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Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine
The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar + ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl + ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.
Large Volume Plasma Generation for CO2 Processing
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Multiple‐Repeated Plasma Surface Treatments for Significantly Improving Bonding Performance of Metal‐Carbon‐Fiber‐Reinforced Thermoplastic Polymer Dissimilar Adhesive Joints
This work investigates how multiple‐repeated plasma surface treatments can significantly enhance adhesively bonded metal‐carbon‐fiber‐reinforced thermoplastic polymer (CFRTP) dissimilar joints, a topic that has been rarely investigated compared to other parameters during the plasma treatment process. By conducting double cantilever beam tests on adhesively bonded AA6061‐carbon‐fiber‐reinforced polyphthalamide (CFRPPA) dissimilar joints, it is shown that the average Mode I specific fracture energy after 20 repetitions of the same plasma treatment, using processing parameters without noticeably changing surface roughness, can be improved and saturated up to almost 2000% compared to non‐treated joints and almost 240% compared to a single plasma treatment commonly used in the literature. The improvement can be attributed to the enhanced chemical bonding at the CFRPPA‐adhesive interface. This study is important for achieving strong bonding performance of CFRTP‐related structural joints using multiple plasma treatments, a simple and effective method.
Comprehensive process and environmental impact analysis of integrated DBD plasma steam methane reforming
Utilization of electricity generated from renewable sources to obtain hydrogen, H 2 , is of critical importance to decrease the overall carbon footprint. Here in this work, integration of a dielectric discharge barrier (DBD) plasma reactor to convert low calorific value gas, such as landfill gas or coal mine gas into hydrogen, into the existing steam methane reforming (SMR) technology was evaluated using process design considerations. In particular, a DBD-enhanced catalytic SMR reactor was modeled to operate at near atmospheric pressure and 500 °C sequentially with the conventional reformer to obtain ~ 65 kmol/hr H2 for distributed production. This allowed decreasing the size of the conventional reformer albeit at the increased overall electricity consumption. Calculated process economics showed that only at an electricity cost of less than $0.004/kWh does the hybrid DBD plasma process derived H 2 price become competitive with that of the conventional SMR. A Life Cycle Assessment framework was used to compare environmental impacts from the conventional SMR, hybrid DBD SMR and hybrid DBD SMR utilizing only onshore wind-derived electricity. Larger environmental impacts in the plasma reformer were obtained due to the use of electricity for the plasma reforming operation, which was modeled as coming from the typical U.S. grid mix. Utilizing only 100% wind-derived electricity provided certain environmental benefits, except for the ecotoxicity impact where the wind power scenario modeled here only reduced ecotoxicity impacts associated with electricity by 30%.
Ion concentration ratio measurements of ion beams generated by a commercial microwave electron cyclotron resonance plasma source
A commercially available electron cyclotron resonance (ECR) plasma source (GenII Plasma Source, tectra GmbH) is widely used for surface processing. This plasma source is compatible with ultrahigh vacuum systems, and its working pressure is relatively low, around 10 –6 –10 –4 Torr even without differential pumping. Here, we report ion flux concentration ratios for each ion species in an ion beam from this source, as measured by a mass/energy analyzer that is a combination of a quadrupole mass spectrometer, an electrostatic energy analyzer, and focusing ion optics. In this study, the examined beams were those arising from plasmas produced from feed gases of H 2 , D 2 , N 2 , O 2 , Ar, and dry air over a range of input power and working pressures. H 2 (D 2 ) plasmas are widely used for nuclear fusion applications and, hence, the ion concentration ratios of H + , H 2 + , and H 3 + reported here will be useful information for research that applies this plasma source to well-controlled plasma-material interaction studies. Ion energy distributions, stability of operation, and impurity concentrations were also assessed for each of the plasma species investigated.
Valorization of waste polyolefins to butene, unsaturated fatty alcohols, and branched alkenes using CO 2 and plasma catalyst
Butene, branched alkenes, and short-chain unsaturated fatty alcohols are among the chemicals that have a wide range of industrial applications in the production of fuels, chemicals, and polymers. In this work, we produced these valuable commodity chemicals from waste plastics using a single-step plasma-catalytic process at atmospheric pressure CO 2 . The study shows that combining non-thermal plasma and zeolite could convert polyolefins at a temperature of 200 °C within 15 minutes, producing liquids rich in C 5 and C 6 branched alkenes and C 6 -C 8 unsaturated fatty alcohols. Additionally, gaseous products include a high yield of butene. Comparative studies indicate that combining CO 2 plasma with zeolite synergistically increases reaction rates and alters product compositions. Product selectivity was strongly dependent on reaction conditions, including plasma power, gas flow rates, reactor temperature, and catalyst loading. Furthermore, this process was applicable to common polyolefins and post-consumer polyethylene, indicating that the plasma catalytic approach has promising potential to valorize waste plastics and greenhouse gas CO 2 into versatile chemicals.
