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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Regulatory Approach to Accelerated Deployment for New Material or Applications in Advanced Reactors

New advanced reactor developers may embark on deploying materials in novel and unique environments where a material’s long-term performance may be in question or unknown. In many cases, sufficient test data may not exist for the material in the desired application, or that no prescript or applicable regulatory testing requirements are available to demonstrate the safety of the application explicitly. This study endeavors to develop the framework for a regulatory and technical approach for safe and predictable accelerated materials use in advanced reactors guided by 10 CFR Part 50.43(e)(1), Advanced Reactor Design Criteria contained in Regulatory Guide 1.232, and Reliability Integrity Management Program (RIM) principles.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Radiological Characterization of Hard to Measure Nuclides Using Accelerator Mass Spectrometry (AMS) - 20140

In this project irradiated heavy concrete was characterized regarding hard to measure nuclides like Ca-41 using Accelerator Mass Spectrometry (AMS). Concrete samples were irradiated with a known neutron flux and the Ca-41 content was investigated via AMS measurements. The comparison of the results with calculations shows a very good agreement in the Ca-41 content. The project revealed that AMS can be used to characterize irradiated concrete and contributes to improve the methodological basis of the radiological characterization and to extending the validation of nuclide vectors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Nb 3 Sn Superconducting Cavities by Bronze Routes for Accelerator Stewardship

This project explored the challenges of making Nb 3 Sn superconducting radio-frequency (SRF) cavities by bronze routes. The SRF research community has prioritized Nb 3 Sn for many reasons, including the possibility to operate linacs for stewardship applications in continuous-wave mode with compact cryogenic systems. Existing Nb 3 Sn approaches use a cavity body made of niobium and apply tin to the polished cavity interior, a starting point that contains the main costs of present niobium cavity technology. Further complexity is then added because a high vacuum and temperature >1100 °C must be used to prevent formation of undesirable Nb-Sn phases, de-gas interstitial contaminants, and maintain the high purity of the Nb body. The project sought a more cost-effective solution for the same performance opportunities by exploiting the Cu-Sn-Nb diffusion reactions used to make Nb 3 Sn superconducting wires for magnets, which can be carried out at ~700 °C and are compatible with much cheaper copper cavity bodies. The project also sought understanding of the materials science and factors that affect superconducting properties. The project has impacted SRF cavity technology by demonstrating feasibility of alternative routes that enable compact, high-power electron accelerators for energy, security, environment, medical, and commercial applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Assessment of Accelerated Stress Testing Data for Silicon Photovoltaics Using Tensor Decomposition Methods

The photovoltaic (PV) industry is simultaneously targeting long warranties and new materials/designs for high-energy-yield modules, requiring an advanced methodology to forecast long-term durability of products with un-proven materials combinations. Extended, sequential, and combined stress testing methods are gaining popularity for assessing durability of PV modules/materials beyond the early-stage mortalities. Importantly, multiple degradation mechanisms can proceed simultaneously, and their separate contributions to the overall power loss should ideally be quantified. This work examines the use of data-driven tools towards developing a strategy for faster learning cycles in accelerated stress testing.

accelerated stress testing↗
DOE OSTI2024

Accelerated CO2 Storage Optimization Using Multi-Resolution Fourier Neural Operator at the Illinois Basin Decatur Project (IBDP)

This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.

accelerated CO2 storage optimization↗
DOE OSTI2024

Dynamic Scaling Analysis of Accelerated Irradiation Testing on Additive Manufacturing Materials by Positron Annihilation

The timely applications of Additive Manufacturing (AM) materials in nuclear environments require accelerated irradiation tests, mainly ion irradiation to enable rapid prototyping. Low dose ion irradiation would cause sub-nanostructure changes by generation of lattice defects, vacancies, vacancy clusters and voids and void swelling caused by cellular dislocations. Positron Annihilation Lifetime (PAL), a novel technology, sensitive towards sub-nanostructure morphology with high accuracy (about 10-7 vacancy per atom), supported by Transition Electron Microscope (TEM) would be applied to identify the type and total size of the defects. The subsequent PAL measurements and TEM surface studies would be followed by PAL analysis that includes sophisticated trapping model. The PAS results would become an input to dynamic scaling analysis (that predicts radiation effects from low dose studies for high dose effects), which incorporate mean-field theory model. The final effect is an in-depth understanding of the microstructure evolution of AM materials under ion irradiation which can be extrapolated to the studies of neutron irradiation, since ion-irradiation takes less time and do not cause the irradiation hazard. The working hypothesis is that PAL technology, that have excellent sensitivity to low-defect concentration would help to identify ion-induced material damage on the atomic and nano-scale level, which then could be extrapolated to understand the neutron damage better.

accelerated irradiation testing↗
DOE OSTI2023

ECAR-6601 Rev 1 MARVEL Project Seismic Accelerations

The MARVEL reactor design requirements are currently listed within TFR-2576. Several MARVEL subsystems are required to be designed to seismic criteria of IBC 2015. This Engineering Analysis (EA) report documents the accelerations that need to be used in an equivalent-static method seismic analysis to satisfy this requirement.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2022

