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At least 397 records · Page 22

Ammonia-Based Energy Storage Technology

The University of North Dakota Energy & Environmental Research Center (EERC), in partnership with the U.S. Department of Energy National Energy Technology Laboratory (NETL), Basin Electric Power Cooperative, Minnkota Power Cooperative, Otter Tail Power Company, and the North Dakota Industrial Commission (energy technology research and development sponsor), undertook a project to model, validate, and advance (from Technology Readiness Level [TRL] 2 to TRL 3) the ammonia-based energy storage technology (NH 3 -BEST) concept. The concept is designed to enable electricity-generating unit (EGU) accommodation of power demand fluctuations while steadily operating within an optimal-performance baseline power output range, ensuring maximum EGU operational efficiency and minimum degradation of materials, equipment, and performance because of load-cycling-driven stresses.

25 ENERGY STORAGE↗

STATUS OF THE SECOND INTERACTION REGION DESIGN FOR ELECTRON-ION COLLIDER

Provisions are being made in the Electron Ion Collider (EIC) design for future installation of a second Interaction Region (IR), in addition to the day-one primary IR. The envisioned location for the second IR is the existing experi- mental hall at RHIC IP8. It is designed to work with the same beam energy combinations as the first IR, covering a full range of the center-of-mass energy of ?20 GeV to ?140 GeV. The goal of the second IR is to complement the first IR, and to improve the detection of scattered particles with magnetic rigidities similar to those of the ion beam. To achieve this, the second IR hadron beamline features a secondary focus in the forward ion direction. The design of the second IR is still evolving. This paper reports the current status of its pa- rameters, magnet layout, and beam dynamics and discusses the ongoing improvements being made to ensure its optimal performance.

Gamage, B.↗

Evaluation of the Stability of Edge-Passivated CZTS Radiation Detectors

CdZnTeSe (CZTS) is a next gen replacement room temperature radiation detector that solves common issues with modern CZT detectors. • Surface states can trap radiation induced carriers and can act as leakage current pathways requiring detectors to passivated for optimal performance. • In this work, we seek to identify the best passivation technique for CZTS detectors.

KLEPPINGER, JOSHUA↗

An Analytical Tool to Evaluate Defect Thermodynamics of (La,Ba)Fe1-xMxO3-δ Perovskites for Solid-Oxide Cell Applications

A modeling tool of the defect thermodynamics of (La,Ba)Fe1-xMxO3-δ perovskites which includes energetic information about oxygen vacancy formation, hydration, hydride formation, and charge disproportionation reactions has been developed1. This tool incorporates defect energies and entropies expressed as sixth order polynomial functions to allow refinements of the defect reaction equilibrium constants in the thermodynamic analysis. Calculation of (La,Ba)Fe1-xMxO3-δ Brouwer diagrams as a function of pO2/pH2O and pH2/H2O in a range of temperatures of interest is facilitated by this modeling tool. The results obtained can provide direct guidance how the electronic and ionic defect concentrations of the triple conducting perovskite materials can be used to optimize performance of solid oxide cells for energy applications. The impact of magnetic and electronic structures of the perovskites on the defect reaction energies and entropies as obtained from density function theory modeling and the role played by hydride defect species will also be discussed.

Lee, Yueh-Lin↗

Evaluate Synergies of Using Hydrothermal Liquefaction and Anerobic Digestion Treatment Technologies for Wastewater Resource Recovery Facilities (CRADA 516 Final Report)

The research focuses on utilizing a new anaerobic digestion (AD) configuration to treat the aqueous by-product generated by hydrothermal liquefaction (HTL) of sewage sludge. This report found that for Anaerobic Digestion for HTL By-product, Anaerobic biofilms can degrade some HTL wastewater contaminants, but co-digestion is essential to address nutrient deficiencies and optimize performance. Without AD, toxicity of HTL aqueous streams may limit broader adoption in wastewater treatment plants (WWTPs). Great Lakes Water Authority (GLWA) used an innovative reactor design, involving a dynamic membrane anaerobic bioreactor to promote biofilm growth, improving contaminant degradation. The tree-like structure inside the reactor supports biofilm development with recirculation enhancing microbial activity. Overall, a 70% chemical oxygen demand (COD) removal was achieved, although nutrient supplementation is required for stability. The reactor achieved a diverse microbial community, including methanogens and bacteria capable of degrading phenols and aromatics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A Block-Structured Adaptive Mesh Framework to Solve Radiation Transfer Equation in Irregular Embedded Geometries

Radiation transport arises in various scientific, industrial, and medical fields, and understanding its effect in applications is needed to make accurate predictions, safety assessments and performance optimizations. Solving the Radiation Transport Equation (RTE) is challenging due to its integro-differential nature, which involves both differential and integral terms. The differential term describes the change in radiation intensity due to absorption and emission, while the integral term accounts for scattering. The accurate modeling of radiation is further complicated in many applications due to the complex, irregular geometries. Various methods exist for solving the RTE, including the zonal, Monte Carlo, spherical harmonics, discrete ordinates, and finite volume methods. Traditional mesh-based approaches, which rely on structured or unstructured meshes, struggle with irregular geometries due to: a) the difficulty of conforming structured grids to irregular domains, b) challenges in enforcing boundary conditions correctly, and c) the additional computational cost of unstructured mesh methods. This work presents a second-order accurate method for solving the RTE in irregular geometries. The radiation intensity is discretized using the finite-volume method in both spatial and angular directions on regular Cartesian grid blocks. Leveraging the block-structured adaptive mesh refinement (AMR) framework provided by AMReX, our method refines the grid locally to reduce spatial discretization error, ensuring a converged numerical solution while minimizing computational costs elsewhere. A two-stage deferred correction approach is employed: First, a first-order discretization on grid blocks is solved using an algebraic multigrid method in HYPRE. Second, a correction term is applied explicitly to achieve second-order accuracy. The correction term is calculated by approximating the radiation flux on cell faces using a Total Variation Diminishing (TVD) scheme. This approach ensures quick convergence of the multigrid method while preserving higher-order accuracy of the numerical solution. Irregular geometries are resolved as embedded boundaries (EB), resulting in both cut cells and regular cells. In cut cells, we modify the fluxes using face fractions and incorporate additional contributions from EB boundary conditions. To ensure higher-order convergence near the EB interface, the correction term is modified by interpolating the radiation intensity to fictitious ghost points. The implementation takes advantage of modern supercomputers by leveraging AMReX’sMPI/X parallelization strategy where X can be MPI or a GPU accelerator including CUDA, HIP and DPC++. We validate our solver using classical test cases, both with and without EB, demonstrating accuracy and efficiency. Additionally, we analyze the impact of adaptive mesh refinement on solution accuracy and computational cost, highlighting the advantages of our approach for high-resolution radiation transport simulations.

