Plasma System Research Articles

Long-lived RONS effects on plasma-activated water physicochemical properties

Abstract Plasma-activated water (PAW) is enriched with reactive oxygen and nitrogen species (RONS), which significantly alter its physicochemical properties and expand its applicability in fields like materials science, biomedicine, and agriculture. This study investigates the specific contributions of key long-lived RONS—hydrogen peroxide (H₂O₂), nitrate ions (NO₃⁻), and ozone (O₃)—to the physicochemical properties of PAW. PAW was produced using a pin-to-liquid plasma system, and its properties were characterized using UV-Vis spectroscopy, Raman spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and measurements of pH, electrical conductivity, total dissolved solids (TDS), and oxidation-reduction potential (ORP). To isolate the effects of individual RONS, aqueous solutions containing H₂O₂, NO₃⁻, O₃, and a composite solution with the three species at concentrations equivalent to those measured in PAW were prepared and analyzed using the same characterization techniques. The individual RONS solutions revealed specific influences on the physicochemical parameters: H₂O₂ led to slight acidification and increased conductivity; NO₃⁻ significantly increased conductivity and TDS; and O₃ had minimal effect on the measured properties. The composite solution is the only one that has a positive ORP; it is an oxidant. However, none of the individual solutions replicated the comprehensive alterations observed in PAW. The composite solution containing all three RONS showed more pronounced changes but still did not fully match PAW's properties. This difference hints to the presence of unidentified long-lived reactive species in PAW, possibly originating from electrode degradation, as spectroscopic analyses indicate. Understanding the individual and combined effects of long-lived RONS is crucial for optimizing PAW generation and tailoring its properties for specific applications.

Self-Gravitational Shock Potential in Degenerate Quantum Plasmas

A rigorous theoretical investigation has been made on the propagation of nonlinear self-gravito-acoustic shock structures (SGASSs) in a quantum plasma system consisting of non-inertial degenerate non-relativistic electron species and inertial non-degenerate heavy nucleus species. The nonlinear behaviors for this self-gravitational perturbation (SGP) mode in planar geometry has been examined. The Burgers equation has been derived by employing the standard reductive perturbation technique. To analyze the Burgers equation numerically, the solution of the Burgers equation has been obtained for stationary localized condition. The dissipative force, which is effective on heavy elements, plays vital role for the formation of SGASSs in the plasma system under consideration. The non-relativistic limit has great effect on the basic properties (amplitude, width, etc.) of the SGASSs. The obtained results are applicable in white dwarfs and neutron stars which are the most common examples of astrophysical compact objects. Journal of Engineering Science 16(1), 2025, 31-36

Nonlinear evolution of dust-acoustic envelope solitons in electron-depleted dusty plasmas with non-thermal ions

A theoretical investigation has been conducted on the modulation dynamics of dust-acoustic envelope solitons (DAES) and also on their modulational instability criteria in an unmagnetized, electron-depleted dusty plasma system containing inertial positively charged as well as negatively charged dust species and non-inertial positive ions following a non-thermal Cairns distribution function. The nonlinear Schrödinger equation, which describes the formation and stability/instability of envelope modes, is derived via the reductive perturbation approach. The formation of DAES and the occurrence of instability conditions of DAES in the proposed plasma system are analyzed numerically. It is found that the stability and the instability regimes for the propagation of DAES are dependent on the sign of the nonlinear coefficient. It is examined that the fundamental characteristics of DAES, including the modulation instability conditions of the wave profile and the formation of DAES, are significantly influenced by various plasma parameters. These parameters encompass factors such as the mass and charge state of the dust, as well as the number density and temperature of the ions. The results of our present study are expected to contribute significantly to the comprehension of localized envelope modes in various astrophysical contexts [e.g., the Van Allen radiation belt, the solar atmosphere, the D-region (H+,O2−) and F-region (H+, H−) of Earth’s ionosphere, the upper atmosphere of Titan, etc.] and laboratory settings (e.g., plasma processing reactors, the semiconductor industry, intense laser fields, tokamaks, etc.) where the plasma systems comprise both negatively and positively charged massive dust grains alongside non-thermally distributed positive ions.

