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At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Acceleration of Pd-V intermetallic diffusion by hydrogen

Vanadium-based membranes have great potential for hydrogen purification due to their perfect selectivity, high permeability, and relatively low cost. With appropriate surface cleaning, V efficiently permeates hydrogen at elevated temperature, but performance declines due to its affinity to absorb impurities. Here, the application of palladium thin films maintains a clean surface that catalyzes hydrogen dissociation and recombination. Hydrogen permeation in Pd-V-Pd membranes initially reach theoretical permeability, but declines due to Pd-V interdiffusion. The objective of this work was to quantify the intermetallic diffusion process as a function of temperature and ambient. Pd-V composites were subjected to various annealing treatments and characterized using Auger electron spectroscopy, X-ray diffraction, and energy dispersive X-ray spectroscopy, as well as correlated to measurements of membrane permeability. In an inert environment Pd-V interdiffusion was observable as low as 300 °C, and the diffusion coefficient had an activation energy of 44 kJ/mol. Furthermore, the presence of hydrogen at partial pressures > 10 kPa accelerated interdiffusion six-fold at T = 400 °C. Membrane performance degraded with an activation energy 75 kJ/mol, suggesting that intermetallic diffusion leads to both a loss of catalytic activity and as well as degradation of bulk permeability. These findings provide a baseline for evaluating hydrogen permeable interdiffusion barriers to overcome these challenges.

08 HYDROGEN↗

DOE OSTI2020

GaAs solar cells grown on intentionally contaminated GaAs substrates

III-V materials such as GaAs and GaInP have some of the best electronic and optical properties of any semiconductor materials, but deposition of these materials relies on high-quality single crystal GaAs substrates for their superior performance. Unfortunately, the cost of these substrates makes these high-efficiency devices only accessible in high-value or niche markets. In this study, we explore the effect of growing bulk GaAs crystals with lower purity input materials in order to reduce their cost. We observe that intentional impurities added to the melt before the crystal growth occurs segregate to the top of the boule. Single junction GaAs solar cells grown on substrates made from contaminated boules showed no performance degradation compared to a high-purity control substrate. These results suggest that lower purity Ga and As source materials can be used during crystal growth to reduce the cost of substrates.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Understanding failure in austenitic steels: Key considerations for molten salt storage in CSP applications

Owing to their excellent properties, austenitic stainless steels are extensively used in boilers, furnaces, molten salt tanks, and other applications that are subjected to extreme mechanical loads and high-temperature conditions. Their high corrosion and creep resistance make them suitable for high-temperature operating environments. Additionally, good fatigue resistance and favorable mechanical and visual properties are essential. However, the premature failure of several components at elevated temperatures has been previously reported. Although the failure analysis of components in service is complex, processes such as cold work and welding have been identified as contributing factors to the performance degradation of these steels. This study aims to analyze the various documented failure modes and mechanisms in austenitic steels, including creep, cracking, stress relaxation cracking, and fatigue, to better understand the multi-objective design requirements for these alloys as structural materials for high-temperature molten salt tanks in Concentrating Solar Power (CSP) plants. Stabilized austenitic grades, such as AISI 347H, demonstrate superior resistance to creep and corrosion-related degradation when compared to non-stabilized grades like AISI 316L at temperatures relevant to concentrated solar power (CSP) applications. In contrast, nickel-based alloys offer enhanced corrosion resistance, albeit at a higher cost. This review underscores that creep, stress relaxation cracking, and thermo-mechanical fatigue are the predominant long-term failure risks in CSP hot tanks.

14 SOLAR ENERGY↗

DOE OSTI2020

Inhomogeneous distribution of lithium and electrolyte in aged Li-ion cylindrical cells

Carbonate-based electrolytes in Li-ion batteries exhibit long range order in a frozen state, which enables their non-destructive analysis by diffraction methods. In the current study the spatial distribution of lithium and electrolyte inside the graphite anode was determined in cycled Li-ion cells using monochromatic spatially-resolved neutron diffraction measurements at 150 K. The results indicate a loss of lithium and electrolyte and their non-uniform distribution in the graphite anode in aged Li-ion cells. The observed lithium and electrolyte losses are directly correlated with two electrochemical performance degradation mechanisms, which are responsible for the cell capacity fade.

