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DOE OSTI2026

Circumventing thermodynamic limitations in converting carbon dioxide into carbon nanotubes via tandem catalysis

Carbon nanotubes (CNTs) are important materials for electronics and structural composites, but their production still relies on hydrocarbon-based chemical vapor deposition, an energy-intensive and fossil-dependent process, limited by rapid catalyst deactivation. Using CO2 as a carbon feedstock offers a sustainable route for CNT synthesis, yet direct CO2 conversion to CNTs is thermodynamically unfavorable and existing CO2-to-carbon pathways mainly yield amorphous or weakly graphitized solids. Here, we demonstrate a tandem electrochemical–thermochemical (EC-TC) strategy that overcomes these limitations. CO2 is first electrochemically reduced to a tunable mixture of C2H4 and CO, which is directly fed into a thermochemical reactor and converted into CNTs with controllable morphology and high CNT-to-metal mass ratios (~200) over NiFe catalysts at 750 °C. In situ synchrotron-based characterization and density functional theory calculations reveal that CO dissociation and C2H4 decomposition on NiFe alloys cooperatively promote CNT nucleation and sustained growth. This EC-TC strategy establishes a modular route for converting CO2 into value-added carbon nanomaterials.

03 NATURAL GAS↗

DOE OSTI2023

Molybdenum-99 from Molten Salt Reactor as a Source of Technetium-99m for Nuclear Medicine: Past, Current, and Future of Molybdenum-99

Technitium-99m ( 99m Tc), a widely used radioisotope, is used in tens of millions of medical diagnostic procedures annually. However, it is hard to store and must be immediately used upon production due to its short half-life (i.e., 6 h); thus, it is currently produced from 99 Mo, which itself is a result of 235 U fission. The majority of 99 Mo supplies to U.S. patients are currently provided by foreign producers and produced using highly enriched uranium (HEU). In order to minimize the proliferation risks of HEU-based medical isotope production, the U.S. Department of Energy’s National Nuclear Security Administration has funded a program to accelerate the development of technologies to produce 99 Mo without the use of HEU. Today, the global supply of 99 Mo depends on a limited number of nuclear reactors, and production has been interrupted unexpectedly since 2009 due to the fleet’s advanced age. Herein, alternative options for 99 Mo production are discussed, and one potential option is to obtain 99m Tc from molten salt reactors (MSRs). A MSR is a nuclear fission reactor that can operate at or close to atmospheric pressure with liquid fuel, which allows for producing isotopes in a timely manner. In this paper, the past and current production of 99 Mo via nuclear reactors is described, and the future of 99 Mo production by MSRs is discussed. The behavior and chemical properties of molybdenum in fluoride salts in MSRs and the possible extraction methods are also examined in addition to the limitation of current studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor

This study explores data-driven anomaly detection methods to analyze sensor fail- ures in the Advanced Gas Reactor (AGR) nuclear fuel irradiation experiments. Specifically, we examine failures of thermocouples (TCs), which are critical for mon- itoring and controlling in-reactor temperatures during operation. Failures were pri- marily observed during abrupt power transitions and manifested as sensor drop-outs, drifts, or unexplained behavior. We applied three time-series analysis techniques— rolling mean smoothing, matrix profile, and vector auto-regression (VAR)—to de- tect anomalies in TC data prior to failure events. The rolling mean method effec- tively highlighted deviations aligned with reported failures, while the matrix profile provided partial early warning but sometimes flagged normal fluctuations during power-down periods. VAR shows potential in capturing multivariate dependencies but requires further calibration. A rare case of TC drift was also documented, which did not result in failure, underscoring the challenge of building predictive models with sparse positive examples. Our findings demonstrate that traditional statistical tools can aid anomaly detection but have limited predictive power without richer training data. We propose future directions including synthetic data generation, real- time surrogate modeling, and multi-modal feature integration. This work provides a foundation for applying robust anomaly detection frameworks to mission-critical sensor systems in experimental settings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Superconductivity in Ruddlesden–Popper nickelates: a review of recent progress, focusing on thin films

The discovery of superconductivity with Tc ∼ 80 K in the nickelate Ruddlesden-Popper bilayer La3Ni2O7 at high pressure has opened a new platform for unconventional superconductivity, followed by the subsequent observation of superconductivity in trilayer La4Ni3O10, also at high pressure. Remarkably, ambient-pressure superconductivity was also observed recently in La3Ni2O7 ultra-thin films when grown on substrates that provide compressive strain. This discovery significantly extends the type of experimental techniques that can be used in nickelates, previously limited due to the high-pressure constraint. Discussing the similarities and differences among these nickel oxides will provide new insights into understanding the mechanism of high-Tc superconductivity in correlated electron systems. In this paper, we review the experimental and theoretical progress on Ruddlesden–Popper nickelates, with emphasis on thin films, and discuss future perspectives and research directions.

