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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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DOE OSTI2020

Studies of surface adsorbate electronic structure and femtochemistry at the fundamental length and time scales. Final report

The electronic structure and ultrafast (10-15 s-femtosecond timescale) electron dynamics were investigated for clean and atom/molecule covered metal surfaces. The studies were performed by scanning tunneling microscopy (STM) to measure the structure of adsorbed atoms and molecules on metal surfaces, and to investigate their electronic properties. The electronic structure of the observed molecular networks was calculated by electronic structure theory in collaboration with Prof. Jin Zhao, who is a long-time collaborator, a Professor at the University of Science and Technology of China, and holds an Adjunct Professorship at the University of Pittsburgh. We also investigated the electronic properties of C60 molecules when they are templated by corrugated black phosphorous surfaces. We found unexpected charge delocalization that is enabled by the templating. This research was done in collaboration with Professor Min Feng at the Wuhan University, and who also holds an Adjunct Professorship at the University of Pittsburgh. Moreover, the electronic structure and electron dynamics in metal surfaces were investigated by time-resolved photoemission electron spectroscopy. The focus of ultrafast spectroscopy has been on the plasmonic response of silver surfaces. One direction has been to develop multidimensional (energy, momentum, and time) photoelectron spectroscopy of the coherent response of solid surfaces. This method was applied to study the collective electron excitations known generally as plasmons, which screen optical fields from penetration into metals. Although this collective response has been known for more than 60 years and is used extensively to deposit optical energy into metals, how this happens is poorly known. We investigated the plasmonic response of silver at the point where the dielectric response passes through zero and bulk plasmon is excited by light. We discovered that the plasmon excitation decays by exciting electrons from the Fermi level of a metal, which is contrary to what is believed in the plasmonic science community. This research has been performed in collaboration with Dr. Marcel Reutzel, who was a postdoctoral fellow working on this research at the University of Pittsburgh, and now has a faculty position at the University of Göttingen in Germany. Prof. Branko Gumhalter from the Institute of Physics in Zagreb contributed on the theory of plasmonic decay processes. Furthermore, we investigated the Floquet engineering of electronic bands in metals leading to multiphoton photoemission and above threshold photoemission. Finally, we demonstrated that it is possible to change the electronic structure of metals by application of optical fields. Our studies indicated that this happens on subfemtosecond time scale and could potentially be used in ultrafast information processing and quantum computation. Related document information

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Expanding the Solar Workforce through the Illinois Community College System

The Illinois Green Economy Network (IGEN) is a consortium of all 39 Illinois community college districts working to grow the green economy in the state. The network aims to build capacity in sustainability and transform education by connecting business/industry and educators to resources, curating information and tools, cultivating partnerships, and advocating for change. Led by IGEN, the goal of the “Expanding the Solar Workforce through the Illinois Community College System” project was to create and sustain a comprehensive, statewide strategy that equips education and training providers, workers, businesses, and communities with resources and tools to prepare a solar workforce. A cohort of five Illinois community colleges participated in the program based on their capacity and interest, as well as their potential for and proximity to industry growth in the state: City Colleges of Chicago, John A. Logan College, Kankakee Community College, Lewis and Clark Community College, and Lincoln Land Community College. The project had three Objectives, 1) Address an unmet need by increasing the pool of skilled solar workers, 2) Keep pace with evolving needs of the solar workforce by improving programs to be more accessible and industry-driven, 3) Strengthen IGEN’s capacity as a statewide resource for solar education, training, and workforce initiatives. Overall, over 400 students were trained in solar installation practices, with over 40 businesses engaged. Additionally. the project spurred a number of innovations and the development of effective practices related to establishing and maintaining a solar installation program at a community college. This project sought to identify effective and scalable practices for developing a solar installation program which included the following components: enrolling students into aligned programs; developing curriculum and courses which meet industry needs; placing students in solar installation or related employment; and sustaining programming past grant funding. Through this project- IGEN was able to identify critical components of a solar installation program. Detailed further in this report these components describe 1) Necessary conditions for establishing a solar installation program, 2) Program structures use cases for different contexts, 3) Effective practices related to student pipeline development and program throughout. As a result of this project- all five participating community colleges have expressed support for continuing to offer solar installation training to their students. Additionally, three nonparticipating colleges in IGEN have expressed interest in adopting practice, courses, or curriculum developed through this program. IGEN has also committed to making the curriculum, course structure and other materials available on their website for accelerated adoption of effective practices both state and nationwide.