18650-type↗

DOE OSTI2020

Modulating electrode utilization in lithium-ion cells with silicon-bearing anodes

The stability of silicon-containing anodes can, in principle, be extended by constraining these electrodes to limited states of lithiation. Partial utilization of the anode lessens the volume changes experienced by silicon particles during cycling, which can mitigate mechanisms of performance degradation. In full-cells, anode utilization can be modulated by adjusting the relative capacities of the negative and positive electrodes - the N/P ratio. Here, we examine how the N/P ratio affects the long-term stability of Si-based full-cells, and investigate how this parameter would impact the cost and energy of realistic Li-ion cells. Here, we show that, for some configurations, cell failure due to rapid anode degradation can only by avoided at higher N/P ratios. The price of this enhanced stability is accelerated impedance rise at the cathode. Surprisingly, when electrode expansion is taken into account, increasing N/P ratio can actually increase the specific energy and energy density of silicon-rich Li-ion cells.

25 ENERGY STORAGE↗

DOE OSTI2024

Computational modeling of coupled mechanical damage and electrochemistry in ternary oxide composite electrodes

Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here, in this work, we develop a computational framework that integrates electrochemistry of a LiNi x Mn y Co 1−x−y O 2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.

Battery degradation↗

DOE OSTI2026

Robust dynamic operation of high temperature electrolysis solid oxide cells

Here, this study evaluates the durability of Ni/YSZ-supported SOECs under dynamic operating conditions relevant to real-world applications. Systematic tests were conducted to assess cell performance under steam cycling (3 to 75% humidified H 2 ), mode cycling between SOFC and SOEC operation, thermal cycling (150 to 750 °C at OCV, and 600 to 800 °C at 1.3V), and redox cycling (between 50% humidified H 2 and 50% humidified N 2 ). Steam cycling, mode cycling, and thermal cycling at OCV do not significantly accelerate performance degradation. Thermal cycling at 1.3V caused minimal damage within 600 to 800 °C. Full redox cycling (multi-hour oxidation holds) induced cell structural failure, while partial redox cycling (0.5 h holds) was tolerated. Extensive characterization revealed some material evolution, namely Sr and Co secondary phase formation, within the oxygen electrode due to La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ instability, especially for steam cycling and mode cycling. Minimal changes were identified within the Ni-YSZ fuel electrodes. These findings provide critical insights into SOEC reliability under dynamic conditions, supporting their application in dynamic or intermittent energy systems.

08 HYDROGEN↗

DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗

DOE OSTI2020

Proton irradiation damage and annealing effects in ON Semiconductor J-series silicon photomultipliers

Silicon photomultipliers (SiPMs) have become popular light conversion devices in recent years due to their low bias voltage and sensitivity to wavelengths emitted from common scintillating materials. These properties make them particularly attractive for resource-constrained missions such as space-based detector applications. However, the space radiation environment is known to be particularly harsh on semiconductor devices, where high particle fluences can degrade performance over time. The radiation hardness of a particular SiPM, manufactured by ON Semiconductor (formally SensL), has yet to be studied with high energy protons, which are native to the space radiation environment. As such, to study these effects we have irradiated groups of two SiPMs to four different fluences of 800 MeV protons delivered by the accelerator at the Los Alamos Neutron Science Center. Fluences of 1.68 × 10 9 , 1.73 × 10 10 , 6.91 × 10 10 , and 1.73 × 10 11 protons cm –2 , and their corresponding estimated doses of 0.15, 1.55, 6.19, and 15.5 kRad, were chosen based on estimates of the potential exposure a SiPM might receive during an interplanetary space mission lasting 10 years. We report the effects these doses have on dark current and the self-annealing time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Performance characterization of 5×5×12 mm 3 virtual Frisch-grid TlBr detectors

