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DOE OSTI2024

Low-temperature etching of silicon oxide and silicon nitride with hydrogen fluoride

Etching of high aspect ratio features into alternating SiO 2 and SiN layers is an enabling technology for the manufacturing of 3D NAND flash memories. In this paper, we study a low-temperature or cryo plasma etch process, which utilizes HF gas together with other gas additives. Compared with a low-temperature process that uses separate fluorine and hydrogen gases, the etching rate of the SiO 2 /SiN stack doubles. Both materials etch faster with this so-called second generation cryo etch process. Pure HF plasma enhances the SiN etching rate, while SiO 2 requires an additional fluorine source such as PF 3 to etch meaningfully. Further, the insertion of H 2 O plasma steps into the second generation cryo etch process boosts the SiN etching rate by a factor of 2.4, while SiO 2 etches only 1.3 times faster. We observe a rate enhancing effect of H 2 O coadsorption in thermal etching experiments of SiN with HF. Ammonium fluorosilicate (AFS) plays a salient role in etching of SiN with HF with and without plasma. AFS appears weakened in the presence of H 2 O. Density functional theory calculations confirm the reduction of the bonding energy when NH 4 F in AFS is replaced by H 2 O.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Innovative Advanced Hydrogen Mobile Fueler (Final Technical Report)

The US Department of Energy (DOE) funded a project to design, develop, deploy and analyze the economic viability of an innovative Advanced Hydrogen Mobile Fueler (AHMF). As part of the design activity the project team defined specifications based upon vehicle requirements and compliance with specific fueling performance criteria. The AHMF was originally designed to fuel 10-20 fuel cell vehicles (FCV) per day, consistent with the requirements of the H70 fueling category. The AHMF is able to operate without remote power connections, is modular for easy transport and deployment, and can provide expanded daily capacity and multi-day operations using delivered gaseous hydrogen. Toward the end of the project, due to request from the hydrogen industry and some necessary modifications of the AHMF, the system is now modified to fuel heavy duty vehicles. The project was conducted over two primary phases, each including several key tasks, subtasks, and project milestones. Phase 1 involved the design, development, and construction of the AHMF, moving from a conceptual design through to completion of assembly and testing. In addition to reviewing several different approaches, the team chose to take a conventional station design and modify it for mobile fueling. There were several innovative components included in the design. The high pressure storage was the first to achieve a US Department of Energy (DOE) Special Permit (SP 20391) to transport high pressure hydrogen (95 MPa) in a composite cylinder. The second novel system was a liquid nitrogen (LIN) cooling system with a compact heat exchanger. Without this change, the cooling system would not be able to fit into the AHMF. Phase 2 demonstrated the AHMF by fueling fuel cell buses at a fleet in Pomona, CA. The site was chosen because a temporary fueling solution was required while a fixed station was being installed. The existing permits for the fixed station and non-public access also was a determining factor. Over two months, the buses were fueled 320 times with over 5000 kg of hydrogen from tube trailers. Existing shore power was used, and the average electrical efficiency was 0.13 kWh/kg. The average liquid nitrogen (LIN) consumption was 90.68 scf/kg. The fueling and consumption data was provided to the National Renewable Energy Laboratory (NREL) for analysis. An economic analysis was performed and will be provided in a separate report. The project was successful, but not without challenges and lessons learned. The DOT special permit led the way for the use of high pressure, composite cylinders and is being by multiple other systems and applications. The pandemic along with time for the DOT special permit approval delayed the project for years. The team also believes that hydrogen mobile fueling has a use for the industry, especially during this upcoming phase of expansion. However, it does have its limitations due to high cost to build and operate, and the same permitting challenges as a fixed station. A fully capable system at the speeds and pressures of the AHMF may not be necessary for most applications and would help reduce the cost and increase storage capacity. The on-board generator can be easily replaced with shore power or the wide range of power generation solutions in the marketplace. One of the major results of the project was the development of new code language in National Fire Protection Agency (NFPA) 2 and the International Fire Code (IFC) for on-demand mobile fueling. This will provide guidance for Authorities Having Jurisdiction (AHJ) and user on how to permit temporary fueling sites across the nation.