Random forest method for predicting discharge current waveform and mode of dielectric barrier discharges

This study addresses the classification of Homogeneous and Filamentary discharge modes in dielectric barrier discharge (DBD) systems and predicts the Homogeneous current waveform using machine learning (ML). The motivation stems from the need for accurate modelling in non-thermal plasma systems. The problem tackled is distinguishing between these two modes and predicting the current waveform for Homogeneous discharge. A random forest classification algorithm is applied, using experimental features such as applied voltage, frequency, gas gap, dielectric material, and gas type. An exponential model is proposed for the discharge current, with Gaussian regression transforming the model’s parameters. The classification results are evaluated through a confusion matrix, showcasing 80% accuracy in distinguishing discharge modes. The regression analysis reveals strong Pearson correlation coefficients between predicted and experimental waveforms. In conclusion, the results demonstrate the efficacy of ML techniques in enhancing DBD system modelling, though improvements can be made by expanding the dataset and refining feature selection for better classification and prediction performance.

Regulating generation of radical and non-radical in plasma systems for selective degradation of persistent organic pollutants in water with high salinity resistance and slight environmental implication: Regulatory method and selective mechanism.

Regulating generation of radical and non-radical in plasma systems for selective degradation of persistent organic pollutants in water with high salinity resistance and slight environmental implication: Regulatory method and selective mechanism.

A Comprehensive Review of Plasma Cleaning Processes Used in Semiconductor Packaging

Semiconductor device fabrication is conducted through highly precise manufacturing processes. An essential component of the semiconductor package is the lead frame on which the silicon dies are assembled. Impurities such as oxides or organic matter on the surfaces have an impact on the process yield. Plasma cleaning is a vital process in semiconductor manufacturing, employed to enhance production yield through precise and efficient surface preparation essential for device fabrication. This paper explores the various facets of plasma cleaning, with a particular emphasis on its application in the cleaning of lead frames used in semiconductor packaging. To provide comprehensive context, this paper also reviews the critical role of plasma in advanced and emerging packaging technologies. This study investigates the fundamental physics governing plasma generation, the design of plasma systems, and the composition of the plasma medium. A central focus of this work is the comparative analysis of different plasma systems in terms of their effectiveness in removing organic contaminants and oxide residues from substrate surfaces. By utilizing reactive species generated within the plasma—such as oxygen radicals, hydrogen ions, and other chemically active constituents—these systems enable a non-contact, damage-free cleaning method that offers significant advantages over conventional wet chemical processes. Additionally, the role of non-reactive species, such as argon, in sputtering processes for surface preparation is examined. Sputtering is the ejection of individual atoms from a target surface due to momentum transfer from an energetic particle (usually an ion). Sputtering is therefore a physical process driven by momentum transfer. Energetic ions, such as argon (<!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ -->

Multiscale autonomous forecasting of plasma systems’ dynamics using neural networks

Abstract Plasma systems exhibit complex multiscale dynamics, resolving which poses significant challenges for conventional numerical simulations. Machine learning (ML) offers an alternative by learning data-driven representations of these dynamics. Yet existing ML time-stepping models suffer from error accumulation, instability, and limited long-term forecasting horizons. This paper demonstrates the application of a hierarchical multiscale neural network architecture for autonomous plasma forecasting. The framework integrates multiple neural networks trained across different temporal scales to capture both fine-scale and large-scale behaviors while mitigating compounding error in recursive evaluation. By structuring the model as a hierarchy of sub-networks, each trained at a distinct time resolution, the approach effectively balances short-term resolution with long-term stability. Fine-scale networks accurately resolve fast-evolving features, while coarse-scale networks provide broader temporal context, reducing the frequency of recursive updates and limiting the accumulation of small prediction errors over time. We first evaluate the method using canonical nonlinear dynamical systems and compare its performance against classical single-scale neural networks. The results demonstrate that single-scale neural networks experience rapid divergence due to recursive error accumulation, whereas the multiscale approach improves stability and extends prediction horizons. Next, our ML model is applied to two plasma configurations of high scientific and applied significance, demonstrating its ability to preserve spatial structures and capture multiscale plasma dynamics. By leveraging multiple time-stepping resolutions, the applied framework is shown to outperform conventional single-scale networks for the studied plasma test cases. Additionally, another great advantage of our approach is its parallelizability by design, which enables the development of computationally efficient forecasters. The results of this work position the hierarchical multiscale neural network as a promising tool for efficient plasma forecasting and digital twin applications.