A Computational Framework to Accelerate the Discovery of Perovskites for Solar Thermochemical Hydrogen Production: Identification of Gd Perovskite Oxide Redox Mediators

A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Role of Histidine‐Containing Peptoids in Accelerating the Kinetics of Calcite Growth

Carbonate mineralization, the conversion of CO 2 into stable, thermodynamically favorable carbonate minerals, offers a promising strategy for permanent and environmentally friendly carbon storage, with minimal risk of long-term leakage and minimal monitoring requirements. Drawing inspiration from carbonic anhydrase (CA), a family of zinc-containing metalloenzymes that catalyze the hydration of CO 2 to bicarbonate and promote carbonate precipitation, a class of histidine-containing peptoids was designed that is capable of coordinating with Zn 2+ ions to act as CA mimetics for accelerating calcite step growth. In situ atomic force microscopy (AFM) measurements reveal that these peptoids significantly enhance step advancement, with a more pronounced effect observed when combined with Zn 2+ ions and under higher calcium-to-carbonate activity ratios, indicating that peptoids facilitate the incorporation of CO 3 2− ions at step edges. Solution NMR and 3D atomic force microscopy (3D AFM) analyses show that the coordination of peptoids with Zn 2+ promotes both the deprotonation of HCO 3 − to CO 3 2− and restructures the interfacial hydration layers of calcite, collectively lowering the activation barrier for step growth. These findings establish a design framework for sequence-defined polymers to regulate carbonate mineralization, offering promising applications in CO 2 capture and long-term storage.

CO2 mineralization↗
DOE OSTI2026

Accelerating Discovery to Deployment: Argonne's Materials Engineering Research Facility (MERF) and Its Role in Scaling Materials Technologies for Water and Resource Solutions

The U.S. Department of Energy (DOE) national laboratories represent a unique class of government‐owned, contractor‐operated research institutions dedicated to conducting research and development (R&D) related activities that address national priorities, supporting and advancing the DOE mission. They play a vital role in sustaining U.S. innovation capacity, stewarding the nation's technical base, and nurturing science and technologies. In this perspective, we highlight the processing science and scaleup capabilities of the Materials Engineering Research Facility (MERF) at DOE's Argonne National Laboratory to demonstrate how DOE National Laboratories bridge fundamental science and applied technology development to accelerate deployment. Case studies are presented on selective membranes for critical mineral recovery, sensors for per‐ and polyfluoroalkyl substances (PFAS) detection, surface functionalization via atomic layer deposition (ALD) and sequential infiltration synthesis (SIS), and lithium recovery from battery recycling waste streams using a novel electrodialysis process. These examples underscore MERF's role in translating innovative technologies into practical solutions for renewable water and critical resource recovery, which also leverage Argonne's analytical and computational capabilities. This perspective also outlines mechanisms for collaborating with the DOE national laboratories to strengthen partnerships across government, the national laboratories, academia, and industry.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes using Artificial Neural Networks

Abstract Previous efforts to understand structure‐function relationships in high ionic conductivity materials for solid state batteries have predominantly relied on density functional theory (DFT‐) based ab initio molecular dynamics (MD). Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a hundred atoms. Here, an artificial neural network (ANN) is trained to accelerate the calculation of high accuracy atomic forces and energies used during such MD simulations. After carefully training a robust ANN for four and five element systems, nearly identical lithium ion diffusivities are obtained for Li 10 GeP 2 S 12 (LGPS) when benchmarking the ANN‐MD results with DFT‐MD. Applying the ANN‐MD approach, the effect of chlorine doping on the lithium diffusivity is calculated in an LGPS‐like structure and it is found that a dopant concentration of 1.3% maximizes ionic conductivity. The optimal concentration balances the competing consequences of effective atomic radii and dielectric constants on lithium diffusion and agrees with the experimental composition. Performing simulations at the resolution necessary to model experimentally relevant and optimal concentrations would be infeasible with traditional DFT‐MD. Systems that require a large number of simulated atoms can be studied more efficiently while maintaining high accuracy with the proposed ANN‐MD framework.

Rao, Karun K.↗
DOE OSTI2021

Ultrahigh-Rate and Long-Life Zinc–Metal Anodes Enabled by Self-Accelerated Cation Migration

Aqueous zinc ion batteries are receiving unprecedented attention owing to their markedly high safety and sustainability, yet their lifespan particularly at high rates is largely limited by the poor reversibility of zinc (Zn) metal anodes, due to the random ion diffusion and sluggish ion replenishment at reaction interface. In this work, we propose a tunnel-rich and corona-poled ferroelectric thin film coating for Zn metal anode to tackle above conundra. It is demonstrated that the poled ferroelectric coating can better deconcentrate and self-accelerate ion migration at coating/Zn interface upon electroplating process than untreated ferroelectric coating and bare Zn, thus enabling a compact and horizontally-aligned Zn morphology even at ultrahigh rates. Notably, a maximal cumulative plating capacity of over 6500 mAh cm -2 (at 10 mA cm -2 ) is achieved for the surface-modified Zn metal anode, showing extraordinary reversibility of Zn plating/stripping. This work provides new insights in stabilizing Zn metal electrodeposition at the scale of interfacial ion diffusion.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Abstract Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.

