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DOE OSTI2020

Matrix-free preconditioning for high-order H (curl) discretizations

The greater arithmetic intensity of high-order finite element discretizations makes them attractive for implementation on next-generation hardware, but assembly of high-order finite element operators as matrices is prohibitively expensive. As a result, the development of general algebraic solvers for such operators has been an open research challenge. Fast matrix-free application of high-order operators has received significant attention in the literature in the context of Poisson-type problems, but preconditioners and solvers for inverting more general operators are not very well-developed. In this paper, we consider the problem of preconditioning a definite Maxwell operator at high polynomial order without assembling a matrix. We show that given efficient preconditioners for high-order H 1 finite element problems on the same mesh, efficient H(curl) preconditioners can be constructed in an auxiliary space framework. We demonstrate the resulting preconditioners in a practical setting with tensor-product basis functions on an unstructured mesh of quadrilaterals. Overall, our approach uses a sparsified H 1 solver constructed on a low-order mesh of the nodal points of the underlying high-order space, and we show that the resulting H(curl) preconditioner is effective at very high polynomial orders for two-dimensional model problems with complicated geometry, varying piecewise constant coefficients, and curved elements. The resulting preconditioner scales with nearly optimal O(p d+1 ) floating point operation count and optimal O(p d ) memory transfer requirements, outperforming existing Maxwell preconditioners in the high-order regime.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials

Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. In this work, we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Sky subtraction in an era of low surface brightness astronomy

ABSTRACT The Vera C. Rubin Observatory Wide-Fast Deep sky survey will reach unprecedented surface brightness depths over tens of thousands of square degrees. Surface brightness photometry has traditionally been a challenge. Current algorithms which combine object detection with sky estimation systematically oversubtract the sky, biasing surface brightness measurements at the faint end and destroying or severely compromising low surface brightness light. While it has recently been shown that properly accounting for undetected faint galaxies and the wings of brighter objects can in principle recover a more accurate sky estimate, this has not yet been demonstrated in practice. Obtaining a consistent spatially smooth underlying sky estimate is particularly challenging in the presence of representative distributions of bright and faint objects. In this paper, we use simulations of crowded and uncrowded fields designed to mimic Hyper Suprime-Cam data to perform a series of tests on the accuracy of the recovered sky. Dependence on field density, galaxy type, and limiting flux for detection are all considered. Several photometry packages are utilized: source extractor, gnuastro, and the LSST science pipelines. Each is configured in various modes, and their performance at extreme low surface brightness analysed. We find that the combination of the source extractor software package with novel source model masking techniques consistently produce extremely faint output sky estimates, by up to an order of magnitude, as well as returning high fidelity output science catalogues.

Astronomy & Astrophysics↗

DOE OSTI2020

High-Frequency Signature-Based Fault Detection for Future MV Distribution Grids

Increasing penetration levels of inverter based distributed energy resources (DERs) impact the legacy distribution system protection. Inverter based DERs provide approximately 1.2-2 pu fault current. In systems with high penetration of inverter based DERs, it is difficult for over-current based protection schemes to differentiate between normal loading conditions and a fault. Directional, distance, and adaptive forms of protection schemes are also affected by low fault currents. This paper analyzes fault generated traveling wave (TW) based high-frequency signatures in the distribution system. In order to simulate such signatures, frequency-dependent distributed parameter line modeling approach is used in this research work to represent distribution lines and underground cables. Modified IEEE 13-bus medium voltage test system is modeled in electromagnetic transient simulation tool and multiple transient scenarios are simulated in this test system. The results are analyzed to understand the high-frequency signatures that can be used to detect and locate faults under high penetration of DERs.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2020

High-Frequency Signature-Based Fault Detection for Future MV Distribution Grids

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗

DOE OSTI2024

Predictions for a Low-mass Cutoff for the Primordial Black Hole Mass Spectrum

In this note, we outline how a modest violation in the conservation of mass during the merger of two PBHs affects the PBH mass spectrum that we previously obtained using a Boltzmann equation model for the evolution of the mass spectrum with no mass loss. We find that if the initial cosmological redshift is of the order of 10 12 , then the fraction of primordial holes with masses greater than 10 3 solar masses appears to be close to what is required to provide the seeds for galaxies. In addition, we note that as a result of rapid collisions and strong coupling to electromagnetic radiation for temperatures > GeV, there will be an effective low-mass cutoff in the mass spectrum for PBH masses less than a certain PBH mass less than 0.1M ⊙ . We also point out that this cutoff in the mass spectrum below ~ 0.1M ⊙ can be confirmed by combining future microlensing observations from the Roman Space Telescope and the Vera C. Rubin Observatory with astrometric observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Casimir force, causality, and the Gurzhi model