Zhang, Yang [ORNL] (ORCID:0000000336235883)↗

DOE OSTI2023

Evidence for multi-decadal fuel buildup in a large California wildfire from smoke radiocarbon measurements

In recent decades, there has been a significant increase in annual area burned in California's Sierra Nevada mountains. This rise in fire activity has prompted the need to understand how historical forest management practices affect fuel composition and emissions. Here we examined the total carbon (TC) concentration and radiocarbon abundance (Δ 14 C) of particulate matter (PM) emitted by the KNP Complex Fire, which occurred during California's 2021 wildfire season and affected several groves of giant sequoia trees in the southern Sierra Nevada. During a 26 h sampling period, we measured concentrations of fine airborne PM (PM 2.5 ), as well as dry air mole fractions of carbon monoxide (CO) and methane (CH 4 ), using a ground-based mobile laboratory. We also collected filter samples of PM 2.5 for analysis of TC concentration and Δ 14 C. High correlation among PM 2.5 , CO, and CH 4 time series confirmed that our PM 2.5 measurements captured variability in wildfire emissions. Using a Keeling plot approach, we determined that the mean Δ 14 C of PM 2.5 was 111.6 ± 7.7‰ (n = 12), which was considerably enriched relative to atmospheric carbon dioxide in the northern hemisphere in 2021 (-3.2 ± 1.4‰). Combining these Δ 14 C data with a steady-state one-box ecosystem model, we estimated that the mean age of fuels combusted in the KNP Complex Fire was 40 years, with a range of 29–57 years. These results provide evidence for emissions originating from woody biomass, larger-diameter fine fuels, and coarse woody debris that have accumulated over multiple decades. This is consistent with independent field observations that indicate high fire intensity contributed to widespread giant sequoia mortality. With the expanded use of prescribed fires planned over the next decade in California to mitigate wildfire impacts, our measurement approach has the potential to provide regionally-integrated estimates of the effectiveness of fuel treatment programs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Projected increases in tropical cyclone-induced U.S. electric power outage risk

Abstract While power outages caused by tropical cyclones (TCs) already pose a great threat to coastal communities, how—and why—these risks will change in a warming climate is poorly understood. To address this need, we develop a robust machine learning model to capture TC-induced power outage risk. When applied to 900 000 synthetic TCs downscaled from simulated historical and future climate conditions under a strong warming scenario, we find outage risk in the United States and Puerto Rico is expected to increase broadly by the end of the century, with some states seeing increases of 60% and higher. Further, we discover that rising rainfall rates will play an increasingly important role in TC-induced power outage risk as the climate changes, explaining more than 50% of the projected change in risk in some regions. These insights are important for guiding decision-makers in their future outage risk investment and mitigation plans.

Grid Resilience↗

DOE OSTI2021

Quantum magnetism on small-world networks

While classical spin systems in random networks have been intensively studied, much less is known about quantum magnets in random graphs. In this work, we investigate interacting quantum spins on small-world networks, building on mean-field theory and extensive quantum Monte Carlo simulations. Starting from one-dimensional (1D) rings, we consider two situations: All-to-all interacting and long-range interactions randomly added. The effective infinite dimension of the lattice leads to a magnetic ordering at finite temperature Tc with mean-field criticality. Nevertheless, in contrast to the classical case, we find two distinct power-law behaviors for Tc versus the average strength of the extra couplings. This is controlled by a competition between a characteristic length scale of the random graph and the thermal correlation length of the underlying 1D system, thus challenging mean-field theories. We also investigate the fate of a gapped 1D spin chain against the small-world effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Materials preparation, single-crystal growth, and the phase diagram of the cuprate high-temperature superconductor La 1.6-x Nd 0.4 Sr x CuO 4

One branch of the La-214 family of cuprate superconductors, La 1.6-x Nd 0.4 Sr x CuO 4 (NdLSCO), has been of significant and sustained interest, in large part because it displays the full complexity of the phase diagram for canonical hole-doped, high TC superconductivity, while also displaying relatively low superconducting critical temperatures. The low superconducting TC’s imply that experimentally accessible magnetic fields can suppress the superconductivity to zero temperature. In particular, this has enabled various transport and thermodynamic studies of the T = 0 ground state in Nd-LSCO, free of superconductivity, across the critical doping p* = 0.23 where the pseudogap phase ends. The strong dependence of its superconducting properties on its crystal symmetry has itself motivated careful studies of the Nd-LSCO structural phase diagram. This paper provides a systematic study and summary of the materials preparation and characterization of both single crystal and polycrystalline samples of Nd-LSCO. Single-phase polycrystalline samples with x spanning the range from 0.01 to 0.40 have been synthesized, and large single crystals of La 1.6-x Nd 0.4 Sr x CuO 4 for select x across the region (0.07, 0.12, 0.17, 0.19, 0.225, 0.24, and 0.26) were grown by the optical floating zone method. Furthermore, systematic neutron and X-ray diffraction studies on these samples were performed at both low and room temperatures, 10 K and 300 K, respectively. These studies allowed us to follow the various structural phase transitions and propose an updated structural phase diagram for NdLSCO. In particular, we found that the low-temperature tetragonal (LTT) phase ends at a critical doping p LTT = 0.255±0.005, clearly separated from p*.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

