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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Deformation Rate‐Adaptive Conducting Polymers and Composites

Abstract Materials are more easily damaged during accidents that involve rapid deformation. Here, a design strategy is described for electronic materials comprised of conducting polymers that defies this orthodox property, making their extensibility and toughness dynamically adaptive to deformation rates. This counterintuitive property is achieved through a morphology of interconnected nanoscopic core–shell micelles, where the chemical interactions are stronger within the shells than the cores. As a result, the interlinked shells retain material integrity under strain, while the rate of dissociation of the cores controls the extent of micelle elongation, which is a process that adapts to deformation rates. A prototype based on polyaniline shows a 7.5‐fold increase in ultimate elongation and a 163‐fold increase in toughness when deformed at increasing rates from 2.5 to 10 000% min −1 . This concept can be generalized to other conducting polymers and highly conductive composites to create “self‐protective” soft electronic materials with enhanced durability under dynamic movement or deformation.

Hernandez, Victor↗
DOE OSTI2024

Silicon ion radiation as a viable surrogate for emulating neutron radiation damage in silicates

Abstract Nuclear power plants are aging around the world, and a precise assessment of irradiation damage in their components is needed. One key component, concrete, and specifically the silicates in its aggregates, can undergo significant expansion upon neutron radiation, which can lead to cracking and, ultimately, structural failure. However, assessing and predicting the extent of damage via neutron radiation is challenging due to reasons such as residual radioactivity and, most importantly, the high time involved. Here, we evaluate whether ion radiation can be a viable surrogate. Specifically, by employing Si 2+ ion radiations and a comprehensive multi-modal imaging protocol, we report mineral-specific responses for key silicates such as quartz, albite, anorthite, and microcline. We find that 10 MeV Si 2+ ions result in mineral expansions that are remarkably comparable to neutron radiation equivalent expansions (R 2 = 0.86, RMSE = 1.29%), opening up pathways towards rapid assessment of silicates subject to irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Performance Coal-Based Commercial Facade Panels and Architectural Components (Phase I Final Report)

Semplastics has developed, tested, and documented the viability for commercialization of a new class of composite architectural panel materials that use coal as the primary constituent. This project produced sample panels using these novel materials that comprise 55% coal by mass (71% carbon by mass) and are comparable in dimensions to commercially available materials, while displaying superior mechanical strength, significant weight savings, and better insulating ability at a competitive cost. Phase I moved this coal-based composite materials technology from a Technology Readiness Level (TRL) of 3 to TRL 5. Semplastics’ new coal-based composite materials display an array of high-performance characteristics, including light weight, mechanical durability, temperature stability, and water resistance. These new materials require less energy to produce than comparable commercial products and could be manufactured on existing conventional plastic resin processing equipment in commercial quantities. The coal particles are completely encapsulated in ceramic from a polymer-derived ceramic (PDC) precursor, then bonded together by another inorganic resin. The material can be molded and cured to produce fireproof components such as ceiling panels, facades, and extruded underlayment, blocking, and backer boards, as well as other architectural design components such as moldings. The project included the production of sample panels for proof of concept, testing of the panels to show their improved characteristics, and development of a scale-up strategy to demonstrate the viability of the coal-based panels as a commercial alternative to existing building materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Enabling Ambient Stability of LiNiO 2 Lithium-Ion Battery Cathode Materials via Graphene–Cellulose Composite Coatings

Nickel-rich layered oxides are widely used as cathode materials for energy-dense lithium-ion batteries. These chemistries, based on the parent compound LiNiO 2 (LNO), are highly sensitive to ambient environments and are known to readily react with moisture and carbon dioxide. As a result, impurities such as lithium hydroxides and lithium carbonates are formed at the LNO surface, compromising electrochemical behavior. Here, we address this issue by coating LNO cathode particles with a hydrophobic barrier layer composed of graphene and ethyl cellulose (GrEC). This coating limits contact between atmospheric moisture and the LNO surface, which minimizes the generation of lithium impurities. This scheme is evaluated by exposing coated LNO to humidified CO 2 for 24 h as an accelerated ambient degradation test. Subsequent spectroscopy, microscopy, and electrochemical characterization show no detectable signatures of carbonates on the LNO surface, thus verifying that the GrEC coating prevents ambient degradation. In conclusion, by demonstrating this methodology for the ultimate nickel-rich chemistry, this approach can likely be generalized to a wide range of ambient-sensitive battery materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Phosphorus-carbon composites as battery anode materials

An electrochemical device includes an anode containing a phosphorus-carbon composite including a conductive carbon matrix and nano-sized phosphorus particles, wherein the nano-sized phosphorus particles are uniformly dispersed on the surface and/or pores of the carbon matrix.

