Hubertus Maria Thomas

Dusty plasmas, ubiquitous throughout the universe, may be found in laboratory and industrial plasmas, nuclear fusion devices, Earth's atmosphere and ionosphere, the lunar surface, planetary rings, cometary tails, and the interstellar and intergalactic media. Despite decades of experimental, theoretical, and computational research, many aspects of dusty plasmas remain poorly understood within a unified predictive framework, i.e., the Standard Model for dusty plasmas. Existing first-principles models based on particle, kinetic, fluid, and compound descriptions have successfully explained numerous phenomena, including dust charging, transport, waves, and self-organization. However, predictions remain challenging because of the inherent multiscale and multiphysics nature of dusty plasmas; computational complexity and other computing limitations; and incomplete knowledge of material properties, transport coefficients, and boundary conditions. Recent advances in machine learning (ML) and artificial intelligence (AI) provide new opportunities to complement traditional physics and first-principles modeling through data-driven scientific inference. This paper surveys current ML and AI applications in dusty plasma research, including microparticle imaging and tracking, image analysis, surrogate modeling, physics-informed learning, geometric learning, uncertainty quantification, and multimodal data fusion. We then introduce Dust Neural nEtworks Technology (DustNET), a community-oriented data infrastructure and benchmark ecosystem designed to support curated dusty plasma datasets spanning laboratory experiments, industrial plasmas, planetary, space, and astrophysical observations, numerical simulations, and AI-driven methods. We further discuss the Dusty-plasma Unified Sensing, Testing and Multi-modal Analysis Platform (DUST-MAP), a proposed member of dusty plasma world models that learn unified representations from heterogeneous dusty plasma data. By integrating experiments, measurements, simulations, scientific data and metadata, physical constraints and other regularizations within a common representation-learning framework, DUST-MAP aims to support universal inference and predictions across heterogeneous instruments, experimental facilities, and natural dusty plasmas. Toward the end, we present a phased community roadmap for the co-evolution of DustNET and DUST-MAP, emphasizing reproducible benchmarking, AI agents, and edge intelligence toward an AI-enabled ecosystem for dusty plasma science, with scalable and data-driven scientific intelligence across laboratory, industrial, planetary, space, and astrophysical dusty plasma environments.

In dense colloids and simulated glasses, it has been discovered that plastic particle rearrangements correlate with nonphononic low-frequency vibrational modes. Here, we demonstrate that this correlation also holds for a very different material class of complex plasmas under shear, characterized by long-range interactions and nonreciprocal forces. We perform experiments with a deformed amorphous quasi-two-dimensional binary complex plasma under optical pressure, which gives rise to a controlled shear rate, and confirm our findings with extensive particle-resolved computer simulations.

In the presence of gravity, the micrometer-sized charged dust particles in a complex (dusty) plasma are compressed into thin layers. However, under the microgravity conditions of the Plasma Kristall-4 (PK-4) experiment on the International Space Station (ISS), the particles fill the plasma, allowing us to investigate the properties of a three-dimensional multi-particle system. This paper examines the change in the spatial ordering and thermal state of the particle system created when dust particles are stopped by periodic oscillations of the electric field, known as polarity switching, in a dc glow discharge plasma. Data from the ISS are compared against experiments performed using a ground-based reference version of PK-4 and numerical simulations. Initial results show substantive differences in the velocity distribution functions between experiments on the ground and in microgravity. There are also differences in the motion of the dust cloud; in microgravity, there is an expansion of the dust cloud at the application of polarity switching, which is not seen in the ground-based experiments. It is proposed that the dust cloud in microgravity gains thermal energy at the application of polarity switching due to this expansion. Simulation results suggest that this may be due to a modification in the effective screening length at the onset of polarity switching, which allows the dust particles to utilize energy from the potential energy in the configuration of the dust cloud. Experimental measurements and simulations show that an extended time (much greater than the Epstein drag decay) is required to dissipate this energy.

