Fundamentals of Structural Materials for Nuclear Energy
Fundamentals of Structural Materials for Nuclear Energy
36 - MATERIALS SCIENCE↗
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One of These Things IS Like the Other: Pursuing a New Taxonomy of Industry for Improved Energy System Modeling
Industrial processes drive the exchange of materials, energy, and currency throughout the economy. These processes are powered by electricity and direct combustion, with variation in their operation even within the same industry. This heterogeneity makes it difficult for large models, including the National Energy Modeling System (US), to project their energy use while remaining tractable. Decarbonization and ensuing changes to the energy system require changes to industrial processes while offering opportunities for process innovation, but the extent and nature of changes are difficult to model with current classification schemes and corresponding data. The North American Industrial Classification (NAICS) is an economic taxonomy of industries, but its categories are less meaningful from an energy and material flow perspective. For example, a facility that makes steel from iron ore in a blast furnace/basic oxygen furnace is categorized under the same NAICS code as a facility that makes steel from scrap in an electric arc furnace despite the scale, use of recycled scrap versus iron ore, and energy use differences in the two facility types. Exploratory analysis is performed on a large dataset used for plant-level energy assessment in order to detect clusters that can aid in better modeling of industry for energy analysis in an evolving system with breakthrough technologies.
28 EE - Advanced Manufacturing Office (EE-5A)↗
Impact of size and thermal gradient on supercooling of phase change materials for thermal energy storage
Phase change material based thermal energy storage has many current and potential applications in the heating and cooling of buildings, battery and electronics thermal management, thermal textiles, and dry cooling of power plants. However, connecting lab scale thermal data obtained with differential scanning calorimetry (DSC) to the performance of large-scale practical systems has been a major challenge primarily due to the dependence of supercooling on the size and temperature gradient of the system. Here we show how a phase change material’s supercooling behavior can be characterized experimentally using common lab scale thermal analysis techniques. We then develop a statistics based theoretical model that uses the lab-scale data on small samples to quantitatively predict the supercooling performance for a general thermal energy storage application of any size, including also allowing for the possibility of temperature gradients. Finally, we validate the modeling methodology by comparing to experimental results for solid-solid phase change in neopentyl glycol, which shows how the model successfully predicts the changes in supercooling temperature across a large range of cooling rates (2 orders of magnitude) and volumes (3 orders of magnitude). By accounting for thermal gradients, the model avoids ~2x error incurred by lumped approximations.
36 MATERIALS SCIENCE↗
Technoeconomic Analysis of Dye Sensitized Solar Cells (DSSCs) with WS 2 /Carbon Composite as Counter Electrode Material
Exploration of clean and renewable energy materials is necessary due to the coming energy crisis and environmental problems. Solar energy is one of the favorable energy sources because of the continuous energy reservoir and its affluence. Silicon-based solar devices are expensive due to their complicated production process, which limits this technology for urban and other commercial applications. Among the third generation of solar cells, Dye-Sensitized Solar Cells (DSSCs) have attracted widespread attention as potential cost-effective alternatives to silicon-based solar cells. In this paper, the commercializing potential of the DSSCs is investigated. A module is introduced where the materials, equipment, and distribution of direct manufacturing costs are calculated. The manufacturing costs and the Levelized Cost of Energy (LCOE) of these DSSCs for a system lifetime of 25 years were determined to be USD 22.40 per m 2 and USD 0.0438 per kWh and the module price of this technology is USD 0.18 per W and the total installed system cost is USD 0.88 per W in Kansas which suggest that this PV technology could challenge other leading PV technologies.
14 SOLAR ENERGY↗
Summary: Nuclear Energy Critical Material Waste Reduction and Supply Chain Solutions Enabled by Advanced Manufacturing
In September 2020, the U.S. government issued an executive order to address the threat to the domestic supply chain from its reliance on critical minerals (CMs) from foreign competitors and to support the domestic mining and processing industry. A national strategy on CMs with impact on the U.S. Department of Energy’s (DOE’s) vision for 2021–2031 was developed. This vision embraces science and technology to re-establish U.S. competitiveness in the CM and material supply chains by (a) scientific innovation and technologies to ensure resilient and secure CMs and maintain a domestic material supply chain, (b) building a long-term minerals and materials innovation ecosystem to foster new capabilities to mitigate CM supply chain challenges, (c) increasing private sector adoption for sustaining the domestic CM supply chain, and (d) coordinating with international partners and federal agencies to diversify global supply chains and ensure the adoption of best practices for sustainable mining and processing (DOE 2021).