08 HYDROGEN↗

DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗

DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Cell-free bioelectrocatalytic platform for carbon dioxide reduction (Final Technical Report)

The University of Minnesota (UMN) EcoSynBio Team aimed to develop a cell-free, enzyme-based platform for the electro- biocatalytic conversion of CO2 into formate as a platform chemical for further upgrading. This type of bio electrocatalytic process delivers a clean product stream without the need for extensive separation from the electrolyte as in electrochemical synthesis and microbial processes. The reduction reaction is catalyzed by metal-dependent formate dehydrogenases (mFDHs) that are capable of efficient electrocatalytic CO2 reduction without the need of costly co-factors. The development of an efficient, scalable electrobiocatalytic process with high total turnover numbers and viable space time yields, however, was not without its challenges. The UM team has developed a protein-based scaffolding system that facilitates enzyme stabilization and attachment to electrodes along with electron transfer. Yet, although FDHs are highly promising enzymes for cell-free, electrobiochemical CO2 reduction, they are also greatly understudied and especially for applications in electrocatalysis. The UM team used the best described mFDH from Clostridium as its benchmark system and spent significant time and effort in attempting to replicate published data and finally, redesigned a recombinant production system for proper metal co-factor incorporation. The UM team has also identified a small set of new enzyme homologs from extreme microorganisms with superior stabilities that have yielded initial structural data for further engineering. In addition, a new bioelectrocatalytic reactor system has been developed that can be 3D printed and used for enzyme attachment to electrodes. In summary the project has generated critical basic information for the further development of this class of enzymes for the electricity driven reduction of CO2 into formate as platform chemical for upgrading into various other chemicals, including fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Temporal covariation of island arc Sr isotopes and seawater chemistry over the past 2 billion years

The chemical compositions of island arc basalts (IAB) reflect contributions from the mantle as well as fluids and melts from the subducting slab. Addition of radiogenic seawater Sr to oceanic crust through hydrothermal alteration and subsequent subduction is often invoked to explain elevated 87 Sr/ 86 Sr signatures in modern IAB. However, changes in the 87 Sr/ 86 Sr of island arc magmatic rocks through time has not been investigated, limiting our understanding of the factors influencing the Sr budgets of arcs throughout Earth’s history. To address this, we compiled 87 Sr/ 86 Sr values from island arc magmatic rocks ranging in age from modern to Paleoproterozoic, only including data from island arc localities that best preserve initial magmatic 87 Sr/ 86 Sr. Median initial 87 Sr/ 86 Sr values are consistently elevated compared to depleted mantle 87 Sr/ 86 Sr over this period, indicating persistent enrichment in radiogenic Sr in island arcs. Moreover, the elevation in island arc 87 Sr/ 86 Sr relative to the depleted mantle is variable. A notable rise in island arc 87 Sr/ 86 Sr during the late Neoproterozoic coincides with a steep increase in seawater 87 Sr/ 86 Sr and Sr concentration. To investigate this potential connectivity, we modeled the 87 Sr/ 86 Sr of island arc magmas between 0 and 830 Ma with inputs of depleted mantle 87 Sr/ 86 Sr, seawater 87 Sr/ 86 Sr, and seawater Sr concentration. The model reproduces the overall trajectory of the compiled data. We interpret the observed temporal variation in island arc 87 Sr/ 86 Sr values and its close association with fluctuations in seawater chemistry as evidence that changes in marine geochemistry have strongly influenced the Sr isotopic record of island arc magmas over time.

Science & Technology - Other Topics↗

DOE OSTI2024

Thermal Adaptation of Enzyme‐Mediated Processes Reduces Simulated Soil CO2 Fluxes Upon Soil Warming