An extended Drude model, termed the Gurzhi model, which takes into account the electron-phonon and electron-electron interactions, is applied to calculate the Casimir force between two metallic plates. It is shown that although the dielectric permittivity of the Gurzhi model has a first-order pole in the upper half-plane of complex frequencies and, thus, violates the causality principle, it can be used in a restricted frequency interval in combination with the experimental permittivity determined by the optical data for the complex index of refraction. The imaginary part of the Gurzhi dielectric permittivity of Au at low frequencies demonstrates better agreement with the permittivity given by the optical data than the simple Drude model. The Casimir pressure between two Au plates is computed using the Gurzhi, Drude, and plasma model approaches, taking into account the optical data, as well as with the simple Drude and plasma models. Finally, the contribution of the electron-electron scattering to the Casimir pressure is estimated. Although a comparison with the measurement data of two precise experiments shows that the Gurzhi model does not resolve the Casimir puzzle, the obtained results suggest further clarification of this fundamental problem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Latent space dynamics identification for interface tracking with application to shock-induced pore collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

Gaussian process↗

DOE OSTI2024

Spinodal enhancement of fluctuations in nucleus-nucleus collisions

Subensemble Acceptance Method (SAM) [1, 2] is an essential link between measured event-by-event fluctuations and their grand canonical theoretical predictions such as lattice QCD. The method allows quantifying the global conservation law effects in fluctuations. In its basic formulation, SAM requires a sufficiently large system such as created in central nucleus-nucleus collisions and sufficient space-momentum correlations. Directly in the spinodal region of the First Order Phase Transition (FOPT) different approximations should be used that account for finite size effects. Thus, we present the generalization of SAM applicable in both the pure phases, metastable and unstable regions of the phase diagram [3]. Obtained analytic formulas indicate the enhancement of fluctuations due to crossing the spinodal region of FOPT and are tested using molecular dynamics simulations. A rather good agreement is observed. Using transport model calculations with interaction potential we show that the spinodal enhancement of fluctuations survives till the later stages of collision via the memory effect [4]. However, at low collision energies the space-momentum correlation is not strong enough for this signal to be transferred to second and third order cumulants measured in momentum subspace. This result agrees well with recent HADES data on proton number fluctuations at $\sqrt{S_{NN}}$ = 2.4 GeV which are found to be consistent with the binomial momentum space acceptance [5].

Poberezhnyuk, Roman↗

DOE OSTI2025

On the Efficacy of Repeat Voltage Holds for Conditioning and Calendar Life Testing of Graphite and Silicon Cells

Voltage-hold (V-hold) protocols have shown promise toward calendar lifetime analysis of cells with graphite (Gr) and silicon (Si) anodes. In this work, repeat V-holds are performed on Gr and Si cells paired with lithium iron phosphate cathodes to delineate their beneficial role in formation and conditioning. We find that V-hold at the top of charge supplements constant current cycling in conditioning the cell to higher capacities for both Gr and Si cells after the first V-hold. A reduced order model provides the irreversible capacity proportions of each V-hold. With each repeat V-hold, parasitic loss of lithium to the solid electrolyte interphase (SEI) decreases on both Gr and Si cells. Gr cells show the square-root-of-time capacity loss behavior within 200 h of V-hold, indicative of its fast relaxation and low impact of reference performance test cycles on the SEI growth. Lifetime estimates from repeat V-holds on Gr can reach years. Si exhibits longer transition times from kinetic to diffusion-limited SEI growth, evidenced by the 400 h and 200 h holds showing square-root-of-time and linear behavior, respectively. Lifetime predictions from repeat V-holds on Si only reach 1–2 months, highlighting its limitations. Recommended duration of V-holds for Si cells should be ≥400 h.

