Conversion of Methane to Graphite-Like Carbon Nanoparticles Using a Low-Temperature Dusty Plasma
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In this paper, a one-dimensional (1D) particle-in-cell Monte Carlo collision (PIC-MCC) model is developed to investigate the effects of anisotropic pressure and inertial terms due to non-Maxwellian velocity distribution functions on cross-field electron transport. The conservation of momentum is evaluated by taking the moments of the first-principles gas-kinetic equation. A steady-state discharge is obtained without any low-frequency ionization oscillations by considering an anomalous electron scattering profile. Further, the results obtained from the 1D PIC-MCC model are compared with fluid models, including the quasi-neutral drift-diffusion (DD), non-neutral DD, and full fluid moment models. The discharge current obtained from the PIC-MCC model is in good agreement with the fluid models. The cross-field electron transport due to the inertial terms, i.e. the gradient of axial and azimuthal drift, is evaluated. Moreover, PIC-MCC simulation results show non-zero, anisotropic, off-diagonal pressure tensor terms due to asymmetric non-Maxwellian electron velocity distribution function, potentially contributing to cross-field electron transport.
An absolute cross section for the radiative capture reaction 2 H(p,γ) 3 He has been measured at the OMEGA laser facility using inertially confined plasmas. These high-temperature plasmas are created by imploding a fuel-containing capsule using laser ablation, and are advantageous in that they better mimic astrophysical systems. We measure an S factor for this reaction of 0.429 ± 0.026 stat ± 0.072 sys eV b at E c.m . = 16.35 ± 0.40 keV, which is higher than the adopted evaluations. Furthermore, this reaction is important as a source of nuclear energy in protostars and brown dwarfs. It is also a critical reaction during big-bang nucleosynthesis, and an accurate cross section can be used as a constraint on cosmology.
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This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.
An absolute cross section for the radiative capture reaction 2H(p, γ ) 3He has been measured at the OMEGA laser facility using inertially confined plasmas. These high-temperature plasmas are created by imploding a fuel containing capsule using laser ablation, and are advantageous in that they better mimic astrophysical systems. We measure an S factor for this reaction of 0.429 ± 0.026stat ± 0.072sys eV b at Ec.m. = 16.35 ± 0.40 keV, which is higher than the adopted evaluations. This reaction is important as a source of nuclear energy in protostars and brown dwarfs. It is also a critical reaction during big-bang nucleosynthesis, and an accurate cross section can be used as a constraint on cosmology.
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In high density, high temperature plasmas, the plasma sheath that develops can result in extremely high electric fields, on the order of tens to hundreds of V/nm. Under the right conditions, these electric fields can reach magnitudes that can increase the probability of electron tunneling ionization to occur, resulting in one or more electron-ion pairs. The presence of tunneling ionization can then modify the development of the plasma sheath, as well as properties such as the ion and electron densities and plasma potential. The tunnel ionization process for hydrogen atoms is demonstrated, in this work, as implemented in a Sandia National Laboratories, particle-in-cell code Aleph. Results are presented for the application of the tunnel ionization process to a one-dimensional, undriven plasma sheath. Additional results for cases that consider warm ions and neutrals, the inclusion of electron–neutral collisions, and the injection of neutral particles, as well as the application to various plasma devices, will be discussed.
Plasma-liquid interaction is a low-temperature plasma science frontier involving fundamental issues in plasma-materials interaction, multiphase kinetics, and collective effects. Plasmas in contact with liquids present two distinctive types of nonequilibrium: kinetic - across the interface - involving complex physical and chemical kinetics; and dissipative - along the interface - comprising superficial transport that is often conducive to instabilities and the formation of dissipative structures such as spot patterns. The project investigated dissipative and kinetic nonequilibrium phenomena concurrently in plasmas in contact with liquid water electrodes. The project’s objective was to determine the properties of dissipative (macroscopic) and kinetic (microscopic) nonequilibrium phenomena, and the interrelation between them, in low-temperature atmospheric-pressure plasmas in contact with liquid electrodes.
Low temperature plasmas (LTPs) are the plasmas of electron-volt (eV) physics and eV technologies. LTPs have characteristic electron temperatures of a few eV and fractional ionizations that are typically small. Since LTPs have electron temperatures commensurate with the threshold energies of excited states in neutral atoms and molecules, power transfer from electrons to these atoms and molecules efficiently produces activated species (e.g., radicals, excited states, photons). Acceleration of ions in the sheaths of LTPs to energies of tens to hundreds of eV enable activation of surface modifying processes – sputtering, etching, deposition. With such properties, LTPs are often and beneficially used in technological devices, ranging from etching and deposition in microelectronics and solar cell fabrication, to hardening of surgical instruments. LTPs harbor fundamental science issues that are intellectually challenging and rewarding. At the same time, there are enormous societal benefits that are enabled by LTPs. The entire present-day and future information technology infrastructure owes its very existence to LTPs. Renewable energy sources, such as solar cell arrays, could not be economically produced in the absence of deposition and etching by LTPs. In acknowledgment of the importance of LTPs, the DOE Office of Fusion Energy Sciences supported the Plasma Science Center for Predictive Control of Plasma Kinetics: Multi-Phase and Bounded Systems from August 2009 to July 2021, consisting of an initial 5-year grant, 3 funded extensions and 2 no-cost extensions. This is the final report of the Center in which the productivity of the Center is discussed in terms of publications, impact and personnel. High-lights of the research performed in the Center are provided.