Thallium bromide (TlBr) is a promising material for semiconductor gamma-ray detectors due to its high atomic number (Tl = 81, Br = 35), high density (7.56 g/cm 3 ), and relatively low melting point (460 °C). These properties give TlBr several competitive features, including high detection efficiency, good energy resolution, and lower fabrication costs, making it suitable for applications requiring compact, highly sensitive gamma-ray detection systems. Recent developments of TlBr detectors have progressed toward thicker crystals, up to 10–20 mm, configured as pixelated or virtual Frisch-grid (VFG) devices with volumes >1 cm 3 . Despite these advances, challenges remain in practical implementation, including material and fabrication issues that affect energy resolution, device stability, performance, and longevity. These challenges are similar to those faced by HgI 2 and lead-halide perovskite detectors. Here, this study presents results from characterizing over 100 position-sensitive 5×5×12 mm 3 virtual Frisch-grid detectors fabricated by Radiation Monitoring Devices Inc. For a prototype Radioisotope Identification Device (RIID). The work identified factors limiting the performance and longevity of TlBr detectors, including stochastic response variations that limit the energy resolution to 2–3 % (at 662 keV) for most devices, performance degradation associated with defect decoration, and contact delamination. Addressing these issues advances the use of TlBr and similar materials for reliable, high-resolution gamma-ray spectroscopy.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Iterative methods in GPU-resident linear solvers for nonlinear constrained optimization

Linear solvers are major computational bottlenecks in a wide range of decision support and optimization computations. The challenges become even more pronounced on heterogeneous hardware, where traditional sparse numerical linear algebra methods are often inefficient. For example, methods for solving ill-conditioned linear systems have relied on conditional branching, which degrades performance on hardware accelerators such as graphical processing units (GPUs). To improve the efficiency of solving ill-conditioned systems, our computational strategy separates computations that are efficient on GPUs from those that need to run on traditional central processing units (CPUs). Our strategy maximizes the reuse of expensive CPU computations. Iterative methods, which thus far have not been broadly used for ill-conditioned linear systems, play an important role in our approach. In particular, we extend ideas from Arioli et al., (2007) to implement iterative refinement using inexact LU factors and flexible generalized minimal residual (FGMRES), with the aim of efficient performance on GPUs. In conclusion, we focus on solutions that are effective within broader application contexts, and discuss how early performance tests could be improved to be more predictive of the performance in a realistic environment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Electronic Spin Relaxation and Clustering in High-Pressure High-Temperature Synthesized Microcrystalline Diamond Particles with Reduced Nitrogen Content

The negatively charged nitrogen-vacancy (NV – ) color center in diamonds is widely studied because of numerous applications of this unique quantum system in sensing and quantum information sciences. While substitutional nitrogen is required to form the NV – centers in diamond, it also yields other paramagnetic defects─primarily the neutrally charged substitutional nitrogen centers (P1)─that decrease NV – spin coherence, which in turn degrades performance in applications. Herein, we investigate high-pressure high-temperature synthesized diamond microparticles (ca. 140–185 μm) having lower─ranging from 3 to 38 ppm─than the typical nitrogen content of type 1b diamond ( ca. 100 ppm and higher) typically used for the production of fluorescent diamond particles with NV – centers. A suite of electron paramagnetic resonance, optically detected magnetic resonance, and nuclear magnetic resonance methods are used to characterize spin properties of P1 and NV – centers in the particles. Upon decreasing the nitrogen content from 29 to 3 ppm, the ensemble NV – T 2 relaxation time increased by about 3-fold as measured directly in the Hahn Echo experiment at magnetic field of 1.2 T. Analysis of electronic relaxation of P1 centers revealed the existence of at least two distinct populations of P1 centers, consisting of fast and slower relaxing spins and allowed for an estimation of local concentrations. Even with <10 ppm nitrogen contents, the analysis indicated a highly heterogeneous distribution of P1 centers, suggesting the possibility of P1 spin clustering even at low nitrogen concentrations. The combined data demonstrate that the particles prepared from HPHT diamond with a low nitrogen content offer improved spin properties that are beneficial for NV – sensing applications.