25 ENERGY STORAGE↗

DOE OSTI2022

Water film-driven Mn (oxy)(hydr)oxide nanocoating growth on rhodochrosite

Minerals exposed to moist air stabilize thin water films that drive a score of chemical reactions of great importance to water-unsaturated terrestrial environments. In this study, we identified Mn (oxy)(hydr)oxide nanocoatings formed by the dissolution, oxidation and precipitation of Mn in oxygenated water films grown on rhodochrosite (MnCO 3 ) microparticles. Nanocoatings that could be identified by vibrational spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and (scanning and transmission) electron microscopy formed in water films containing the equivalent of at least 7 monolayers (~84 H 2 O/nm 2 ). These films were formed by exposing microparticles to moist air with at least 50% relative humidity (RH). Films of neutral pH reacted up to 14% of the Mn II located in the topmost ~5 nm region of the microparticles in atmospheres of up to 90% RH for 7 d. These reactions produced MnOOH, birnessite (MnO 2 ) and hausmannite (Mn 3 O 4 ) nanoparticles of low crystallinity, while exposure to atmospheric air for 1 yr. converted only 2% of Mn II in this region to MnOOH. In contrast, reactions in alkaline water films converted up to ~75% of the Mn II but only after 16 d of reaction. These films produced MnOOH and MnO 2 of low crystallinity, as well as crystalline hausmannite. Kinetic modeling of the time-resolved growth of the Mnsingle bondO stretching vibrational bands of these nanocoatings revealed two concurrent reaction processes. A 1 rst -order process was assigned to nucleation events terminating only after a few hours, and a 0-order process was assigned to the sustained growth of nanocoatings from these nuclei over longer reaction time. By identifying nanocoatings formed by water film-driven reactions on rhodochrosite, our study adds new insight into mineralogical transformations relevant to anoxic–oxic boundaries in water-unsaturated environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Decoupling Lattice and Magnetic Instabilities in Frustrated CuMnO2

The AMnO 2 delafossites (A = Na, Cu) are model frustrated antiferromagnets, with triangular layers of Mn 3+ spins. At low temperatures (T N = 65 K), a C2/m → P$\bar1$ transition is found in CuMnO 2 , which breaks frustration and establishes magnetic order. In contrast to this clean transition, A = Na only shows short-range distortions at T N . Here, we report a systematic crystallographic, spectroscopic, and theoretical investigation of CuMnO 2 . We show that, even in stoichiometric samples, nonzero anisotropic Cu displacements coexist with magnetic order. Using X-ray/neutron diffraction and Raman scattering, we show that high pressures act to decouple these degrees of freedom. This manifests as an isostuctural phase transition at ~10 GPa, with a reversible collapse of the c-axis. This is shown to be the high-pressure analogue of the c-axis negative thermal expansion seen at ambient pressure. Density functional theory (DFT) simulations confirm that dynamical instabilities of the Cu + cations and edge-shared MnO 6 layers are intertwined at ambient pressure. However, high pressure selectively activates the former, before an eventual predicted reemergence of magnetism at the highest pressures. Our results show that the lattice dynamics and local structure of CuMnO 2 are quantitatively different from nonmagnetic Cu delafossites and raise questions about the role of intrinsic inhomogeneity in frustrated antiferromagnets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Elucidating Design Rules toward Enhanced Solid-State Charge Transport in Oligoether-Functionalized Dioxythiophene-Based Alternating Copolymers

This study investigates the solid-state charge transport properties of the oxidized forms of dioxythiophene-based alternating copolymers consisting of an oligoether-functionalized 3,4-propylenedioxythiophene (ProDOT) copolymerized with different aryl groups, dimethyl ProDOT (DMP), 3,4-ethylenedioxythiophene (EDOT), and 3,4-phenylenedioxythiophene (PheDOT), respectively, to yield copolymers P(OE3)-D, P(OE3)-E, and P(OE3)-Ph. At a dopant concentration of 5 mM FeTos 3 , the electrical conductivities of these copolymers vary significantly (ranging between 9 and 195 S cm –1 ) with the EDOT copolymer, P(OE3)-E, achieving the highest electrical conductivity. UV–vis–NIR and X-ray spectroscopies show differences in both susceptibility to oxidative doping and extent of oxidation for the P(OE3) series, with P(OE3)-E being the most doped. Wide-angle X-ray scattering measurements indicate that P(OE3)-E generally demonstrates the lowest paracrystallinity values in the series, as well as relatively small π–π stacking distances. The significant (i.e., order of magnitude) increase in electrical conductivity of doped P(OE3)-E films versus doped P(OE3)-D or P(OE3)-Ph films can therefore be attributed to P(OE3)-E exhibiting both the highest carrier ratios in the P(OE3) series, along with good π–π overlap and local ordering (low paracrystallinity values). Furthermore, these trends in the extent of doping and paracrystallinity are consistent with the reduced Fermi energy level and transport function prefactor parameters calculated using the semilocalized transport (SLoT) model. Observed differences in carrier ratios at the transport edge (c t ) and reduced Fermi energies [η(c)] suggest a broader electronic band (better overlap and more delocalization) for the EDOT-incorporating P(OE3)-E polymer relative to P(OE3)-D and P(OE3)-Ph. Ultimately, we rationalize improvements in electrical conductivity due to microstructural and doping enhancements caused by EDOT incorporation, a structure–property relationship worth considering in the future design of highly electrically conductive systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Noncollinear polar magnet Fe 2 (SeO 3 ) 3 (H 2 O) 3 with inequivalent Fe 3+ sites