Abstract Understanding factors influencing carbon effluxes from soils to the atmosphere is important in a world experiencing climatic change. Two important uncertainties related to soil organic carbon (SOC) stock responses to a changing climate are (a) whether soil microbial communities acclimate or adapt to changes in soil temperature and (b) how to represent this process in SOC models. To further explore these issues, we included thermal adaptation of enzyme‐mediated processes in a mechanistic SOC model (ReSOM) using the macromolecular rate theory. Thermal adaptation is defined here to encompass all potential responses of soil microbes and microbial communities following a change in temperature. To assess the effects of thermal adaptation of enzyme‐mediated processes on simulated SOC losses, ReSOM was applied to data collected from a 13‐year soil warming experiment. Results show that a model omitting thermal adaptation of enzyme‐mediated processes substantially overestimates observed CO 2 effluxes during the initial years of soil warming. The bias against observed CO 2 effluxes was lower for models including thermal adaptation of enzyme‐mediated processes. In addition, for a simulated linear 3°C soil warming over 100 years, models including thermal adaptation of enzyme‐mediated processes simulated SOC losses of a factor of three smaller than models omitting this process. As thermal adaptation of microbial community characteristics is generally not included in models simulating feedback between the soil, biosphere and atmosphere, we encourage future studies to assess the potential impact that microbial adaptation has on soil carbon – climate feedback representations in models. Plain Language Summary A major uncertainty in projecting how much soil organic carbon (SOC) will be converted to CO 2 as a consequence of climate change is related to how soil microbes may adapt to increasing soil temperatures. While this “microbial thermal adaptation” has been shown to occur in short‐term lab incubation experiments, its effect on SOC cycling on a decadal timescale is not clear. To address this knowledge gap, a mechanistic SOC model was used to simulate data collected from a 13‐year soil warming experiment, to assess how microbial thermal adaptation affects predicted SOC losses upon soil warming. The model results show that incorporating microbial thermal adaptation into the model led to reduced CO 2 effluxes from the soil to the atmosphere compared to the common approach of omitting this mechanism. Our results imply that projected SOC losses for the decades to come may be reduced when this mechanism is incorporated in land models. We therefore advocate for more research on the mechanisms controlling microbial thermal adaptation, and how to implement this mechanism in SOC models. Key Points A crucial aspect of soil organic carbon (SOC) models is the representation of soil microbes Predicted soil CO 2 fluxes upon soil warming are reduced when accounting for microbial thermal adaptation On a centennial time scale, this thermal adaptation results in up to a factor of three lower predicted SOC loss

Van de Broek, Marijn↗

DOE OSTI2025

Three rate-determining protein roles in photosynthetic O 2 -evolution addressed by time-resolved experiments on genetically modified photosystems

Light-driven water splitting by plants, algae and cyanobacteria is pivotal for global bioenergetics and biomass formation. A manganese cluster bound to the photosystem II proteins catalyzes the complex reaction at high rate, but the rate-determining factors are insufficiently understood. Here we trace the oxygen-evolution transition by time-resolved polarography and infrared spectroscopy for cyanobacterial photosystems genetically modified at two strategic sites, complemented by computational chemistry. Our results highlight three rate-determining roles of the protein environment of the metal cluster: acceleration of proton-coupled electron transfer, acceleration of substrate-water insertion after O 2 -formation, and balancing of rate-determining enthalpic and entropic contributions. Whereas in general the substrate-water insertion step may be unresolvable in time-resolved experiments, here it likely becomes traceable because of deceleration by genetic modification. Our results may stimulate new time-resolved experiments on substrate-water insertion in photosynthesis, clarification of enthalpy-entropy compensation in enzyme catalysis, and knowledge-guided development of inorganic catalyst materials.

Bioenergetics↗

DOE OSTI2025

Critical Role of the Steric Factor in the Viscoelasticity of Vitrimers

Dynamic covalent networks (DCNs) are a promising solution to mitigate plastic-waste-related issues through improved recyclability enabled by dynamic bonds. However, our understanding of the mechanisms controlling their viscoelasticity, especially in vitrimers where bond exchange relies on associative reactions, remains limited. Here, in this study, we investigate the dynamics in model DCNs with boric ester functionalities, and the analysis of the temperature dependence of their terminal relaxation times revealed a puzzling result: extremely large Arrhenius prefactors associated with their dynamic bond rearrangement times. We ascribe this observation to the often-overlooked chemical steric factor that slows down chemical reactions, therefore decreasing the vitrimers’ bond exchange rate by many orders. The estimated steric factor of the bond exchange in our model DCNs is comparable to those observed in boronic ester exchange reactions between small molecules. Additional analysis of literature data revealed an overall low steric factor also for imine bond exchange, thus highlighting the role of this parameter in tremendously slowing down bond exchange in DCNs despite low activation energy barriers. We propose a general approach for designing vitrimers with desired viscoelastic and creep properties considering the critical role of the steric factor in bond rearrangement mechanisms, in addition to the traditionally considered activation energy and matrix properties.