14 SOLAR ENERGY↗

DOE OSTI2026

Sub-melt nanosecond pulsed-laser induced densification and strain-field relaxation in single-crystal diamond

Dislocations and polishing-induced defect networks in synthetic diamond introduce local strain fields and broaden Raman features, limiting performance in optical, thermal, and electronic applications. Laser annealing is emerging as a promising approach to repair surface and near-surface defects in diamond without entering the melt regime, yet surface densification, defect-state modification, and associated structural changes have not been well quantified. In this work, we show that sub-melt nanosecond pulsed-laser annealing (PLA) induces near-surface densification and defect-mediated strain relaxation in single-crystal Chemical Vapor Deposition (CVD) diamond. Single- and two-pulse PLA were applied, and structural evolution was quantified using co-registered ISO 25,178 white-light interferometry, depth-resolved Raman spectroscopy, and cross-sectional STEM with geometric phase analysis (GPA). Across a 5 × 6 grid (n = 30), responsive regions exhibit large reductions in local slope (Sdq 45–65%), developed area (Sdr 60–90%), height spread (Sp, Sz 30–65%), void volume (Vv 57–60%), and roughness amplitude (Sa, Sq 48–57%), consistent with densification of ∼4–6.5 nm. Raman profiling shows narrowing of the diamond line and improved spectral uniformity to depths of ∼2–3 μm. Given that the Raman probing depth significantly exceeds the densified layer thickness, this response is interpreted as consistent with long-range strain-field redistribution originating from the near-surface region. STEM-GPA strain maps further support this interpretation, showing smoother strain fields, suppressed hotspots, and redistribution of localized strain concentrations following PLA. These results are consistent with defect-mediated strain relaxation and densification-driven modification of the near-surface energy state. The approach provides a scalable pathway for improving near-surface structural quality in diamond relevant to electronic, photonic, and quantum applications.

Areal surface metrology (ISO 25,178)↗

DOE OSTI2021

Tracking the phase changes in micelle-based NiGa nanocatalysts for methanol synthesis under activation and working conditions

The hydrogenation of CO 2 into high energy density fuels such as methanol, where the required H 2 is obtained from renewable sources, is of utmost importance for a sustainable society. In recent years, NiGa alloys have attracted attention as promising catalyst material systems for the hydrogenation of CO 2 into methanol at ambient pressures. They thus represent an energy-saving alternative to the Cu-based catalysts employed in today's catalytic industry that require high pressures for the CO 2 hydrogenation. However, the underlying reaction mechanisms for the NiGa system are still under debate. One of the challenges here is to unravel the evolution and coexistence of the different species in the heterogeneous NiGa catalyst system under activation and reaction conditions. To shed light on their evolution under activation in H 2 and their catalytic roles under CO 2 hydrogenation working conditions on well-defined Ni 3 Ga 1 and Ni 5 Ga 3 nanoparticle (NP) catalysts, we employed a multi-probe approach in this study. It included advanced machine learning-based analysis of operando X-ray absorption spectroscopy data combined with operando powder X-ray diffraction and near ambient pressure X-ray photoelectron spectroscopy measurements, as well as reactivity studies using bed-packed mass flow reactors. In addition, we employed atomic force microscopy and scanning transmission electron microscopy for structural characterization. Under H 2 activation at 1 bar total pressure, we concluded the formation of metallic Ni, starting for Ni 3 Ga 1 at 300 °C, and for Ni 5 Ga 3 at 400 °C. At higher temperatures, the formation of NiGa alloys follows. The α'-Ni 3 Ga 1 alloy phase is predominantly formed for the Ni 3 Ga 1 NPs, while the coexistence of α'-Ni 3 Ga 1 , δ-Ni 5 Ga 3 and Ga 2 O 3 phases is observed for the Ni 5 Ga 3 NPs after the H 2 activation. The formation of the Ga 2 O 3 phase also results in the presence of excess metallic Ni. Under CO 2 hydrogenation reaction conditions, Ga partially oxidizes again to form a Ga 2 O 3 -rich particle shell for both NP compositions, yet, to a larger extent for the Ni 3 Ga 1 NPs, which, in turn, feature a higher amount of excess Ni. We reveal that metallic Ni is responsible for the high selectivity of the Ni 3 Ga 1 NPs towards the production of methane in our catalytic tests. In contrast, the Ni 5 Ga 3 NPs display a strong selectivity toward methanol production (>92%), more than one order of magnitude higher than that for the Ni 3 Ga 1 NPs, which we ascribe to the presence of the δ-Ni 5 Ga 3 phase.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Demonstrate new plasticity models for doped UO 2 that capture dislocation mechanisms