Xu, Guiliang↗
DOE OSTI2020

Strain-driven surface reconstruction and cation segregation in layered Li(Ni 1– x – y Mn x Co y )O 2 (NMC) cathode materials

The composition, structure and phase transformations occurring on cathode surfaces greatly affect the performance of Li-ion batteries. Li-Ion diffusion and surface–electrolyte interaction are two major phenomena that impact the capacity and cell impedance. The effects of surface reconstruction (SR) of cathode materials on the performance of Li-ion batteries are of current interest. However, the origin and evolution of the SR are still not well understood. In this work, density functional theory (DFT) calculations are used to investigate the processes taking place during surface segregation and reconstruction. Facet dependent segregation was found in Li(Ni 1– x – y Mn x Co y )O 2 (NMC) cathodes. Specifically, Co tends to segregate to the (104) surface of the primary particles within the transition metal layer, while Ni ions tend to segregate to the (012) surface in the Li layer, forming a SR. Experimental evidence shows the SR to be epitaxial with the bulk of the as-synthesized material, and the new SR phase is pinned to the NMC unit cell leading to a strained SR. Here, we demonstrate that strain can stabilize a spinel structure of the SR layers. Understanding the effects of surface strain opens a new avenue for the design of cathode materials with enhanced surface properties.

25 ENERGY STORAGE↗
DOE OSTI2022

Controlled release of hydrogen from composite nanoparticles

Multi-functional materials for use in reversible, high-capacity hydrogen separation and/or storage are described. Also described are systems incorporating the materials. The multi-functional materials combine a hydrogen-absorbing material with a high-efficiency and a non-contact energy-absorbing material in a composite nanoparticle. The non-contact energy-absorbing material include magnetic and/or plasmonic materials. The magnetic or plasmonic materials of the composite nanoparticles can provide localized heating to promote release of hydrogen from the hydrogen storage component of the composite nanoparticles.

Murph, Simona H.↗
DOE OSTI2020

Rooting MnO 2 into protonated g-C 3 N 4 by intermolecular hydrogen bonding for endurable supercapacitance

Composited electrode materials for energy storage typically benefit from the merits of each component but meanwhile largely suffer from the vulnerable structural integrity during repetitive cycling. Realizing the merely physical attachment in most composited structures being too weak to survive harsh cycling, we report here a facile one-step synthesis strategy with generation of chemical bonds, more specifically, intermolecular hydrogen bonds, to tightly combine each component. Here, we demonstrate this concept by designing a composite featuring MnO 2 nanorods chemically rooted into protonated g-C 3 N 4 , where the protonation of 2D g-C 3 N 4 substrate (pg-C 3 N 4 ), the nucleation/growth of MnO 2 and the formation of hydrogen bonding between pg-C 3 N 4 and MnO 2 simultaneously occur. The obtained composite, when applied for supercapacitive energy storage, yields maximum specific capacitances of 348.4 F·g -1 at a current density of 1.0 A·g -1 as well as a high retention of ~85.0% after 10000 cycles at 6.0 A·g -1 , surpassing most previously reported composited electrode materials based on either MnO 2 or g-C 3 N 4 . Finally, we expect this work to inspire the synthesis of composited electrode materials with consolidated 3D architecture for endurable energy storage performance.

25 ENERGY STORAGE↗
DOE OSTI2023

Synthesis and Characterization of Carbon Microbeads

We report a microfluidic-based droplet generation platform for synthesizing micron-sized porous carbon microspheres. The setup employs carbon materials such as graphite, carbon nanotubes, graphene, fullerenes, and carbon black as starting materials. Custom composition, structure, and function are achieved through combinations of carbon materials, cross-linkers, and additives along with variations in process parameters. Carbon materials can be assembled into spheres with a mean diameter of units to hundreds of μm with relatively tight size distribution (<25% RSD). Pore structure and size (tens to hundreds of angstrom) can be modulated by incorporating porogen/coporogen dilutants during synthesis. The microbeads have excellent mechanical stability with an elastic modulus of hundreds of MPa. They can sustain high dynamic fluid flow pressures of up to 9000 psi. This work lays the foundation for synthesizing novel tailorable and customizable carbon microbeads. It opens avenues for applying these novel materials for composite and additive manufacturing, energy, life science, and biomedical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Material processing, microstructure, and composite properties of low carbon Engineered Cementitious Composites (ECC)