Carden, Gregory P. [Univ. of Tennessee, Knoxville,↗

DOE OSTI2025

Editorial overview: Unlocking the secrets of nongenetic plasticity, one cell at a time

Cellular noise, the non-genetic variability observed among isogenic cells, arises from factors such as growth conditions, aging, and stochastic gene expression, influencing cell stress-response, metabolism, morphology, and size. Here, such plasticity, while critical for adaptation, often goes unnoticed with traditional population-averaging biotechnologies that inevitably mask cell-specific variations and prompting the question, "What else might we be missing". However, recent breakthroughs in optical imaging, microfluidics, and omics, are beginning to uncover the complexity of cellular plasticity. This special is-sue highlights some of these breakthroughs, with key contributions including innovations in multimodal chemical imaging, label-free microscopy, spatial and temporal omics, and droplet-based microfluidics. Collectively, these cutting-edge tools provide unprecedent-ed insights into non-genetic cell-to-cell variability, enhancing our understanding of cellu-lar plasticity and its implications for health, energy, and ecology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Fast-airflow tumble clothes dryer with small thermoelectric heat pump: Experimental evaluation

Residential clothes drying accounts for about 5% of the total residential-sector energy consumption in the United States. Most dryers use electric resistance heaters to dry clothes and have low efficiencies. Higher-efficiency dryers that use vapor compression heat pumps are expensive and complex and have not gained a large market share in the United States. A novel tumble clothes dryer using a small thermoelectric heat pump with faster airflow than typical dryers is presented in this work. The benchtop performance of the thermoelectric heat pump and high-speed blower are presented, and the development of the prototype dryer is described. The dryer was tested for efficiency and dry time for a range of airflow rates and applied currents to the thermoelectric heat pump. The combined efficiency factor was 5.09–6.29 lb BDW /kWh (specific moisture extraction rate of 1.23–1.53 kg w /kWh) with 100–138 min dry times for these tests. The measured efficiency was 36 %–68 % greater than the minimum efficiency standard in the United States, and compared with vapor compression heat pump–based clothes dryers, the prototype dryer had less expensive, less complex components and did not use refrigerants. Finally, the performance of this small thermoelectric heat pump clothes dryer is also compared with previous iterations of the thermoelectric tumble clothes dryer described in the literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Chemical Insights into the Molecular Composition of Organic Aerosols in the Urban Region of Houston, Texas

Molecular functional groups, such as organosulfates (CHOS) and organonitrates (CHNO) are important tracers for field observations of secondary organic aerosols (SOA). While CHOS and CHNO are prevalent in the atmosphere, there is a lack of knowledge regarding daily and day- and night-time variations in these species in the urban atmosphere. Meteorological factors such as wind speed/direction, relative humidity (RH), and temperature can influence the formation of CHOS/CHNO. To investigate these trends, we utilized multimodal chemical imaging and advanced high resolution mass spectrometry techniques to acquire particle speciation and molecular formulas (MFs) associated with day and night sampling periods. Back trajectory analyses revealed the oceanic influence of southern wind airmasses in later June sampling periods with organic fractions <10%. Conversely, northern winds in early June sampling periods contributed to the episodic emergence of extremely low volatile organics (ELVOCs) and organic factions up to 41%. The observed unique MFs to June 3 (223 MFs) and to June 4 (144 MFs) were largely found to be of biogenic rather than anthropogenic origin. Finally, our findings reveal episodic prevalence and temporal distribution of SOA constituents across the urban region of Houston, Texas.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Superconducting Qubits above 20 GHz Operating over 200 mK

Current state-of-the-art superconducting microwave qubits are cooled to extremely low temperatures to avoid sources of decoherence. Higher qubit operating temperatures would significantly increase the cooling power available, which is desirable for scaling up the number of qubits in quantum computing architectures and integrating qubits in experiments requiring increased heat dissipation. To operate superconducting qubits at higher temperatures, it is necessary to address both quasiparticle decoherence (which becomes significant for aluminum junctions above 160 mK) and dephasing from thermal microwave photons (which are problematic above 50 mK). Using low-loss niobium-trilayer junctions, which have reduced sensitivity to quasiparticles due to the higher superconducting transition temperature of niobium, we fabricate transmons with higher frequencies than previously studied, up to 24 GHz. We measure decoherence and dephasing times of about 1 μs, corresponding to average qubit quality factors of approximately 10 5 , and find that decoherence is unaffected by quasiparticles up to 1 K. Without relaxation from quasiparticles, we are able to explore dephasing from purely thermal sources, finding that our qubits can operate up to approximately 250 mK while maintaining similar performance. The thermal resilience of these qubits creates new options for scaling up quantum processors, enables hybrid quantum experiments with high heat-dissipation budgets, and introduces a material platform for even-higher-frequency qubits.