Platform chemicals from renewable resources with broad applications are highly desirable, particularly for replacing fossil‐based monomers. Bifunctional aliphatic ester‐aldehydes, accessible via regioselective hydroformylation of unsaturated oleochemicals, can be converted into linear ω‐amino/ω‐hydroxy esters and dicarboxylic acids—key building blocks for biobased aliphatic polycondensates. However, their success hinges on efficient, economically viable production, with catalyst recycling being critical. We present the Rh‐catalyzed, cyclodextrin‐mediated, aqueous biphasic hydroformylation of methyl 10‐undecenoate (from castor oil) and methyl 9‐decenoate (from rapeseed oil) to produce methyl 12‐oxododecanoate and methyl 11‐oxoundecanoate, respectively, with high yields and productivity. This system allows for efficient catalyst recycling via decantation, maintaining 30 % of its native activity in aqueous biphasic conditions. Reaction conditions were optimized using a tailored experimental design, reducing nearly 200 experiments to 39 without sacrificing predictive accuracy. The optimized conditions were transferred to a continuous miniplant, achieving a low rhodium loss of 0.018 % h⁻¹, with excellent space‐time yields of 76.5 kg h⁻¹ m⁻³. Rhodium in the product was as low as 79 ppb, with 4.4 kg of product per mg of catalyst lost, marking a significant step in combining hydroformylation‐derived, bio‐based platform chemicals with economic industrial potential.

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.

Complex plasmas, i.e., low-temperature plasmas containing suspensions of solid microparticles, exhibit electrorheological properties which are manifested by the formation of stringlike clusters (SLCs) in microgravity experiments. It is thought that SLCs form due to a long-range effective attraction between the particles under the influence of a directed ion flow. We performed molecular dynamics (MD) simulations of negatively charged microparticles with positive model wakes to mimic the effect of ion flow in experiments, and achieved SLC formation without long-range attraction between the microparticles. We show that long-range-reduced repulsion was enough to obtain the SLCs similar to the experiments and found that the simulations with the long-range attraction became unstable due to particle accelerations. Destruction and recrystallization of the stringlike structure was also studied experimentally using the Plasmakristall-4 facility on board the International Space Station, and the experimental findings were compared to those from three–dimensional MD simulations. We found excellent qualitative agreement between simulation and experiment when recrystallization was simulated using an interparticle potential without effective long-range attraction.

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.

Machine learning is applied to investigate the phase transition of two-dimensional complex plasmas. The Langevin dynamics simulation is employed to prepare particle suspensions in various thermodynamic states. Based on the resulted particle positions in two extreme conditions, bitmap images are synthesized and imported to a convolutional neural network (ConvNet) as a training sample. As a result, a phase diagram is obtained. This trained ConvNet model has been directly applied to the sequence of the recorded images using video microscopy in the experiments to study the melting.

Self-organization of dust grains into stable filamentary dust structures (or “chains”) largely depends on dynamic interactions between individual charged dust grains and complex electric potential arising from the distribution of charges within a local plasma environment. Recent studies have shown that the positive column of the gas discharge plasma in the Plasmakristall-4 (PK-4) experiment at the International Space Station supports the presence of fast-moving ionization waves, which lead to variations of plasma parameters by up to an order of magnitude from the average background values. The highly variable environment resulting from ionization waves may have interesting implications for the dynamics and self-organization of dust particles, particularly concerning the formation and stability of dust chains. Here, we investigate the electric potential surrounding dust chains in the PK-4 experiment by employing a molecular dynamics model of the dust and ions with boundary conditions supplied by a particle-in-cell with Monte Carlo collision simulation of the ionization waves. The model is used to examine the effects of the plasma conditions within different regions of the ionization wave and compare the resulting dust structure to that obtained by employing the time-averaged plasma conditions. The comparison between simulated dust chains and experimental data from the PK-4 experiment shows that the time-averaged plasma conditions do not accurately reproduce observed results for dust behavior, indicating that more careful treatment of plasma conditions in the presence of ionization waves is required. It is further shown that commonly used analytic forms of the electric potential do not accurately describe the electric potential near charged dust grains under these plasma conditions.