Ionization disequilibrium in K- and L-shell ions
Time-gated Sc K-shell and Ge L-shell spectra are presented from a range of characterized thermodynamic states spanning ion densities of 1019–1020cm−3 and plasma temperatures around 2000 eV. For the higher densities studied and temperatures from 1000 to 3000 eV, the Sc and Ge x-ray emission spectra are consistent with steady-state calculations from the modern atomic kinetics model SCRAM. At the lower ion densities achieved through plasma expansion, however, the model calculations require a higher plasma temperature to reproduce the observed Ge spectrum. We attribute this to ionization disequilibrium of the Sc because the ionization time scales exceed the hydrodynamic timescale when the inferred temperatures diverge.
Surrogate models to optimize plasma-assisted atomic layer deposition in high aspect ratio features
In this work, we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching (ALE), surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma–surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to ALE and more complex structures.
Formation of transient high-β plasmas in a magnetized, weakly collisional regime
We present experimental data providing evidence for the formation of transient (~20 μs) plasmas that are simultaneously weakly magnetized (i.e. Hall magnetization parameter ωτ > 1) and dominated by thermal pressure (i.e. ratio of thermal-to-magnetic pressure β > 1). Particle collisional mean free paths are an appreciable fraction of the overall system size. These plasmas are formed via the head-on merging of two plasmas launched by magnetized coaxial guns. The ratio λ gun = μ 0 I gun /ψ gun of gun current I gun to applied magnetic flux ψ gun is an experimental knob for exploring the parameter space of β and ωτ. These experiments were conducted on the Big Red Ball at the Wisconsin Plasma Physics Laboratory. The transient formation of such plasmas can potentially open up new regimes for the laboratory study of weakly collisional, magnetized, high-β plasma physics; processes relevant to astrophysical objects and phenomena; and novel magnetized plasma targets for magneto-inertial fusion.
Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression
The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.
Numerical thermalization in 2D PIC simulations: Practical estimates for low-temperature plasma simulations
The process of numerical thermalization in particle-in-cell (PIC) simulations has been studied extensively. It is analogous to Coulomb collisions in real plasmas, causing particle velocity distributions (VDFs) to evolve toward a Maxwellian as macroparticles experience polarization drag and resonantly interact with the fluctuation spectrum. This paper presents a practical tutorial on the effects of numerical thermalization in 2D PIC applications. Scenarios of interest include simulations, which must be run for many thousands of plasma periods and contain a population of cold electrons that leave the simulation space very slowly. This is particularly relevant to many low-temperature plasma discharges and materials processing applications. We present numerical drag and diffusion coefficients and their associated timescales for a variety of grid resolutions, discussing the circumstances under which the electron VDF is modified by numerical thermalization. Though the effects described here have been known for many decades, direct comparison of analytically derived, velocity-dependent numerical relaxation timescales to those of other relevant processes has not often been applied in practice due to complications that arise in calculating thermalization rates in 1D simulations. Using these comparisons, we estimate the impact of numerical thermalization in several examples of low-temperature plasma applications including capacitively coupled plasma discharges, inductively coupled plasma discharges, beam plasmas, and hollow cathode discharges. Finally, we discuss possible strategies for mitigating numerical relaxation effects in 2D PIC simulations.
Selective laser sintering and spark plasma sintering of (Zr,Nb,Ta,Ti,W)C compositionally complex carbide ceramics
Abstract Two advanced manufacturing processes, spark plasma sintering (SPS) and selective laser sintering (SLS), have been developed for synthesis of (Zr,Nb,Ta,Ti,W)C compositionally complex carbide (CCC) via reactive sintering of a powder mixture of constitute monocarbides. X‐ray diffraction analysis confirmed that the single‐phase CCC can be formed by both SPS and SLS. While a homogenous microstructure with uniform metal element distributions was developed during SPS, three‐layer microstructures with a thin TiC‐rich layer and two TaC‐rich layers along with a TiO 2 ‐rich surface layer containing W nanoparticles were formed during SLS. In addition, cellular structures with W, Zr, and Ti element segregation and dislocations on cell boundaries were observed in the SLS‐CCC sample, indicating the effect of nonequilibrium conditions on microstructure formation during laser melting followed by rapid cooling and solidification process. Compared to the SPS‐CCC sample, the SLS‐CCC showed enhanced hardness and reduced thermal conductivity, which may be related to their unique cellular structures.
Direct HCN synthesis via plasma-assisted conversion of methane and nitrogen
Hydrogen cyanide (HCN) is synthesized from ammonia (NH 3 ) and methane (CH 4 ) at ~1200°C over a Pt catalyst. Ammonia synthesis entails several complex, highly emitting processes. Plasma-assisted HCN synthesis directly from CH 4 and nitrogen (N 2 ) could be pivotal for on-demand HCN production. Here, we evaluate the potential of dielectric barrier discharge (DBD) N 2 /CH 4 plasma for decentralized catalyst-free selective HCN production. We demonstrate a single-step conversion of methane and nitrogen to HCN with a 72% yield at <300°C. HCN is favored at low CH 4 concentrations with ethane (C 2 H 6 ) as the secondary product. We propose a first-principles microkinetic model with few electron impact reactions. The model accurately predicts primary product yields and elucidates that methyl radical (·CH 3 ) is a common intermediate in HCN and C 2 H 6 synthesis. Compared to current industrial processes, N 2 /CH 4 DBD plasma can achieve minimal CO 2 emissions.
