Cheng-Ran Du

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 this letter, the structural evolution of two approaching plasma crystals is investigated in a binary complex plasma by Langevin dynamics simulations. Each particle cloud is confined by a parabolic potential. As the clouds move towards each other, the number of layers decreases and the particles rearrange in the crystals. As a result, the interfacial order depends strongly on the size and thus the charge of the particles. A well-matched interface exists only when the disparity of the particle size is marginal. These results clarify the evolution of crystalline structure and interfacial matching in binary complex plasmas.

This work investigates a complex plasma in a capacitively coupled discharge operated in push–pull mode. Experiments are combined with particle-in-cell/Monte Carlo collisions (PIC/MCC) simulations under single-frequency (27.12 MHz) and dual-frequency (2 and 27.12 MHz) excitation. In argon at 20 Pa, non-intrusive measurements of electron density and electron temperature are performed using optical-emission spectroscopy coupled with a collisional–radiative model. Under single-frequency 27.12 MHz excitation, both electron density and electron temperature exhibit a symmetric double-peaked distribution. Under dual-frequency excitation, with the 27.12 MHz voltage amplitude held constant, increasing the 2 MHz voltage amplitude promotes electron transport toward the bulk-plasma centre, and the electron-density profile gradually evolves toward an approximately bulk-uniform distribution. These experimental trends are consistent with the PIC/MCC results. The solid SiO ₂ particles of 3.9 µm in diameter are dispensed, which levitate in the sheath above the lower electrode. The levitation height of the bottom of the particle cloud remains stable under single-frequency excitation, the dust layer compresses with increasing voltage and void formation exhibits a clear threshold. Under dual-frequency excitation, the void appears at lower voltages, the overall levitation height increases and rises with the 2 MHz voltage amplitude and the void undergoes stepwise growth. These findings show how discharge parameters shape two-dimensional plasma distributions and dust-cloud structure, providing experimental evidence for controlling complex plasmas in dual-frequency push–pull mode.

The presence of dust particles in plasma introduces distinctive electron kinetic and electrodynamic behaviors, and understanding these effects is crucial for advancing both fundamental plasma properties and practical applications. In this study, the impact of dust particles on electron bounce resonance heating (BRH) and nonlocal electrodynamics is numerically investigated in radio frequency inductively coupled plasmas. As the dust size or number density increases, the BRH manifested by a plateau in the electron energy probability function weakens and eventually disappears owing to the combined effects of modified energy diffusion associated with electron–electron collisions and electron heating, together with the enhanced dust-induced energy losses. The presence of dust particles causes localization of the electric field, current density, and power deposition density near the plasma boundary adjacent to the coil because of the reduced skin depth. Consequently, the peak conductivity shifts from the discharge center toward the boundary. This redistribution significantly weakens the nonlocality between the electric field and the plasma current and suppresses power deposition in the plasma bulk.

The finite-size scaling of the density distribution of a fluid in a nonuniform confining potential can be used to precisely determine whether it undergoes liquid–gas phase separation.

Dust-plasma interactions in complex plasmas driven by dual-frequency capacitively coupled discharges have been investigated using a self-consistent two-dimensional fluid model. The effects of single-frequency driving voltages on dust-cloud spatial distribution are validated by comparing the void size with previous experiment and simulation, achieving a good qualitative consistency. Independent tuning of the high-(27 MHz) and low-frequency (2 MHz) driving voltages enables opposite control of dust-cloud spatial distribution: the high-frequency voltage expands the void by driving the cloud outward, whereas the low-frequency voltage compresses it, leading to void closure. This behavior reflects the balance among the electric-field force, ion-drag force, and self-diffusion of dust. Moreover, a quadrupole vortex pattern in the cloud with large central vortices and counter-rotating edge vortices has been observed and attributed to competition between curls of ion-drag and electric-field forces. Specially, dust-cloud spatial distribution markedly modifies plasma properties via collisional electron absorption, which lowers conductivity, strengthens the drift field, and produces both reversed and amplified ambipolar fields. These changes transform the spatio-temporal ionization pattern from tilted to nearly vertical, resulting in a striated discharge structure.

Nanoparticles grown in a plasma are used to visualize the process of film deposition in a modulated radio-frequency atmospheric pressure glow discharge. Modulation of the plasma makes it possible to successfully prepare porous TiO2 films. We study the trapping of the particles in the sheath during the plasma-on phase. During the plasma-off phase, the particles are driven to the substrate by the electric field generated by their residual charges, leading to the formation of a porous TiO2 film. Using video microscopy, the collective dynamics of particles in the whole process is revealed at the most fundamental “kinetic” level.

The application of an external field often renders empirical criteria for identifying liquid-gas phase transitions ambiguous. Here, we demonstrate that the finite-size scaling of the density profile provides a definitive criterion to distinguish liquid-gas coexistence from a single fluid phase in field-confined systems. Our scaling method collapses the density profiles of different system sizes onto a single master curve for a one-phase system, while causing the profiles to intersect at the interface in a two-phase system. We validate this theoretical proposal through experiments and simulations of two model systems: colloidal suspensions under gravity and/or two-dimensional complex plasmas confined by a central potential. Our method is broadly applicable for detecting liquid-gas phase transitions in laboratory systems where external fields are inherent.

Electrostatic energy harvesting and storage technologies for next-generation wearable devices are typically constrained by slow carrier dynamics and dielectric polarization delays. Here, we developed a plasma-enabled energy textile (PEET) by emulating the carrier transport mechanism in lightning return strokes. By engineering plasma-treated discharge microchannels, our design enables direct conduction current generation through cascading ionization, overcoming the efficiency limitations of conventional polarization-dependent systems. Under 2-hertz mechanical excitation, the PEET achieves a current density of 2.5 amperes per square centimeter, an average power output of 4.46 watts per square meter per hertz, and an energy conversion efficiency of 19%—two orders of magnitude higher than conventional electrostatic energy harvesting technologies, e.g., triboelectric, piezoelectric, and capacitive systems. This work represents a transformative advance in electrostatic energy harvesting, enabling efficient and scalable wearable energy solutions.