Sendek, Austin D.↗
DOE OSTI2023

Accelerated Short Circuiting in Anode-Free Solid-State Batteries Driven by Local Lithium Depletion

“Anode-free” solid-state batteries (SSBs), which have no anode active material, can exhibit extremely high energy density (≈1500 Wh L -1 ). However, there is a lack of understanding of the lithium plating/stripping mechanisms at initially lithium-free solid-state electrolyte (SSE) interfaces because excess lithium metal is often used. Here, it is demonstrated that commercially relevant quantities of lithium (>5 mAh cm -2 ) can be reliably plated at moderate current densities (1 mA cm -2 ) using the sulfide SSE Li 6 PS 5 Cl. Investigations of lithium plating/stripping mechanisms, in conjunction with cryo-ocused ion beam (FIB) imaging, synchrotron tomography, and phase-field modeling, reveal that the cycling stability of these cells is fundamentally limited by the nonuniform presence of lithium during stripping. Local lithium depletion causes isolated lithium regions toward the end of stripping, decreasing electrochemically active area and resulting in high local current densities and void formation. This accelerates subsequent filament growth and short circuiting compared to lithium-excess cells. Despite this degradation mode, here it is shown that anode-free cells exhibit comparable Coulombic efficiency to lithium-excess cells, and improved resistance to short circuiting is achieved by avoiding local lithium depletion through retention of thicker lithium at the interface. These new insights provide a foundation for engineering future high-energy anode-free SSBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Coupling reduced‐order modeling and coarse‐grained CFD‐DEM to accelerate coal gasifier simulation and optimization

Abstract High fidelity three‐dimensional (3‐D) numerical simulations of commercial‐scale coal gasifiers are very time consuming and expensive. This article proposes a reduced‐order modeling approach that uses quasi one‐dimensional (1‐D) CFD–DEM simulation results to serve as an accurate initial condition for the 3‐D simulation, which significantly shortens the physical time from several hours to minutes to achieve steady states. The 3‐D simulation employs a previously validated coarse‐grained CFD‐DEM approach, which further accelerates the simulation. Both 1‐D and 3‐D simulations adopt a particle shrinkage scheme to correctly account for the particle volume change due to chemical reaction and thus provides an accurate bed height. Comparison with available experimental measurements of the bed temperature and synthesis gas composition is used to validate the simulation. The final syngas composition and flowrates are strongly affected by the fuel or gasification agent rate and process operating conditions as expected. This approach is then used to optimize two important operating conditions (air and steam flows) to achieve specific gasifier performance goals for the production of Fischer–Tropsch synthesis gas and production of fuel gas for gas turbines.

Yu, Jia↗
DOE OSTI2021

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters

Abstract Although the coordinates of the metal atoms can be accurately determined by X‐ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep‐learning approach that can accelerate determination of hydride locations in single‐crystal X‐ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu 25 H 10 (SPhCl 2 ) 18 ] 3− and [Cu 61 (S t Bu) 26 S 6 Cl 6 H 14 ] + whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry.

Wang, Song↗
DOE OSTI2021

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters

Although the coordinates of the metal atoms can be accurately determined by X-ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep-learning approach that can accelerate determination of hydride locations in single-crystal X-ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu 25 H 10 (SPhCl 2 ) 18 ] 3- and [Cu 61 (S t Bu) 26 S 6 Cl 6 H 14 ] + whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Trigonal Planar Bis (carbene)Cu(I) Complexes Enable Divergent H 2 Activation with H 2 O for Accelerated Olefin Hydrogenation

CuH-catalyzed olefin hydrogenation is rare compared to those of carbonyl-derived substrates. Olefin insertion into Cu–H to form Cu-alkyl is ubiquitous; however, subsequent H 2 activation remains unknown to our knowledge. Herein, we investigated the transformations of β-H elimination, H 2 cleavage, and catalytic olefin hydrogenation in a series of linear and trigonal planar Cu(I)-alkyl complexes supported by monodentate N-heterocyclic carbene and bidentate naphthyridine- bis (carbene) ligands, respectively. Contrary to unreactive linear species, trigonal planar variants promote β-H elimination, hydrogenolysis, and catalytic hydrogenation of unactivated alkenes at mild temperatures and H 2 pressure. The rare isolation of a naphthyridine- bis (carbene)CuH monomer further affirms two predominant competing pathways for H 2 cleavage of metal–ligand cooperativity at Cu(I)-alkyl or internal electrophilic substitution at Cu(I)-OH. Employing either isolated or in situ generated Cu(I)-OH complex, via protonolysis of alkyl precatalyst by adventitious water, significantly accelerated catalysis compared to that operating primarily by the metal–ligand cooperativity pathway. DFT calculations and energy decomposition analysis on the disparate β-H elimination reactivity between linear and trigonal planar tert-butyl complexes and the mechanism of H 2 activation at a hydroxide complex, indicate that coordination geometry at Cu(I) and properties of the naphthyridine- bis (carbene) ligand are integral to the transformations reported here.

ALMO-EDA↗