36 MATERIALS SCIENCE↗
Energy storage material and method of producing the same
The instant invention includes a spherical porous secondary silicon-based particle and methods for producing the same. The spherical porous secondary silicon-based particle is comprised of agglomerated primary silicon-based nanoparticles. The secondary particle comprises a carbon coating that reduces the effective exposed surface area of the primary particles to the electrolyte, thus improving first cycle efficiency. The secondary particle further comprises porous regions that enable the silicon nanoparticles to expand during lithiation. Advantages include ease of castability with micron-sized spherical particles, ease of mixing spherical particles, ease of flow for spherical particles in various processing steps, and ease with obtaining higher loading, which translates to higher areal capacity and overall energy density of the cell. A readily scalable process for producing the particles using low-cost materials and low-cost processing methods is disclosed.
Holt, Christopher T.↗
Dynamic crystallography reveals spontaneous anisotropy in cubic GeTe
Cubic energy materials such as thermoelectrics or hybrid perovskite materials are often understood to be highly disordered. In GeTe and related IV–VI compounds, this is thought to provide the low thermal conductivities needed for thermoelectric applications. Since conventional crystallography cannot distinguish between static disorder and atomic motions, we develop the energy-resolved variable-shutter pair distribution function technique. This collects structural snapshots with varying exposure times, on timescales relevant for atomic motions. In disagreement with previous interpretations, we find the time-averaged structure of GeTe to be crystalline at all temperatures, but with anisotropic anharmonic dynamics at higher temperatures that resemble static disorder at fast shutter speeds, with correlated ferroelectric fluctuations along the <100> c direction. We show that this anisotropy naturally emerges from a Ginzburg–Landau model that couples polarization fluctuations through long-range elastic interactions. By accessing time-dependent atomic correlations in energy materials, we resolve the long-standing disagreement between local and average structure probes and show that spontaneous anisotropy is ubiquitous in cubic IV–VI materials.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials
Here we employed Density Functional Theory (DFT) to compute oxidation potentials of 1,400 homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The generated data were used to construct an active learning model based on Bayesian optimization (BO) that targets candidates with desired oxidation potentials utilizing only a minimal number of DFT calculations. The active learning model demonstrated not only significant efficiency improvement over the random selection approach but also robust capability in identifying desired candidates in an untested set of 112,000 homobenzylic ether molecules. Our findings highlight the efficacy of quantum chemistry-informed active learning to accelerate the discovery of materials with desired properties from a vast chemical space.
25 ENERGY STORAGE↗
ELECTRIFICATION OF A HEAVY-DUTY OFF-ROAD MATERIAL HANDLER: ENERGY SAVINGS AND EMISSION REDUCTIONS
Federal regulations are driving the adoption of electrification technologies to reduce carbon dioxide equivalent (CO2e) emissions, a metric that quantifies the global warming potential of various greenhouse gases in terms of carbon dioxide (CO2). Although no specific CO2 regulations exist for heavy-duty off-road machines, future reductions are likely, given stricter emissions standards for on-road vehicles. The heavy-duty off-road sector offers significant fuel-saving potential, as its focus has traditionally been on reliability and performance rather than fuel efficiency. This dissertation examines fuel and CO2e savings opportunities on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i, from stock configuration to simple modifications to a complete teardown and reconfiguration of the machine with a plug-in series hybrid architecture using electrified hydraulics. The study begins by modeling the baseline machine’s fuel and energy consumption, calibrating with experimental data from custom operating cycles. An energy analysis identifies key areas for fuel savings. Two simple powertrain modifications result in a combined 16.2% fuel savings. Next, a Pugh-style analysis narrows a list of electrified architectures, leading to high-fidelity models that evaluate total lifetime CO2e and costs. Higher electrification levels reduce CO2e emissions but increase costs, and electricity grid emissions significantly impact CO2e for plug-in architectures. A plug-in series hybrid is chosen for the project. In its base control form, 49% fuel and 29% CO2e savings are expected from the plug-in series hybrid compared to the baseline machine. Further savings are pursued through regenerative braking (6.3%) and load-following hydraulic control (17.8%), totaling 24.1% fuel savings, and leading to a total of 61% fuel and 41% CO2e savings compared to the baseline. Battery chemistries and charging strategies are also analyzed for cost and CO2e impacts, finding LFP batteries as superior due to longevity, and overnight level 2 charging usually at a lower cost but resulting in higher emissions than opportunity DC fast-charging (DCFC). DCFC emissions are highly dependent on grid emissions, and DCFC cost is highly dependent on grid demand charges. Finally, artificial intelligence is applied to operating cycle recognition. Neural network accuracy ranges from 81% to 99%, with applications to worksite efficiency and safety improvements.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.