Simulation of Plasma Emission in Magnetized Plasmas
The recent Parker Solar Probe observations of type III radio bursts show that the effects of the finite background magnetic field can be an important factor in the interpretation of data. In the present paper, the effects of the background magnetic field on the plasma-emission process, which is believed to be the main emission mechanism for solar coronal and interplanetary type III radio bursts, are investigated by means of the particle-in-cell simulation method. The effects of the ambient magnetic field are systematically surveyed by varying the ratio of plasma frequency to electron gyrofrequency. The present study shows that for a sufficiently strong ambient magnetic field, the wave–particle interaction processes lead to a highly field-aligned longitudinal mode excitation and anisotropic electron velocity distribution function, accompanied by a significantly enhanced plasma emission at the second-harmonic plasma frequency. For such a case, the polarization of the harmonic emission is almost entirely in the sense of extraordinary mode. On the other hand, for moderate strengths of the ambient magnetic field, the interpretation of the simulation result is less clear. The underlying nonlinear-mode coupling processes indicate that to properly understand and interpret the simulation results requires sophisticated analyses involving interactions among magnetized plasma normal modes, including the two transverse modes of the magneto-active plasma, namely, the extraordinary and ordinary modes, as well as electron-cyclotron-whistler, plasma oscillation, and upper-hybrid modes. At present, a nonlinear theory suitable for quantitatively analyzing such complex-mode coupling processes in magnetized plasmas is incomplete, which calls for further theoretical research, but the present simulation results could provide a guide for future theoretical efforts.
Relativistic Magnetic Reconnection in Astrophysical Plasmas: A Powerful Mechanism of Nonthermal Emission
Magnetic reconnection—a fundamental plasma physics process, where magnetic field lines of opposite polarity annihilate—is invoked in astrophysical plasmas as a powerful mechanism of nonthermal particle acceleration, able to explain fast-evolving, bright high-energy flares. Near black holes and neutron stars, reconnection occurs in the relativistic regime, in which the mean magnetic energy per particle exceeds the rest mass energy. This review reports recent advances in our understanding of the kinetic physics of relativistic reconnection (RR): ▪ Kinetic simulations have elucidated the physics of plasma heating and nonthermal particle acceleration in RR. ▪ The physics of radiative RR, with its self-consistent interplay between photons and reconnection-accelerated particles—a peculiarity of luminous, high-energy astrophysical sources—is the new frontier of research. ▪ RR plays a key role in global models of high-energy sources, in terms of both global-scale layers and reconnection sites generated as a by-product of local magnetohydrodynamic instabilities. We summarize themes of active investigation and future directions, emphasizing the role of upcoming observational capabilities, laboratory experiments, and new computational tools.
Fast particles in drift wave turbulence
This study aims to incorporate the effects of fast particles into our present fluid model for tokamak transport. The parameter ε f = ω / ω f, where ω is the mode frequency and ω f is the typical frequency of the fast particles, which enters as a factor in front of the fast particle response. Thus, for trapped fast particles, where ω f = ω pres the precession frequency of the fast particles, this parameter is of order 10 – 2 for drift waves, and thus, the fast particle response can be neglected. However, ε f will be of order 1 for fast particle modes such as in the fishbone instability. An important turbulence property, affecting both these limits, is resonance broadening. Effects of resonance broadening have recently been considered for fast particle instabilities, often coupled directly to the linear growth rate, while we here consider the original Dupree formulation where the turbulence directly drives a nonlinear frequency shift. Resonance broadening has a general tendency to counteract dissipative wave particle resonances. This has been observed for fast particle instabilities. Here, there is a resonant external source for the fast particles, so the instability survives if this source is dominant over the resonance broadening. For drift waves, however, external sources are not resonant since ε f << 1. Furthermore, the resonance broadening is able to remove the dissipative wave particle resonance completely.
Deep learning for NLTE spectral opacities
Computer simulations of high energy density science experiments are computationally challenging, consisting of multiple physics calculations including radiation transport, hydrodynamics, atomic physics, nuclear reactions, laser–plasma interactions, and more. To simulate inertial confinement fusion (ICF) experiments at high fidelity, each of these physics calculations should be as detailed as possible. However, this quickly becomes too computationally expensive even for modern supercomputers, and thus many simplifying assumptions are made to reduce the required computational time. Much of the research has focused on acceleration techniques for the various packages in multiphysics codes. In this work, we explore a novel method for accelerating physics packages via machine learning. The non-local thermodynamic equilibrium (NLTE) package is one of the most expensive calculations in the simulations of indirect drive inertial confinement fusion, taking several tens of percent of the total wall clock time. We explore the use of machine learning to accelerate this package, by essentially replacing the physics calculation with a deep neural network that has been trained to emulate the physics code. Overall, we demonstrate the feasibility of this approach on a simple problem and perform a side-by-side comparison of the physics calculation and the neural network inline in an ICF Hohlraum simulation. We show that the neural network achieves a 10× speed up in NLTE computational time while achieving good agreement with the physics code for several quantities of interest.