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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2022

Liquid lithium wetting and percolation in a porous tungsten/liquid Li plasma facing component (PFC)

This work explores the concept of a hybrid material system designed to protect the plasma from high-Z material emission while also protecting an underlying high-Z wall material from erosion by integrating a low-Z component in the liquid phase. Plasma-surface interaction properties of a porous tungsten (W)-liquid metal hybrid system, having the favorable bulk thermomechanical properties of W while serving as a scaffold for a liquid metal with self-healing and radiative vapor shielding characteristics, are examined. W-substrates with 70% density of bulk W and 1–5 μm sized pores have been fabricated with 50-nm W powders using spark plasma sintering. Enhanced lithium (Li) wettability, driven by percolation through the porous W architecture, is demonstrated with in-situ liquid Li drop measurements. Finally, results show complete wetting of liquid Li at 250 °C in the porous W, 100 °C lower temperature than what has been observed on traditional W surfaces. In operando ion beam analysis of Li films deposited on porous W substrates provides microscopic evidence of Li percolation into the substrate and a first look at D retention up to a depth of 100 nm after plasma exposure at a flux of 10 20 m –2 s –1 , 250 eV/amu D + plasma to a fluence of 1.16 × 10 23 m –2 at a temperature of 200 °C.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Numerical benchmark of transient pressure-driven metallic melt flows

Fluid dynamics simulations of melting and crater formation at the surface of a copper cathode exposed to high plasma heat fluxes and pressure gradients are presented. The predicted deformations of the free surface and the temperature evolution inside the metal are benchmarked against previously published simulations. Despite the physical model being entirely hydrodynamic and ignoring a variety of plasma–surface interaction processes, the results are also shown to be remarkably consistent with the predictions of more advanced models, as well as experimental data. This provides a sound basis for future applications of similar models to studies of transient surface melting and droplet ejection from metallic plasma-facing components after disruptions.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Preliminary 3D Simulations of Anomalous Thermocouple Coupling to Surface Electric Fields

Anomalous measurements from subsurface thermocouples have been observed during re-entry of the Orion capsule (EFT-1 plugs). These measurements are considered anomalous because the amplitude and frequency of the inferred temperature modulations are too large to be due to plausible heating processes. Consequently, it is believed the anomalies are caused by electrical interference generated by the re-entry plasma. Quantifying these effects requires several steps: modeling the external plasma flows, modeling the plasma-surface interaction, and finally modeling the surface-thermocouple coupling. This paper addresses this last step using 3D finite element simulations of a wire embedded in a weakly conductive medium.

thermocouple anomaly↗
NASA NTRS1992

Experimental breakdown of selected anodized aluminum samples in dilute plasmas

Anodized aluminum samples representative of Space Station Freedom structural material were tested for electrical breakdown under space plasma conditions. In space, this potential arises across the insulating anodized coating when the spacecraft structure is driven to a negative bias relative to the external plasma potential due to plasma-surface interaction phenomena. For anodized materials used in the tests, it was found that breakdown voltage varied from 100 to 2000 volts depending on the sample. The current in the arcs depended on the sample, the capacitor, and the voltage. The level of the arc currents varied from 60 to 1000 amperes. The plasma number density varied from 3 x 10 exp 6 to 10 exp 3 ions per cc. The time between arcs increased as the number density was lowered. Corona testing of anodized samples revealed that samples with higher corona inception voltage had higher arcing inception voltages. From this it is concluded that corona testing may provide a method of screening the samples.

Grier, Norman T.↗
NASA NTRS1984

Plasma variables and tribological properties of coatings in low pressure (0.1 - 10.0 torr) plasma systems

A detailed treatment is presented of the dialog known as plasma surface interactions (PSI) with respect to the coating process and its tribological behavior. Adsorption, morphological changes, defect formation, sputtering, chemical etching, and secondary electron emission are all discussed as promoting and enhancing the surface chemistry, thus influencing the tribological properties of the deposited flux. Phenomenological correlations of rate of deposition, flux composition, microhardness, and wear with the plasma layer variables give an insight to the formation of chemical bonding between the deposited flux and the substrate surface.

Avni, R.↗
DOE OSTI2025

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.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Center for Tokamak Transient Simulations (RPI Unstructured Mesh Developments for FES SciDAC4 Partnerships) (Final Report)

The goal of this project was the development of unstructured mesh technologies for fusion simulation codes” for FES SciDAC partnerships and to integrate those technologies into the simulation workflows of those partnerships. Specific developments were executed in support of the following FES SciDAC4 partnerships: Partnership Center for High‐fidelity Boundary Plasma Simulation (HBPS), Center for Integrated Simulation of Fusion Relevant RF (RF‐SciDAC), Center for Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces (PSI2), and Center for Tokamak Transient Simulations (CTTS). The key unstructured mesh development areas addressed in this project include (i) methods to most effectively perform PIC calculations on unstructured meshes; (ii) creating meshes for fusion systems accounting for any desired level of geometric complexity and providing physics aware mesh configurations, (iii) adapting unstructured meshes to control the discretization errors, (iv) executing unstructured mesh calculations on GPU accelerated systems, (v) supporting physics/application‐specific PIC operations including surface/wall interactions of particles, (vi) providing infrastructure tools to support the interactions of solvers with unstructured meshes, and (vii) providing advanced methods for coupling plasma physics codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗
DOE OSTI2022

Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

In this work, we present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
DOE OSTI2024