α Effect and Magnetic Diffusivity β in Helical Plasma Under Turbulence Growth

We investigate the transport coefficients α and β in plasma systems with varying Reynolds numbers while maintaining a unit magnetic Prandtl number (PrM). The α and β tensors parameterize the turbulent electromotive force (EMF) in terms of the large-scale magnetic field B¯ and current density as follows: ⟨u×b⟩=αB¯−β∇×B¯. In astrophysical plasmas, high fluid Reynolds numbers (Re) and magnetic Reynolds numbers (ReM) drive turbulence, where Re governs flow dynamics and ReM controls magnetic field evolution. The coefficients αsemi and βsemi are obtained from large-scale magnetic field data as estimates of the α and β tensors, while βtheo is derived from turbulent kinetic energy data. The reconstructed large-scale field B¯ agrees with simulations, confirming consistency among α, β, and B¯ in weakly nonlinear regimes. This highlights the need to incorporate magnetic effects under strong nonlinearity. To clarify α and β, we introduce a field structure model, identifying α as the electrodynamic induction effect and β as the fluid-like diffusion effect. The agreement between our method and direct simulations suggests that plasma turbulence and magnetic interactions can be analyzed using fundamental physical quantities. Moreover, αsemi and βsemi, which successfully reproduce the numerically obtained magnetic field, provide a benchmark for future theoretical studies.

Deep learning-based spatiotemporal sequence forecasting of physical fields in tin droplet laser-produced plasma

To address the computational challenges in modeling laser-produced plasma spatiotemporal evolution, this study pioneers the application of neural operators for 2D radiation hydrodynamics (RHD) simulations in fiber-laser-produced plasma systems employing liquid tin droplets for extreme ultraviolet lithography (EUVL) sources. Our novel framework enables rapid prediction of multi-physics field evolution by learning the underlying physical operators governing the complex interplay between radiation transport, hydrodynamic motion and plasma dynamics in EUV light source configurations. Through comparative analysis with convolutional long short-term memory (ConvLSTM) and convolutional neural operator (CNO) architectures, using over 50,000 spatiotemporal snapshots generated by FLASH software, the multi-variable Fourier neural operator (FNO) demonstrates superior performance in all three cases. In the case of single-laser pulse scenarios, it achieves an electron density mean squared error (MSE) of 7.49×10−5, representing a 53% improvement over ConvLSTM (1.58×10−4) and a 50% improvement over the CNO (1.51×10−4) in the normalized domain. The FNO exhibits unique zero-shot super-resolution capabilities, reconstructing high-fidelity 96×192 grid solutions from low-resolution 48×96 inputs while maintaining a normalized MSE of 10−4 relative to ground truth simulations. Demonstrating six-order-of-magnitude acceleration compared to conventional RHD solvers, this approach enables real-time analysis of plasma evolution patterns critical for EUVL source optimization, including tin droplet fragmentation dynamics and extreme ultraviolet emission characteristics. The demonstrated multi-physics modeling capability and memory-efficient super-resolution reconstruction positions FNO as a potential transformative tool for next-generation plasma diagnostics and EUVL system monitoring.