Carbon↗

DOE OSTI2024

Untapped Potential of Fluoride Ions in Maximizing the Electrochemical Stability of Deep Eutectic Solvents

Advancing the batteries is pivotal to propelling our society toward a sustainable, electrified future. The stability of electrolytes forms the backbone of energy storage systems. This is particularly the case for redox flow batteries (RFBs). Their deployability depends on their longevity and dependability. The presence of unstable electrolytes can trigger undesirable reactions, degrade performance, and lead to battery failure. Aqueous electrolytes, with limited electrochemical stability window (ESW), are prone to hydrogen and oxygen evolution. Conversely, non-aqueous electrolytes offer enhanced stability. In this study, we unveil the ESW of a non-aqueous eutectic solvent, comprising choline fluoride and ethylene glycol—a composition that has eluded experimental investigation until now. Our findings show that the stability window, reduction and oxidation potential limits of deep eutectic solvents, are sensitive to variations in the halide component of the ammonium salt. Furthermore, this work not only highlights the benefits of novel deep eutectic solvents but also sets the stage for their strategic use in future battery electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Decoding the Desorption Mechanism of 2LiH:1Mg(NH2)2 Using Metal Borohydrides

The complex metal hydride 2LiH:1Mg(NH2)2 has emerged as a promising material for stationary hydrogen storage applications, such as seasonal storage or energy backup systems, due to its high volumetric and gravimetric capacities and robust reversibility. However, its widespread adoption is hindered by sluggish reaction rates, performance degradation upon cycling, and improper end-use cases. To address these problems and better understand the desorption pathway, we used metal borohydrides (MBH4; M = Li–Cs) as chemical probes. A thorough analysis of the bulk behavior of all six materials, including hydrogen cycling experiments, X-ray absorption spectroscopy, FTIR, pXRD, solid-state NMR, and ab initio DFT simulations, shows that the borohydride additives decrease the activation energy of hydrogen release by about 20 kJ/mol for MBH4@2:1 materials versus pristine. Furthermore, more surface-sensitive studies show that the amide-to-imide desorption pathway in these materials, while essentially complete in the bulk, is incomplete in the near-surface region, suggesting an “inverse core–shell” desorption mechanism for amide dehydrogenation to imide. The kinetic enhancements produced by MBH4 additives (M = K, Rb, and Cs) are attributed to the destabilization of the amide N–H bond and interaction with the LiH/Mg(NH2)2 interface to promote H–H bond formation. An inverse core-shell mechanism is also operative in the hydrogen desorption for the 2LiH:1LiNH2 system, suggesting this may be a general feature of amides. Given the fast dehydrogenation rate and large gravimetric capacity, these materials satisfy these requirements for telecom backups and seasonal microgrid storage applications.

Absorption↗

DOE OSTI2026

Surfactant Effects on Carbon Black Slurries for Aqueous Flow-Electrode Applications

Aqueous carbon black (CB) slurries are promising for flow-electrode applications due to their low cost and high electrical conductivity. Here, in this study, we investigate the impact of introducing a nonionic surfactant (Tween 20) on the rheological and electrical properties of 5 wt % Vulcan XC72R CB slurries in a 50 mM NaCl aqueous solution. We observe a Tween 20 critical concentration around 1.2 wt % that triggers an abrupt transition from a conductive gel to a high-resistance Newtonian fluid. This sudden network collapse coincides with a 250-fold reduction in settled mass, a 1–2 order of magnitude drop in viscosity, and a nearly 100-fold decrease in slurry conductivity. Centrifuged pellet conductivity measurements support the conclusion that this performance loss is largely due to the destruction of the gel network, rather than increased particle-to-particle resistance. SEM imaging suggests that the network is built from large 10 to 50 μm agglomerates that are fully dissolved above the critical surfactant concentration. We found that subcritical surfactant addition improved slurry shelf stability over 90 days, but that electrochemical cell testing revealed progressive performance degradation during operation. These results highlight the need for strategies that maximize long-term electrical conductivity without compromising viscosity, sedimentation stability, or resistance to accumulation on internal surfaces.