Josephson junctions↗

DOE OSTI2024

NSTXU Diagnostic Disruption Dynamic Loading Represented by Response Spectra

This article presents the results of transient dynamic simulations of loads due to disruption eddy currents on the NSTXU vacuum vessel. Dynamic loading at diagnostic mounting locations is expressed as response spectra derived from the time history results of the dynamic structural simulations of a variety of disruption scenarios. The disruption simulations draw on a history of the project assessments of worst case disruptions for specific components. Major efforts to assess disruption loading have included the vacuum vessel which is the major structural support for the machine, as well as the passive plates (PPs), high harmonic fast wave (HHFW) antenna, and centerstack casing. Each one of these efforts included transient electromagnetic simulations producing time-dependent eddy current Lorentz loads (and in some cases halo loads) which then were applied to time-dependent structural dynamic analyses intended to obtain the proper dynamic amplification factors. In some instances, the EM model and structural model were identical allowing direct transfer of EM forces to the structural model. In other cases, the EM and structural model were not identical and the vector potential (VP) transfer method was used. The results files from these analyses were available (or re-run) to post process in ANSYS Classic time history postprocessor. In conclusion, the ANSYS command is used to create response spectra from time history data at desired points on the vessel.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Peering into cloud physics using ultra-fine resolution radar and lidar systems

Cloud microphysical processes, such as droplet activation, condensational growth, and collisional growth, play a central role in the evolution of clouds and precipitation. Accurate representations of these processes in numerical models are challenging partially due to incomplete understanding of them at the process-level arising from limited systematic observations. Most surface-based active remote sensors, including today’s operational cloud radars and lidars, have a resolution on the order of tens of meters. This resolution is insufficient to resolve cloud microphysical processes that manifest at finer (meter and sub-meter) scales. A new set of ultra-high-resolution ground-based radar and lidar systems have been developed to address this observational gap. The newly developed 94-GHz cloud radar has a range resolution down to 2.8 m, or a factor of 10 finer than typical radars, using a large bandwidth and quadratic phase coding techniques. The lidar has a range resolution down to 10 cm, or a factor of 100 finer than typical lidars, using a time-gated time-correlated single photon counting technique. Such high-resolution observations were previously only achievable through in situ aircraft measurements. Even then, aircraft measurements do not permit continuous long-term cloud observation as is possible with ground-based remote sensing instruments. In this study, the first-light cloud observations from the new radar and lidar systems are shown to reveal detailed cloud structures that conventional sensors could only perceive in a bulk sense, thus providing new avenues to investigate cloud microphysical processes and their impact on weather and climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Enabling Next Generation Reaction Injection Molding (RIM) for Lightweight Structures

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this project, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. This optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption. This research has been performed in collaborations with McClarin Composites. The research outcome has been submitted to the Journal of Manufacturing Processes.

36 MATERIALS SCIENCE↗

DOE OSTI2026

An Assessment of Technical Hydropower Potential at Non-Powered Dams in the United States

Historically, dams have been constructed for a variety of purposes, such as providing a more secure and reliable water supply, mitigating impacts from variations in river flow, allowing continuous navigability, and harnessing mechanical power. A relatively small portion of dams have been designed to store or regulate flows for the purpose of generating electricity (roughly 3% of nationally inventoried dams in the US and 17% of the dams included in the World Register of Dams). The remaining population of existing non-powered dams (NPDs) presents both an opportunity to generate renewable energy and a need to modernize aging infrastructure. This report describes an assessment of more than 2,600 NPDs in the US that have a collective potential of nearly 4 GW in new power capacity. Previous national-scale assessments were aimed at evaluating the theoretical maximum power potential at existing dams in the United States. These estimates were based on the best available information at the time for water availability, hydraulic head, and representative regional capacity factors. This study revisits a subset of 3,299 dams identified in the most recent theoretical resource assessment and uses more detailed and updated hydrologic data to produce estimates of technical potential. These improvements in data enable estimates that more realistically reflect what is physically possible given simple assumptions about the existing structure and constraints on flow and head (Figure 1).

13 HYDRO ENERGY↗