Effect of Combined Kappa–Cairns distributed electrons on ion-acoustic solitary structures in electron–ion dusty plasma

Abstract We have studied the formation of arbitrary amplitude ion-acoustic solitary structures using the Sagdeev pseudo-potential approach in an unmagnetized dusty plasma system in the absence of collision whose constituents are Combined Kappa–Cairns distributed electrons, negative-charged stationary dust particulates, and adiabatic warm ions. This system supports both positive and negative potential conventional solitons along with the coexistence of these two solitons, double layers, and supersolitons of negative polarity. Different types of solitary structures along with the impacts of the plasma parameters, viz., Mach number, superthermal parameter, and nonthermal parameter, of this system on the amplitudes of these structures have been investigated by plotting the pseudo-potential with respect to the electrostatic potential. The formation of supersoliton after the formation of soliton has been illustrated using the mechanical analogy of phase portraits. The transitions of soliton to supersoliton and soliton to double layer have been illustrated.

Characterization of H2 plasma within RF-powered hollow cathode discharge equipped with hybrid multicusp magnetic field under varying inner-ring magnetic strength

This study investigates hydrogen plasma behavior in an RF-powered hollow cathode discharge system equipped with hybrid multicusp magnetic fields featuring varied inner-ring magnetic strengths. Two configurations—configuration A (moderate strength) and configuration B (enhanced strength)—were examined at hydrogen gas pressures of 0.7, 1, and 3 Pa. Plasma characteristics including magnetic field distribution, optical emission, discharge voltage, plasma density, and spatial uniformity were analyzed across three reactor regions: R1 (inner groove), R2 (transition zone), and R3 (downstream expansion). To evaluate performance more comprehensively, plasma uniformity factor (PUF) and relative plasma uniformity factor were introduced, integrating density and uniformity metrics. Configuration B exhibited superior plasma confinement at low pressures, achieving a 121% increase in plasma density and a 144% improvement in PUF in R3 at 0.7 Pa compared to configuration A. In contrast, at 1 Pa, configuration A provided 13% higher plasma density and higher PUF (11% more) in R2, despite both configurations sharing similar uniformity. These findings highlight the critical influence of inner-ring magnetic strength on confinement dynamics and establish PUF as robust, application-oriented metrics for optimizing plasma systems under low-pressure conditions.

Brain microdialysis to assess trace elements dynamics in traumatic brain injury: An exploratory study

BackgroundTrace elements (TEs) status alterations in the brain have been linked to neurodegenerative diseases. However, data on TEs in living humans and in the post-traumatic conditions are scarce. Some TEs (copper – Cu, selenium – Se, zinc – Zn) are involved in essential antioxidant defence. This study aims to measure the evolution of TEs concentrations in the brain and serum of severe traumatic brain injury (TBI) patients over time.MethodsTwenty adult patients with severe TBI were monitored using cerebral microdialysis (CMD) and blood sampling within three days of intensive care unit admission. TEs levels were measured using inductively coupled plasma system coupled to mass spectrometry.ResultsTEs concentrations of chromium – Cr, Cu, cobalt – Co, manganese – Mn, molybdenum – Mo, Se, and Zn were quantified in brain interstitial fluid and serum. While serum and CMD levels did not differ significantly for Co, Mo and Mn, and modest differences was observed for Cr and Zn, significant differences were observed for Cu and Se with higher serum levels (8–10-fold higher) compared to CMD. No correlation was found between serum and brain TEs levels, except for Mo.ConclusionThis study provides novel TEs concentration data in living TBI patients, the largest differences between brain and serum being observed for Cu and Se, serving as a basis for further research on TEs dynamics in acute brain injury.

Brain microdialysis to assess trace elements dynamics in traumatic brain injury: An exploratory study.