Traditional PVA fiber-reinforced Engineered Cementitious Composites (ECC) show high tensile ductility and superior durability with tight crack width, but the high cost and embodied carbon can hinder its wider application in infrastructures. The objective of this study is to develop a better understanding of the fresh and hardened properties of an ECC that employs a lower embodied-carbon binder, Limestone Calcined Clay Cement (LC3), and lower-cost PP fiber that is widely available. Specifically, the interrelations between material processing, microstructure, and composite properties were studied experimentally. The results showed that ECC with high tensile ductility up to 9% tensile strain and tight crack width with 50 μm at 2% tensile strain can be achieved. It was found that a matrix paste with higher viscosity generally enhanced fiber dispersion uniformity and robustness in tensile strain-hardening. The paste viscosity is increased when OPC is replaced by LC3 and can be tuned with superplasticizer content. Larger maximum flaw size leads to lower first crack strength, beneficial for microcrack initiation and multiple cracking. This study generates fundamental knowledge linking processing-microstructure-performance of PP-LC3-ECC. This class of low embodied carbon ECC with tight crack width is expected to contribute to reducing the carbon footprint of the built environment.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Compositional phase stability of correlated electron materials within DFT+DMFT

Predicting the compositional phase stability of strongly correlated electron materials is an outstanding challenge in condensed matter physics, requiring precise computations of total energies. In this work, we employ the density functional theory plus dynamical mean-field theory (DFT+DMFT) formalism to address local correlations due to transition metal $\textit{d}$ electrons on compositional phase stability in the prototype rechargeable battery cathode material Li x CoO 2 , and detailed comparisons are made with the simpler DFT+$\textit{U}$ approach (i.e., the Hartree-Fock solution of the DMFT impurity problem). Local interactions are found to strongly impact the energetics of the band insulator LiCoO 2 , most significantly via the $E_g$ orbitals, which are partially occupied via hybridization with $\textit{O p}$ states. We find CoO 2 and Li 1/2 CoO 2 to be moderately correlated Fermi liquids with quasiparticle weights of 0.6–0.8 for the $T_{2g}$ states, which are most impacted by the interactions. Further, as compared to DFT+$\textit{U}$, DFT+DMFT considerably dampens the increase in total energy as $\textit{U}$ is increased, which indicates that dynamical correlations are important to describe this class of materials despite the relatively modest quasiparticle weights. Unlike DFT+$\textit{U}$, which can incorrectly drive Li x CoO 2 toward spurious phase separating or charge-ordered states, DFT+DMFT correctly captures the system's phase stability and does not exhibit a strong charge-ordering tendency. Most importantly, the error within DFT+$\textit{U}$ varies strongly as the composition changes, challenging the common practice of artificially tuning $\textit{U}$ within DFT+$\textit{U}$ to compensate the errors of Hartree-Fock. DFT+DMFT predicts the average intercalation voltage decreases relative to DFT, opposite to the result of DFT+$\textit{U}$, which would yield favorable agreement with experiment in conjunction with the overprediction of the voltage by the strongly constrained and appropriately normed DFT functional.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Direct reuse of graphite and lithium nickel manganese cobalt oxide (NMC) recovered from ultrafast-laser ablation debris in Li-ion battery electrodes

Here we demonstrate that debris collected from the ultrafast-laser ablation of graphite anodes can be directly reused in a lithium-ion battery with little to no negative effects on electrochemical performance. Further, we show that while post-ablation NMC (LiNi 0.33 Mn 0.33 Co 0.33 O) cathode debris will require additional processing before being incorporated into an electrode, its critical materials (nickel, manganese, and cobalt) are not lost during ablation and can be recovered for recycling. Pre- and post-ablation materials are characterized with a suite of diagnostics, including SEM, TEM, X-ray CT, EDS, and XRD, to study changes in material morphology, composition and crystal structure. Graphite exhibited little to no morphological or compositional changes and a slight annealing of its crystal structure. NMC underwent profound morphological and crystallographic changes, but retained its elemental composition. Finally, post-ablation materials were re-manufactured into electrodes and cycled vs lithium metal. Graphite showed equal or better capacity and Coulombic efficiency compared to pre-ablation electrodes, while the post-ablation NMC exhibited severely reduced electrochemical performance. Furthermore, we discuss the outlook for application of this work to advanced manufacturing lines that produce next-generation, laser-patterned electrodes.

25 ENERGY STORAGE↗