β -Technetium: An allotrope with a nonstandard volume-pressure relationship

In this work, we report the synthesis and structure of the second allotrope of technetium, β-Tc. Transformative pathways are accessed at extreme conditions using the laser-heated diamond anvil cell and confirmed with in situ synchrotron x-ray diffraction and Raman spectroscopy. βTc is fully recoverable to ambient conditions, although counter to our DFT calculations predicting a face-centered-cubic lattice, we observe a tetragonal structure (I4/mmm) that exhibits further tetragonal distortion with pressure. β-Tc has an expanded volume relative to the hcp ground state phase, that when doped with nitrogen has an unexpected volume lowering. Such anomalous behavior is possibly indicative of a rare electronic phase transition in a 4d element.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗

DOE OSTI2020

Activation Energy for Solder Bond Degradation: Thermal Cycling of Field-aged Modules at Multiple Upper Temperatures

The reliability of solder joints in the solar cell metallization-interconnect system influences the lifetime of photovoltaic modules. Two field-aged modules-one with Sn 62 Pb 36 Ag 2 solder at the solder joints (Solarex MSX 60), and the other with the standard Sn 60 Pb 40 solder (Siemens M55)-were subjected to a modified thermal cycling (TC) test of IEC 61215. Three sections in each module were maintained at 85 degrees C, 95 degrees C, and 105 degrees C during the 15-minute high temperature dwell time. Current equivalent to the module short-circuit current was injected through the module when the chamber temperature was above 25 degrees C to simulate regular field operation. This novel approach aims to induce thermomechanical fatigue (TMF) at the solder joints and intermetallic compound (IMC) formation at the metal/solder interfaces. The activation energy (E a ) for solder bond degradation was calculated based on the series resistance (R s ) increase in TC testing rather than power drop to avoid the effect of confounding variables. Module-level R s increase in MSX 60 module after TC800 cycles and in M55 module after TC400 cycles was 1.22% and 183.7%, respectively. The E a determined for the module with 2wt% Ag is 0.24 eV and for that with the standard solder is 0.27 eV. The solder bond degradation seems to have been driven by TMF rather than IMC formation for both modules.

14 SOLAR ENERGY↗

DOE OSTI2022

Extended Accelerated Stress Testing (EAST) of Glass/Glass, Glass/Backsheet and Glass/Transparent Backsheet PV Modules: Influence of EVA and POE Encapsulants: Preprint

This paper presents the indoor extended accelerated stress testing (EAST) results of glass/glass (GG), glass/backsheet (GB) and glass/transparent backsheet (GT) modules having identical cells and two different encapsulant types, ethyl-vinyl-acetate (EVA) and polyolefin-elastomer (POE). Six 4-cell modules having the above-mentioned construction combinations were subjected to extended ultraviolet (UV; 600 kWh/m2), damp-heat (DH; 2000 hours) and thermal-cycling (TC; 600 cycles) tests. The post-stress UV fluorescent imaging, electroluminescent imaging, reflectance spectrophotometry and colorimetry results indicated that the grid finger degradation and encapsulant browning are slightly higher in the GG modules compared to the GB modules. The post-stress IV test results indicated, in general, that the GG/EVA modules tend to perform inferior to the GG/POE modules with the EAST evaluation.

EVA↗

DOE OSTI2025

Individual and Simultaneous Imaging of ⁹⁹mTc and ¹⁷⁷Lu With a Preclinical Broad Energy-Spectrum CZT-Based SPECT

Radiopharmaceutical therapy has demonstrated a high efficacy in the treatment of various tumor types. One of the radionuclides already used in the clinic is 177Lu, a beta emitter that also emits several photons imageable with SPECT. Quantitative imaging of 177Lu is critical for developing new radiopharmaceuticals. Energy resolution is an important factor when imaging multiple photon emissions. Solid-state detectors offer a superior performance over scintillators, that are commonly used in commercially-available preclinical SPECT scanners. This study demonstrates the feasibility of 99m Tc and 177Lu quantitative imaging in mouse phantoms, individually and simultaneously, with a SPECT prototype built with four CdZnTe (CZT) detector heads and a custom-designed and energy-optimized parallel-hole tungsten collimator. With a custom implementation of the one-step late (OSL) image reconstruction algorithm, the system is capable of imaging energies from ~70 keV to 250 keV. Above 250 keV, images were significantly affected by septal penetration, consistent with the collimator design. A recovery coefficient within 25% was obtained for activities as low as 2 kBq/mL for 99m Tc and 45% for 177Lu. Compared to a commercial NaI-based preclinical SPECT (VECTor4/CT), our prototype showed a superior energy resolution (< 5% at 140 keV), a similar uniformity with a high-compact design.