Trace elements (TEs) status alterations in the brain have been linked to neurodegenerative diseases. However, data on TEs in living humans and in the post-traumatic conditions are scarce. Some TEs (copper - Cu, selenium - Se, zinc - Zn) are involved in essential antioxidant defence. This study aims to measure the evolution of TEs concentrations in the brain and serum of severe traumatic brain injury (TBI) patients over time. Twenty adult patients with severe TBI were monitored using cerebral microdialysis (CMD) and blood sampling within three days of intensive care unit admission. TEs levels were measured using inductively coupled plasma system coupled to mass spectrometry. TEs concentrations of chromium - Cr, Cu, cobalt - Co, manganese - Mn, molybdenum - Mo, Se, and Zn were quantified in brain interstitial fluid and serum. While serum and CMD levels did not differ significantly for Co, Mo and Mn, and modest differences was observed for Cr and Zn, significant differences were observed for Cu and Se with higher serum levels (8-10-fold higher) compared to CMD. No correlation was found between serum and brain TEs levels, except for Mo. This study provides novel TEs concentration data in living TBI patients, the largest differences between brain and serum being observed for Cu and Se, serving as a basis for further research on TEs dynamics in acute brain injury.

Generalized method for optimizing impedance matching in capacitively coupled plasmas

Impedance matching in capacitively coupled plasma (CCP) systems is crucial. However, due to the complex nonlinear interactions between the plasma and the external circuit, identifying control parameters that enable efficient and robust power transfer remains particularly challenging. Traditional optimization methods rely on favorable initial conditions for convergence. Even when successful, they yield only a single set of control parameters and often require highly complex optimization designs due to strong coupling with the CCP load. To address these challenges, we propose a simpler yet more robust optimization method by shifting the optimization process from the control parameter space to the load’s complex impedance space. In this approach, the load impedance acts as a bridge between the new and old control parameters, decoupling the derivation of new parameters from historical trajectories. This eliminates dependence on initial conditions and enables the method to yield multiple sets of control parameters at different saddle positions. Additionally, the load impedance also acts as a bridge between the optimization algorithm and the load device, decoupling complex physical details from the optimization process, making our method remain consistent across both numerical simulations and experimental setups. In this paper, when applying the method to a numerical CCP system with an L-type matching network, it not only achieved efficient energy matching optimization within a few iterations but also identified multiple well-matched solutions.

Pyrolysis Process, Reactors, Products, and Applications: A Review

With the rapid growth of the global population, increasing per capita energy demands, and waste generation, the need for innovative strategies to mitigate greenhouse gas emissions and effective waste management has become paramount. Pyrolysis, a thermochemical conversion process, facilitates the transformation of diverse biomass feedstocks, including agricultural biomass, forestry waste, and other carbonaceous wastes, into valuable biofuels such as bio-oil, biochar, and producer gas. The article reviews the benefits of pyrolysis as an effective and scalable technique for biofuel production from waste biomass. The review describes the different types of pyrolysis processes, such as slow, intermediate, fast, and catalytic, focusing on the effects of process parameters like temperature, heating rate, and residence time on biofuel yields and properties. The review also highlights the configurations and operating principles of different reactors used for pyrolysis, such as fixed bed, fluidized bed, entrained flow, plasma system, and microwaves. The review examines the factors affecting reactor performance, including energy consumption and feedstock attributes while highlighting the necessity of optimizing these systems to improve sustainability and economic feasibility in pyrolysis processes. The diverse value-added applications of biochar, bio-oil, and producer gas obtained from biomass pyrolysis are also discussed.

Infinite Boundary Terms and Pairwise Interactions: A Unified Framework for Periodic Coulomb Systems.

