was given by the discovery of plasma crystals leading to a series of microgravity experiments of which the purpose was to investigate generic phenomena in condensed matter physics using strongly coupled complex (dusty) plasmas as model systems. Finally, the field has gained an increasing amount of attention due to its inevitable connection to the development of novel applications ranging from the synthesis of functional nanoparticles to nuclear fusion and from particle sensing and diagnostics to nano-contamination control. The purpose of the present perspectives paper is to identify promising new developments and research directions for the field. As such, dusty plasmas are considered in their entire variety: from classical low-pressure noble-gas dusty discharges to atmospheric pressure plasmas with aerosols and from rarefied astrophysical plasmas to dense plasmas in nuclear fusion devices. Both fundamental and application aspects are covered.

We have set up a diagnostic magnet (D-Mag) laboratory for a wide range of applications in plasma physics. It consists of a superconducting magnet for field strengths of up to 5.9 T. The main purpose is to provide an experimental environment for the development of plasma diagnostics for nuclear fusion studies and the investigation of dusty plasmas in strong magnetic fields. We describe in the article the setup and operation of the D-Mag. Some applications are presented for the development of plasma diagnostics, such as neutral pressure gauges and Langmuir probes that have to be operated in strong magnetic fields. Among the examples is the test of the long-pulse capability and stability of the diagnostic pressure gauge (DPG) for the ITER device.

The future of complex plasma research under microgravity condition, in particular on the International Space Station ISS, is discussed. First, the importance of this research and the benefit of microgravity investigations are summarized. Next, the key knowledge gaps, which could be topics of future microgravity research are identified. Here not only fundamental aspects are proposed but also important applications for lunar exploration as well as artificial intelligence technology are discussed. Finally, short, middle and long-term recommendations for complex plasma research under microgravity are given.

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.

Under the weightlessness conditions of parabolic flights, phase separation processes in a binary mixture of dust particles in a dusty plasma have been experimentally studied using a polarization-sensitive camera. The camera allows a pixel-wise derivation of the polarization state of the light scattered by the dust. With this, the light scattered from individual particles in a dust cloud consisting of dust particles with two different sizes has been analyzed. By comparison with analytical calculations of the scattered light polarizations, a technique has been developed to discriminate between the two different species, even for small size disparities. With that, the dynamics of the phase separation process of the two species is followed over the entire parabola and it is found that the larger particles preferably assemble on the outer parts of the dust cloud whereas the smaller particles accumulate on the inner parts.

We 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.

We have performed measurements of the ir absorption of SiO_{2} nanoparticles confined in an argon radiofrequency plasma discharge using a Fourier transform infrared spectrometer. By varying the gas pressure of the discharge and duty cycle of the applied radiofrequency voltage, we observed a shift of the absorption peak of SiO_{2}. We attributed this shift to charge-dependent absorption features of SiO_{2}. The charge-dependent shift has been calculated for SiO_{2} particles, and from comparisons with the experiment the particle charge has been retrieved using our infrared phonon resonance shift method. With the two different approaches of changing the gas pressure and altering the duty cycle, we are able to deduce a relative change of the particle charge with pressure variations and an absolute estimate of the charge with the duty cycle.

Molecular dynamics simulations of binary dusty plasmas have been performed and their behavior with respect to the phase separation process has been analyzed. The simulated system was inspired by experimental research on phase separation in dusty plasmas under microgravity on parabolic flights. Despite vortex formation in the experiment and in the simulations the phase separation could be identified. From the simulations it is found that even the smallest charge disparities lead to phase separation. The separation is due to the force imbalance on the two species and the separation becomes weaker with increasing mean particle size. In comparison, experiments on the phase separation have been performed and analyzed in view of the separation dynamics. It is found that the experimental results are reproduced by the simulation regarding the dependency on the size disparity of the two particle species.

Infrared 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.

In this review, we summarize recent advances in the field of dusty plasmas at strong magnetic fields. Special emphasis is put on situations where experimental laboratory observations are available. These generally comprise dusty plasmas with magnetized electrons and ions, but unmagnetized dust. The fundamental parameters characterizing a magnetized (dusty) plasma are given and various effects in dusty plasmas under magnetic fields are presented. As examples, the reaction of the dust component to magnetic-field modified plasma properties, such as filamentation, imposed structures, dust rotation, nanodusty plasmas and the resulting forces on the dust are discussed. Further, the behavior of the dust charge is described and shown to be relatively unaffected by magnetic fields. Wake field formation in magnetized discharges is illustrated: the strength of the wake field is found to be reduced with increased magnetic field. The propagation of dust acoustic waves in magnetized dusty plasmas is experimentally measured and analyzed indicating that the wave dynamics are not heavily influenced by the magnetic field. Only at the highest fields ( $$B> 1$$  T) the wave activity is found to be reduced. Moreover, it is discussed how dust-cyclotron waves might be used to indicate a magnetized dust component. Finally, implications of a magnetized dusty plasma are illustrated.

The temperature of a dust ensemble in a dusty plasma is one of its most fundamental properties. Here, we present experiments using the configurational temperature as a for the temperature analysis in dusty plasmas. Using a model of the particle interactions, the configurational temperature allows us to determine the temperature of the dust ensemble from measurements of the particle positions, rather than particle velocities. The basic concept will be presented and the technique is applied to two-dimensional finite clusters as well as three-dimensional data from an extended dust cloud. Additionally, the configurational temperature can be used to derive the particle charge and the screening length from a comparison with the standard kinetic temperature.

In a plasma containing micrometer-size dust particles, nonlinear wave synchronization was investigated experimentally under microgravity conditions on board the International Space Station (ISS). These dust particles were confined into a 3-D cloud, in the vicinity of a diffuse edge of the plasma, which was generated by an inductively coupled radio frequency (RF) glow discharge. A cross-sectional slab of the cloud was imaged using a video camera. The dust-density fluctuations in the slab were characterized using the video image data. A steady and long-lived dust acoustic wave (DAW) was observed to be spontaneously generated in the cloud; it propagated through the dust cloud, which had a gradually varying density distribution. Two kinds of spectral analyses of the wave motion were performed, using Fourier transforms and Hilbert transforms, respectively; these revealed two distinctive spatial domains in the cloud, termed frequency clusters. Within each cluster, waves were found to oscillate at a dominant frequency that remained constant, manifesting mutual synchronization throughout the cluster. Across the two clusters, the dominant frequency exhibited a step-wise change, with a frequency ratio of 2:1, which is consistent with phase-lock conditions for a harmonic synchronization state.

This corrects the article DOI: 10.1103/PhysRevE.99.063203.

Three-dimensionally extended dusty plasmas containing mixtures of two particle species of different size have been investigated under microgravity conditions. To distinguish the species even at small size disparities, one of the species is marked with a fluorescent dye, and a modified two-camera video microscopy setup is used for position determination and tracking. Phase separation is found even when the size disparity is below 5%. Particles are tracked to obtain the diffusion flux, and resulting diffusion coefficients are calculated to be about -10^{-6}mm^{2}/s, which is in the expected range for a phase separation process driven by plasma forces. Additionally, a measure for the strength of the phase separation is presented that allows us to quickly characterize measurements. There is a clear correlation between size disparity and phase separation strength.