Encarnação, Pedro M C C↗

DOE OSTI2020

(Invited) All-Solid-State Batteries Using Li 7 La 3 Zr 2 O 12 Garnet Electrolyte Framework

All-solid-state batteries (ASSB) using LLZO (variants of Li 7 La 3 Zr 2 O 12 ) garnet solid electrolyte with lithium metal anode potentially offer higher energy density and improved safety. Rational design of cell architecture as well as manufacturing scalability are key aspects to consider as the technology readiness level advances. A thick composite cathode layer attached to a thin solid electrolyte layer is desirable to obtain superior energy densities. Furthermore, the architecture within the composite cathode may be engineered to contain directional conduction paths of lithium ions or electrons for enhanced rate capabilities. Here, we demonstrate a functioning bulk-type LLZO based all-solid-state battery with a practical form factor incorporating the above described design concepts. Freeze-tape-casting (FTC), a scalable and environmentally friendly ceramic processing method, is used to construct 3D porous LLZO scaffolds composed of vertical arrays of LLZO walls. The thin solid-electrolyte layer is fabricated by tape-casting (TC). By sintering the stacks of FTC and TC green tapes, porous/dense bilayers and porous/dense/porous trilayers of LLZO frameworks are obtained. An ASSB was constructed using a porous/dense bilayer by infiltrating LiNi 0.6 Mn 0.2 Co 0.2 O 2 cathode powder and carbon black into the porous layer and adhering lithium metal foil to the dense side. We find it crucial to introduce a plastic crystal based soft solid electrolyte to the porous layer to electrochemically connect all cathode components, obviating the need for co-sintering to establish contact. The soft nature of the plastic crystal based solid-electrolyte may be able to accommodate the volume change of the cathode material as it cycles. In this device, which contained no liquid component, the initial discharge capacities were similar to those observed in a lithium ion battery configuration using the same cathode powder at C/10 rates.

25 ENERGY STORAGE↗

DOE OSTI2023

Open Source High Fidelity Modeling Of A Type-5 Wind Turbine Drivetrain

The purpose of this code is to provide a high-fidelity model of Type-5 wind turbine drivetrain for grid integration and transient stability studies. The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system’s inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help mitigate the aforementioned impact. A hydraulic torque converter (TC) and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine unit. A generic and high-fidelity model of a Type-5 wind turbine drivetrain is not openly and widely available for grid integration and transient stability studies. This hinders appropriate assessment of Type-5 wind power plant's contribution to the BPS resilience. This work develops and validates a TC model based on those generally used in automobile's transmission system.

Hussain, Tanveer↗

DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Pronounced Impact of Salinity on Rapidly Intensifying Tropical Cyclones

We show the importance of salinity for rapidly intensifying Atlantic tropical cyclones and demonstrate the potential for improved prediction of rapid intensification through the inclusion of salinity. Tropical Cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well-known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon-Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical-dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near real-time.

Balaguru, Karthik↗

DOE OSTI2025

Toward Understanding the Differences between Mesoscale and Large-Eddy Simulations of Tropical Cyclones

In this work, we investigate the ability of mesoscale and large-eddy simulation (LES) model configurations to predict the mean wind speed profile within the boundary layer of tropical cyclones (TCs). To this end, we perform idealized simulations of five hypothetical intense storms ranging from categories 1 to 5 on the Saffir–Simpson scale and extract time-averaged quantities near the eyewall region. We compare the model-generated data against mean wind speed profiles compiled from dropsondes launched from reconnaissance aircraft operating in the North Atlantic basin. Our analysis shows that mesoscale- and LES-generated mean wind fields display important differences in the boundary layer, including the magnitude of shear as well as the height where their low-level wind speed maxima are located. In addition, a comparison between the two model configurations with the dropsonde data shows that both modeling approaches are unable to capture the typical structure of mean winds in the lower part of the TC boundary layer (10–500 m), calling into question the use of simulations of near-axisymmetric storms for investigating the wind structure of past events. To better understand these differences, we conduct a momentum-budget analysis and show that modeled turbulent fluxes are underestimated in the mesoscale boundary layer parameterization compared to the LES model. Based on the analysis of the horizontal turbulent fluxes and their potential impact on mean flow quantities, a TC-specific boundary layer parameterization may be needed.

17 WIND ENERGY↗