The introduction of the infinite boundary terms and the pairwise interactions [Hu, Z. J. Chem. Theory Comput. 2014, 10, 5254-5264] enables a physically intuitive approach for deriving electrostatic energy and pressure for both neutral and non-neutral systems under the periodic boundary condition (PBC). For a periodic system consisting of N point charges (with charge qj located at rj where j = 1, 2, ···, N) and one charge distribution of density ρ(r) within a primary cell of volume V, the derived electrostatic energy can be expressed as, Σi<jNqiqj ν(rij) + Σj=1Nqj∫Vdr0ρ(r0)ν(r0j) + 1/2∫Vdr0∫Vdr1ρ(r0)ρ(r1)ν(r01), where rij = ri - rj is the relative vector and ν(r) represents the effective pairwise interaction under PBC. The charge density ρ(r) is free of Delta-function-like divergence throughout the volume but may exhibit discontinuity. This unified formulation directly follows that of the isolated system by replacing the Coulomb interaction 1/|r| or other modified Coulomb interactions with ν(r). For a particular system of one-component plasma with a uniform neutralizing background, the implementation of various pairwise formulations clarifies the contribution of the background and subsequently reveals criteria for designing volume-dependent potentials that preserve the simple relation between energy and pressure.

Rapid charge transfer and O3 selective catalysis induced by B-doped nanoconfined reactor realized complete Cu-EDTA decomplexation: Significant role of BC3 conformation.

Rapid charge transfer and O3 selective catalysis induced by B-doped nanoconfined reactor realized complete Cu-EDTA decomplexation: Significant role of BC3 conformation.

New Plasma Source with Accelerator for Creating a Dust Flow of a Lunar Dust Simulant

Abstract In a framework of a program on interaction of lunar dust simulants with materials, we developed a new plasma source integrated into the lunar environment simulator and used in charging the dust simulants deposited onto various space materials. The designed source includes an electrically insulated dust container located at the bottom of the chamber, allowing to ground it or apply an electrical potential. Above the container a mesh electrode is placed, isolated electrically from the dust container. Magnets, installed in the system, allow forming and focusing of plasma. Samples used for testing are fixed on a holder located above the plasma dust source. It is also electrically insulated, and can be either grounded or supplied with an electrical potential. The plasma, ignited in the vacuum chamber, interacts with the dust in the container, charging it. The charged dust particles begin moving towards the sample holder, raised to a much higher potential. By selecting the electrical potentials applied to the sample holder or to the plasma source mesh, or to both, the dust particles will accelerate in the space between the dust container and the sample holder. This paper describes the idea, design and testing of this plasma source. It also describes the original studies of charging the dust simulator obtained both by this plasma source and by the VUV and tribological methods, carried out in the same vacuum chamber using the same measuring devices. A comparison of these original data was also made. A prototype of this source has been tested under various conditions, and its operation and possible advantages over other designs of dust distribution sources are presented and discussed in this paper.

Breeding of lactic acid-tolerant Saccharomyces cerevisiae based on atmospheric and room temperature plasma technology and automatic high-throughput microbial microdroplet culture system.

Breeding of lactic acid-tolerant Saccharomyces cerevisiae based on atmospheric and room temperature plasma technology and automatic high-throughput microbial microdroplet culture system.

Chirality-induced topological phase transitions in magnetized plasmas

The study of topological phase transitions has profoundly influenced various fields, from condensed matter physics to photonics. However, the exploration of topological properties in plasma systems remains relatively unexplored. In this work, we introduce a chirality-induced parameter and develop a modified electromagnetic coupling model to investigate topological phase transitions in magnetized plasmas. Our results reveal that chirality fundamentally breaks the intrinsic symmetry of reciprocal space, leading to three key effects: the spontaneous emergence of multiple degeneracy points, the formation of non-trivial topological phases with robust edge states, and the enrichment of phase transition types in the context of the bulk-edge correspondence. By examining the evolution of Chern numbers, we systematically map the topological phase diagram and identify distinct phase boundaries that characterize different topological states. Furthermore, we analyze the band structure and uncover the presence of topologically protected bandgaps, which play a crucial role in supporting robust chiral edge modes and reinforcing the bulk-edge correspondence principle. These findings establish a comprehensive framework for chirality-controlled topological states in plasmas, opening new possibilities for designing topologically protected wave transport and adaptive electromagnetic devices in plasma-based systems.