Abstract ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.
Injection of impurities in the form of sub-millimeter powder grains is performed for the first time in the Large Helical Device (LHD) plasma, employing the Impurity Powder Dropper (IPD) [A. Nagy et al., RSI 2018], developed and built by PPPL. Controlled amounts of boron (B) and boron nitride (BN) powder are injected into the helical plasma. Visible camera imaging, UV and charge exchange spectroscopy measurements show that the injected impurities effectively penetrate into the plasma in two different magnetic configurations.The prompt effects of the impurities on the plasma are characterized as the injection rate is scanned. The injected impurities provide a supplemental electron source, causing the plasma density to increase, together with the radiated power. Beneficial effects on the confined plasma temperature are observed at low plasma densities, due to an increased efficiency in NBI power absorption. For $n_{e,av}<10^{19}m^{-3}$ the powder grains penetrate deeper into the plasma, as they can be less effectively deflected by the plasma flow in the divertor leg, which they have to cross first as they are injected from the top of the machine.In this case, the created B ions are observed to move outwards from UV spectroscopy and charge exchange measurements, due to the outwards direction of the radial electric field. This makes low density plasmas a better candidate for powder boronization techniques.
Atmospheric pressure, ambient temperature plasma jets have become a promising candidate for material processing in parallel with developments in additive manufacturing. Recent work has shown that plasma jets can be used to sinter printed nanoparticles at temperatures much lower than typically required for conventional thermal sintering. In this report we conduct a mechanistic study on plasma jet sintering that correlates specific energy input with the electrical conductivity of printed silver films after sintering. Increasing the specific energy input accelerated the sintering process following an Arrhenius-like exponential trend across a large range of conditions, including both helium and argon plasma jets. Although an exponential relationship is also found with the plasma heated substrate temperature, independent studies indicate that heating is not the primary mechanism. These results suggest there is a general behavior that couples the plasma jet with the surface.
Abstract The field of low-temperature plasmas (LTPs) excels by virtue of its broad intellectual diversity, interdisciplinarity and range of applications. This great diversity also challenges researchers in communicating the outcomes of their investigations, as common practices and expectations for reporting vary widely in the many disciplines that either fall under the LTP umbrella or interact closely with LTP topics. These challenges encompass comparing measurements made in different laboratories, exchanging and sharing computer models, enabling reproducibility in experiments and computations using traceable and transparent methods and data, establishing metrics for reliability, and in translating fundamental findings to practice. In this paper, we address these challenges from the perspective of LTP standards for measurements, diagnostics, computations, reporting and plasma sources. This discussion on standards, or recommended best practices, and in some cases suggestions for standards or best practices, has the goal of improving communication, reproducibility and transparency within the LTP field and fields allied with LTPs. This discussion also acknowledges that standards and best practices, either recommended or at some point enforced, are ultimately a matter of judgment. These standards and recommended practices should not limit innovation nor prevent research breakthroughs from having real-time impact. Ultimately, the goal of our research community is to advance the entire LTP field and the many applications it touches through a shared set of expectations.
The boundary regions of low-temperature plasmas are known to be susceptible to kinetic instabilities, which can affect the energies and fluxes of particles directed at the material boundary. For example, both the ion acoustic instability as well as an instability near the electron plasma frequency have been observed. Particle-in-cell (PIC) simulation is a tool that, alongside experiments, can capture the effects these instabilities have on the particle distribution functions. Ultimately, simulations can determine under what conditions these effects are significant by comparing to theoretical predictions and explore conditions unamenable to experiments.
The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.
Multi-scale problems are commonly known in many scientific and engineering fields where microscopic behaviors are coupled with macroscopic processes. This is also an unsolved problem in low-temperature plasma chemistry where hundreds of chemical species are involved in thousands of chemical reactions. To address this problem, a physics-informed data-driven modeling is developed to solve such a multi-scale problem using the experimental Fourier-transform infrared spectroscopy (FTIR) measurements of several species’ concentrations. The modeling based on modern machine learning techniques provides concentrations of other relevant species along with the electron temperature and gas temperature at the location of FTIR measurements. For example, the concentrations O, OH, and H 2 O 2 play key roles in plasma-based cancer therapy. This approach overcomes the multi-scale difficulties of microscopic low-temperature plasma chemistry coupling with macroscopic gas flow and allows the acquisition of a full picture of output species concentrations. Presented here for the helium-air jet at atmospheric pressure, the ML-based modeling can be used to describe and possibly control multiscale systems using partial experimental data sets.