Materials science↗

DOE OSTI2026

Extreme Temperature Cryptography Based On Nitrogen-Incorporated Ultrananocrystalline Diamond

Physical entropy sources that remain stable under extreme temperatures are essential for cryptography in emerging technological frontiers in deep space exploration, geothermal energy harvesting, and nuclear energy. However, conventional semiconductor platforms fail to generate stable and reliable cryptographic keys above 200 degrees C due to performance degradation. Here, we report a diamond-based cryptographic primitive that exploits the defect-rich sp 2 -bonded grain boundary network in nitrogen-incorporated ultrananocrystalline diamond (n-UNCD) film as a robust entropy source to generate cryptographic keys that remain operationally stable even after enduring extreme temperatures of 700 degrees C for 54 h while also surviving thermal cycling between room temperature and 700 degrees C for 48 h. The strength of the generated keys is assessed through several cryptographic metrics such as bit uniformity, entropy, hamming distances, and correlation coefficients, all of which are found to be near their respective ideal values. Moreover, the generated keys pass the NIST SP 800 and SP 800-90B tests and are also resilient to supply bias variations and a regression-based machine learning attack model based on the Fourier series. The robustness of the keys is attributed to the better thermal stability and chemical inertness of the n-UNCD film. This is supported by high-resolution energy-dispersive X-ray spectroscopy (EDS), which shows no significant lateral diffusion of metal atoms into the n-UNCD layer, and by Raman spectroscopy, which reveals no significant changes in the bonding configuration of the n-UNCD structure. Our findings highlight the remarkable potential of n-UNCD film for extreme environment cryptography by expanding the operational limits of conventional hardware security platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Constraining Interlayer Slipping in P2-Type Layered Oxides with Oxygen Redox by Constructing Strong Covalent Bonds

Lattice oxygen redox (LOR) in P2-type layered oxides is an effective strategy to break through the limit of energy density of conventional cathodes due to its high redox potential (>4 V vs. Na + /Na) as well as extra capacity. Nevertheless, LOR induced local structure distortion and irreversible phase transitions cause serious electrochemical performance degradation, hindering the practical applications. In this work, we propose that the generation of the OP4 phase can be replaced with the Z phase by introducing Sb element with higher ionic potential and strong covalent bonds within the TMO 6 octahedron. Z phase transition is realized by constraining interlayer slipping between adjacent TM layers compared to OP4, which reduces the strain in the layered structure, lowers the Na+ diffusion energy barrier and creates more efficient Na+ diffusion channels. Consequently, Sb-substituted oxides demonstrates excellent kinetics, rate capability (79 mAh g -1 at 1 A g -1 ) in half cell and a high energy density of 487 Wh kg -1 (on cathode) in full cell.

25 ENERGY STORAGE↗

DOE OSTI2026

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (CHPS) with forecasters-in-the-loop. Results demonstrate that forecasters-in-the-loop systems consistently outperform ML models in both general forecasts and flood alerting across lead times up to 96 hr, even when ML models use observed forcings, while CNRFC operational process relies on biased weather forecasts. Our analysis reveals that forecaster expertise maintains forecast reliability despite inaccurate precipitation inputs, with human-guided systems showing superior performance degradation characteristics at extended lead times. These findings highlight the irreplaceable value of human expertise in operational forecasting and caution against overstating current ML capabilities in real-world applications.

Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI ↗