In light water reactors, fuel vendors are investigating the use of dopants to modify the properties of UO 2 pellets, with the goal of improving pellet-cladding mechanical interactions during operation. Dopants are expected to ‘soften’ the pellets; that is, the doped pellets have higher plastic deformation than conventional UO 2 . This leads to a reduction in the severity of mechanical pellet-cladding interactions, helping to reduce the hoop strain on the cladding. By minimizing the strain exerted by the pellet on the cladding, it is anticipated that cladding performance under accident conditions can be enhanced (i.e., lowering the risk of burst during a LOCA). Dopants such as chromium (Cr) promote grain growth during pellet fabrication, leading to larger grains; therefore, understanding the link between chemistry, microstructure and mechanical deformation (enhanced creep rates) behavior of UO 2 is critical to helping operators further substantiate the benefits of doping UO 2 . Historically, the nuclear energy industry has relied on empirical models to make assessments of performance. Compared to empirical models, mechanistic physics-based models provide benefits, such as, fewer data points for validation and better extrapolation where experimental data is scarce or non-existent. In this report, Bayesian inference techniques have been applied to a previously developed lower length-scale-informed diffusional creep model. The objective is to i) infer lower-length-scale parameter distributions from available experiment and then ii) determine the uncertainties in the measurable quantity (in this case creep rates) after propagating the inferred lower length scale parameter uncertainties. The approach requires many evaluations of the model, which becomes computationally insurmountable; therefore, a neural-network model is trained to data obtained by sampling the full model over the most important parameters. This neural-network is then used in the Bayesian inference approach to determine probability distributions in the parameter values that represent the uncertainty in the model given what is known from the experiments (posterior). A significant reduction compared to conservative initial (prior) uncertainties is achieved through inference against the experimental data, demonstrating the efficacy of this approach. Furthermore, by accounting for uncertainties in the experimental conditions and sample non-stoichiometry, it is possible to resolve apparent discrepancies in experimental measurements within a self-consistent grain boundary (Coble) creep model that is sensitive to chemistry. This work has been written up and submitted to Nuclear Technology for a special issue on accelerated fuel qualification (AFQ). This uncertainty quantification (UQ) work not only improves the diffusional model, while accounting for uncertainty, but also establishes a framework which can readily be applied to the mechanistic models of dislocation deformation developed in this study. The most likely values from the Bayesian analysis are incorporated into our UO 2 diffusional creep model and a lower length scale-informed irradiation UO 2 creep mechanistic model to generate a dataset. This dataset has been provided to our INL collaborators for training an artificial neural network surrogate model, which will be implemented in the BISON fuel performance code to assess how the results differ from those currently obtained using a fully empirical model and that of using the nominal (uncalibrated) atomic scale parameters in our mechanistic model. Plastic deformation (creep and glide) in UO 2 is a complex phenomenon, governed by multiple underlying processes such as local defect concentrations, applied stresses, and microstructural characteristics. Consequently, there is a need for a meso-scale model with polycrystalline resolution capable of extrapolating to large grain sizes applicable to doped UO 2 , where data is limited and the model can help bridge the knowledge gap. By integrating atomistic data into the polycrystal LApx code, it becomes possible to predict dislocation climb and glide plasticity that simple analytical models cannot accurately represent. The application of atomic-scale data within LApx demonstrated the importance of climb and glide mechanisms in reproducing high-stress UO 2 behavior. Behaviors such as this are crucial to capture and implement in BISON, as parts of the fuel pellet can reach temperatures where glide can occur before pellet cracking. This model which captures dislocation based mechanisms for UO 2 is then used to stand up the doped model accounting for larger grain sizes. It was found that larger grain sizes can lead to enhanced deformation rates in the glide regime, and therefore can help with the pellet cladding mechanical interaction. Therefore if the fuel pellet reaches conditions (stress/temperature) where glide is active, the enhanced creep rates for larger grains in the glide regime (doped UO 2 ) can help with pellet cladding mechanical interactions. Plastic deformation in UO 2 involves multiple mechanisms, including diffusional creep, dislocation climb, and glide. This milestone contains two parts: (1) UQ of a pre-existing lower length scale informed mechanistic diffusional creep model, and (2) development of a new LApx based model for dislocation-mediated creep mechanisms in UO 2 , with application to large-grain doped UO 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