The emergence of novel magnetic states becomes more likely when the inversion symmetry of the crystal field, relative to the center between two spins, is broken. We propose that placing magnetic spins in inequivalent sites in a polar lattice can promote a realization of nontrivial magnetic states and associated magnetic properties. To test our hypothesis, we study Fe 2 (SeO 3 )(H 2 O) 3 as a model system that displays two distinct Fe(1) and Fe(2) magnetic sites in a polar structure (R3c space group). At low fields μ 0 H≤ 0.06 T, the material undergoes an antiferromagnetic ordering with T N1 = 77 K and a second transition at T N2 ≈ 4 K. At μ 0 H≥ 0.06 T and 74 K ≤T≤ 76 K, a positive entropy change of ∼0.12 mJ mol −1 K −1 can be associated with a metamagnetic transition to possibly nontrivial spin states. At zero field, Fe(1) is nearly fully ordered at T≈ 25 K, while Fe(2) features magnetic frustration down to T = 4 K. The magnetic ground state, a result corroborated by single-crystal neutron diffraction and 57 Fe Mössbauer spectroscopy, is a noncollinear antiparallel arrangement of ferrimagnetic Fe(1)–Fe(2) dimers along the c-axis. The results demonstrate that placing distinct magnetic sites in a polar crystal lattice can enable a new pathway to modifying spin, orbital, and lattice degrees of freedom for unconventional magnetism.

Crystal lattices↗

DOE OSTI2023

Role of nanoscale coherent precipitates on the thermo-mechanical response of martensitic materials (Final Report)

Coherent second phases can have a profound effect on the properties and performance of martensitic materials, and we lack a comprehensive, mechanistic understanding of the underlying processes. This limits our ability to rationally design second phases to tailor the response of martensitic materials. To address this situation, this project sought to develop a mechanistic, predictive understanding of the thermal and mechanical response of martensitic materials with nanoscale coherent heterogeneities. A synergistic combination of atomistic simulations and experiments was used to relate the local properties of each phase (in particular, their free energy landscape) and the material nanostructure (volume fraction, shape and size of second phase precipitates, and defects that may disrupt coherency) to the overall materials response. The focus was on second phases expected to induce phenomena or properties not otherwise achievable: second order martensitic transformation in SMAs, ultra-low stiffness, increased control of transition temperatures, and fatigue resistance.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Development of Mechanistic Fission Gas Release and Swelling Models for UN Fuels in BISON

This report describes the work in NEAMS (Nuclear Energy Advanced Modeling and Simulation) to develop a mechanistic fission gas model for uranium nitride fuels in BISON. The existing Sifgrs (Simple integrated fission gas release and swelling) model tracks the average properties of two bubble populations in the bulk and at the grain boundaries. It was recognized that dislocations play a crucial role in the fission gas swelling of UN, and an irradiation-induced dislocation density model was needed, as well as a model describing how fission gas interacts with dislocations creating a third population of bubbles along dislocations. A mechanistic model that tracks dislocation bubbles was implemented in Sifgrs. This model was used to simulate fission gas swelling and release in UN. Lower-length-scale calculations and experimental observations from carbide fuel were leveraged to help populate the model with essential parameters. As a placeholder, an empirical function was formulated for the evolution of the dislocation network. Because of the difference in evolution of this network at different temperatures, the dislocation bubbles are able to capture behavior that the bulk intragranular bubbles cannot. It was found that the bulk bubbles dominate microscopic swelling at low temperatures, and the dislocation bubbles dominate at higher temperatures. Based on this model, the transition between the two bubble types is the main factor behind the breakaway swelling phenomenon in UN. In order to test the model, a couple of assessments were run. For the lower temperature JOYO pins, the model produces reasonable fission gas release and swelling values. For the higher temperature SP1 pin, the model dramatically underestimates the fission gas release. This issue is attributed to the gas being trapped inside dislocation bubbles, unable to escape to grain boundaries to cause fission gas release, and could be remedied by a mechanistic dislocation model that allows the dislocation density to decrease at very high temperatures. A preliminary mechanistic dislocation model was developed supported by first-principle calculations. These calculations provided valuable insight into how interstitial defects cluster in UN in the {110} orientation, and may eventually form dislocation loops, leading to the conclusion that dislocation loops may nucleate from these clusters. An estimate of the dislocation line energy in UN was obtained and will be improved in future work. The free-energy cluster dynamics code Centipede was used to track interstitial and vacancy absorption at dislocation loops and calculate their growth. In addition, improvements to Centipede were made including new convergence criteria for transient simulations and sink driving force updates.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