Ferreira da Silva, Rafael [Oak Ridge National Labo↗
VISIONARY: Virtual Intelligence System for Optimizing Novel Analytical Research Yields
VISIONARY is an AI system that accelerates energy materials discovery by automatically generating hypotheses about structure-property relationships. It analyzes patterns in materials data, identifies promising correlations, and proposes testable scientific hypotheses without human intervention. By streamlining this reasoning process, VISIONARY helps researchers efficiently identify candidate materials with desired properties, significantly speeding up the materials development pipeline for energy applications. During the project, we developed a standalone application. The application uses a combination of papers provided by the user and data collected from FutureHouse’s dataset to build an understanding of the background that the user wants to explore for the hypothesis.
36 MATERIALS SCIENCE↗
Immersive Analytics in Critical Spatial Domains: From Materials to Energy Systems
Immersive analytics (IA) leverages virtual reality, augmented reality, and mixed reality to transform how users interact with complex datasets across domains such as science, industry, and education. These immersive technologies offer spatial and multimodal environments that foster intuitive exploration, but they also introduce challenges related to cognitive load, interface design, and system performance. Here, this article presents a comprehensive review of visualization techniques, interaction models, and multimodal inputs utilized in IA. Drawing on case studies in scientific visualization, industrial training, and educational communication, we examine both the potential and limitations of current systems. Finally, we propose future research directions, focusing on real‐time collaboration, adaptive user interfaces, and scalable data exploration strategies to advance the field.
99 - GENERAL AND MISCELLANEOUS↗
A Physical Organic Chemistry Approach to Developing Cyclopropenium-Based Energy Storage Materials for Redox Flow Batteries
Explore the source record for details and available documents.
Walser-Kuntz, Ryan↗
Hybrid Polymer Salogels for Reversible Entrapment of Salt-Hydrate-Based Thermal Energy Storage Materials
Not Available
Rajagopalan, Kartik Kumar↗
PV_ICE: PV in the Circular Economy, Dynamic Energy and Materials Tool (PV ICE)
This open-source tool implements Circular Economy metrics for photovoltaic (PV) materials. It can be used to quantify and assign a value framework to efforts on re-design, reduction, replacement, reuse, recycling, and lifetime and reliability increases in the PV value chain. The PV_ICE is leveraging published data from different sources on PV manufacturing and predicted technological changes. Input data is being compiled here This tool will help implement circularity metrics, quantify and assign a value framework to efforts on re-design, reduction, replacement, reusage, recycling, and lifetime and reliability increases on PV.
Ovaitt, nee Ayala Pelaez, Silvana↗
Importance of suberin biopolymer in plant function, contributions to soil organic carbon and in the production of bio-derived energy and materials
Abstract Suberin is a hydrophobic biopolymer of significance in the production of biomass-derived materials and in biogeochemical cycling in terrestrial ecosystems. Here, we describe suberin structure and biosynthesis, and its importance in biological (i.e., plant bark and roots), ecological (soil organic carbon) and economic (biomass conversion to bioproducts) contexts. Furthermore, we highlight the genomics and analytical approaches currently available and explore opportunities for future technologies to study suberin in quantitative and/or high-throughput platforms in bioenergy crops. A greater understanding of suberin structure and production in lignocellulosic biomass can be leveraged to improve representation in life cycle analysis and techno-economic analysis models and enable performance improvements in plant biosystems as well as informed crop system management to achieve economic and environmental co-benefits.
09 BIOMASS FUELS↗