An examination of the performance of molecular dynamics force fields: Silicon and silicon dioxide reactive ion etching

Classical molecular dynamics (MD) simulations of plasma-surface interactions were performed of physical sputtering and reactive ion etching (RIE), with predictions based on several force fields. In this paper, we focus mainly on SiO 2 but include some results for Si substrates as well. Here, we compare predictions from these MD simulations to experimental studies of SiO 2 physical sputtering (by Ar + ions), RIE of Si, and RIE of SiO 2 (both using F atoms and Ar + ions). MD results using different published force fields are compared to reported yields from published vacuum beam experiments. The near-surface depth profiles predicted using different force fields are compared. One motivation for the present study is to document the nature and magnitude of differences in the predictions for selected systems and conditions of practical interest.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Recrystallization suppression through dispersion-strengthening of tungsten

Tungsten is the material of choice for the divertor region of future nuclear fusion reactors, an environment that will expose plasma-facing components (e.g. divertor, etc...) to high temperatures and transient high heat flux events. Under these conditions, recrystallization and grain growth of tungsten can occur, leading to undesirable microstructural and mechanical property changes. Therefore, there is a need to raise the recrystallization temperature of tungsten and limit the kinetics of the recrystallization and grain growth processes. In this work, we examine the effect of different types (TiC vs. TaC vs. ZrC) and different concentrations (1.1 vs. 5 vs. 10 wt.%) of dispersed second phase particles in a tungsten matrix on the high temperature performance. Additionally, the addition of second-phase particles effectively increases the temperature of and time for recrystallization and slow grain growth; however, the addition of a high weight fraction of particles alters the surface chemistry, which may impact subsequent plasma-surface interactions. These results show that the addition of small concentrations of dispersed particles can be effectively employed in tungsten to raise the upper operating temperature limit for tungsten in a fusion reactor.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The mobility of small, over-pressurized helium bubbles in tungsten at 2000 K

Fusion reactor environments inevitably lead to the formation of high-pressure helium bubbles whose nucleation, growth, and diffusion strongly impact the performance of plasma-facing components. Here this research describes a diffusion mechanism of over-pressurized bubbles via a sequence of Frenkel pair nucleation, self-interstitial migration, and Frenkel pair annihilation. Molecular dynamics was used to simulate the diffusion of small bubbles in tungsten at 2000 K with helium-per-vacancy ratios in the range of 4.5 to 7. The diffusion coefficients are calculated and their dependence on helium content, number of vacancies, and number of attached self-interstitials is characterized. It is found that bubbles are most mobile when the nucleation/annihilation rates of Frenkel pairs are nearly equal and when the bubbles nucleate and annihilate a single self-interstitial. All bubbles experience a peak diffusivity, which can be as high as 10 -11 m 2 /s decreasing with bubble size. The calculated diffusion coefficients provide valuable insight into the mobility of small, high-pressure bubbles, and can be used as input parameters in mesoscale models to improve predictions of plasma-surface interactions. (LA-UR-21-21881)

36 MATERIALS SCIENCE↗
DOE OSTI2026

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗
NASA NTRS1984

Effects of the Solar and Extraterrestrial Environments on Space Power Systems

Environments surrounding the major extraterrestrial bodies in the solar system and their interactions with spacecraft power systems are summarized. The environments associated with neutrals/dust, low energy plasma, and where applicable, magnetospheres are discussed for a wide variety of cases. The impact of these environments on power systems - in particular, radiation effects, spacecraft charging, plasma interactions, surface sputtering/erosion, and induced currents - are presented. As power systems must be designed to survive in these hostile environments, it is important that they be taken into account in planning future power systems.

Garrett, H. B.↗
NASA NTRS1985

Effect of argon and hydrogen on deposition of silicon from tetrochlrosilane in cold plasmas

The roles of Ar and H2 on the decomposition of SiCl4 in cold plasma were investigated by Langmuir probes and mass spectrometry. Decomposition of the reactant by Ar only has been found to be very slow. In presence of H2 in the plasma SiCl4 is decomposed by fast radical-molecule reactions which are further enhanced by Ar due to additional ion-molecule reactions in which more H radicals are produced. A model for the plasma-surface interactions during deposition of mu-Si in the Ar + H2 + SiCl4 system is presented.

Manory, R. R.↗
NASA NTRS2011

Direct Solar Wind Proton Access into Permanently Shadowed Lunar Polar Craters

Recent analyses of Lunar Prospector neutron spectrometer (LPNS) data have suggested that high abundances of hydrogen exist within cold traps at the lunar poles, and it has often been assumed that hydrogen-bearing volatiles sequestered in permanent shadow are topographically shielded from sputtering by solar wind protons. However, recent simulation results are presented showing that solar wind protons clearly access the floor of an idealized, shadowed lunar crater through a combination of thermal and ambipolar processes, in effect creating a plasma "miniwake". These simulations are the first to model the mini-wake environment in two spatial dimensions with a self-consistent lunar surface-plasma interaction. Progress is reported on constraining the nonzero particle fluxes and energies incident on kilometer-scale shadowed topography, such as a small crater embedded within a larger one. The importance of direct solar wind proton bombardment is discussed within the context of understanding the stability and inventory of hydrogen-bearing volatiles in shadow at the lunar poles. The support of the National Lunar Science institute, the DREAM institute, LPROPS, and the NASA Postdoctoral Program at NASA Goddard Space Flight Center administered by ORAU are gratefully acknowledged.

Zimmerman, M. I.↗