computational fluid dynamics (CFD)↗

Report on the "Symposium on Advancements in Simulating Neutron Scattering Instruments and Experiments"

An international group of researchers gathered for the “Symposium on Advancements in Simulating Neutron Scattering Instruments and Experiments” at Oak Ridge National Laboratory (ORNL) on October 29- 30, 2024. The symposium highlighted state-of-the-art advancements in neutron scattering instrument simulations and the development of digital twins for scattering instruments. This document summarizes the recurring themes from the 19 talks and discussions. A key focus was on the role of neutronics simulations in the design, analysis, and performance optimization of neutron instruments. Presentations featured the most widely used software packages, such as McStas, McVine, Vitess, and MCNP. Another major theme addressed the analysis and interpretation of experimental neutron data, where materials models and simulations play a critical role. Talks on relevant software tools, including NCrystal, OCLimax, and Paradyse, were delivered by their respective developers. During the symposium, both talks and breakout sessions explored strategies to enhance the interplay between neutronics simulations and materials models. A key question emerged: How can we develop a RealTime Explainable Digital Twin of a neutron scattering experiment? Discussions include the idea that the Digital Twin concept has implications for instrument control and testing. Through this symposium, we gained valuable insights into the challenges and requirements for developing a Real-Time Explainable Digital Twin of a neutron scattering experiment. The discussions and presentations paved the way for future innovations in this rapidly advancing field.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Automation of debris removal and resurfacing of insulators in terawatt pulsed power systems using state of the art laser technology

The increasing complexity and scale of pulsed power systems necessitate effective maintenance strategies to ensure optimal performance and safety. This report investigates the automation of debris removal and resurfacing of insulators in terawatt pulsed power systems using advanced laser cleaning technologies. The motivation for this research stems from the limitations of current manual cleaning methods, which are labor-intensive and pose health risks due to hazardous materials. The study addresses the problem of surface flashover, a significant issue affecting insulator performance, by evaluating two innovative cleaning techniques: Pulsed Laser Cleaning (PLC) and Flash Lamp Annealing (FLA). Experimental results demonstrate that PLC effectively restores the dielectric strength of Rexolite insulators, while FLA shows limited success. The findings highlight the potential for automated cleaning solutions to enhance safety and efficiency in insulator maintenance, paving the way for future advancements in pulsed power technology.

42 ENGINEERING↗

Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation

This Final Scientific and Technical Report summarizes work performed under the Phase IIA SBIR project “Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation” (DE-SC0020906) from August 2023 through August 2025. The objective of the Phase IIA effort was to mature and harden capabilities developed during Phase II, with the goal of enabling practical interoperability between Coreform’s isogeometric analysis (IGA) technologies and the Multiphysics Object-Oriented Simulation Environment (MOOSE), while improving robustness, performance, and scalability for complex, nuclear-relevant geometries. Over the course of Phase IIA, the project established and validated an extraction-based interoperability pathway between Coreform tools and MOOSE. A combined mesh and matrix format was defined collaboratively with MOOSE developers and integrated into the solver, enabling standard MOOSE workflows to operate on data exported from Coreform’s IGA and Flex Representation Method (FRM) pipelines. Early demonstrations validated architectural compatibility using linear solid mechanics problems, while later efforts focused on benchmark testing and external use. By the end of the project period, engineers at BWXT were able to independently set up and execute a simulation using the Coreform–MOOSE workflow and provide direct feedback that informed further refinement. In parallel, substantial effort was devoted to improving the robustness of trimmed U-spline construction for complex CAD geometries. A growing test suite of nuclear-relevant models was compiled through collaboration with multiple stakeholders and used to drive extensive bug fixing and reliability improvements. These efforts resulted in improved robustness and performance, including the addition of fallback capabilities that enhance reliability when the underlying commercial CAD kernel fails. Performance-oriented work progressed later in the project, with the development and demonstration of methods to decompose complex geometries into structured subregions and updated data representations to support more efficient solver processing. Additionally, extensive enhancements to threadsafe parallel data structures and trimming operations established a foundation for scalable processing of large assemblies. Collaboration with Sandia National Laboratories on the SGM geometric modeling kernel advanced to a functioning interface test case, positioning the workflow for future kernel integration. Overall, the Phase IIA effort successfully transitioned the project from architectural proof-of-concept to externally exercised, solver-integrated capability, while clarifying remaining technical challenges related to standardization, performance optimization, and kernel integration.

42 ENGINEERING↗

Bipolar Membranes Electrodialysis for Water Purification and Resource Recovery

Reverse osmosis concentrate (ROC) from inland brackish water desalination and wastewater recycling presents moderate salinity, posing significant challenges for treatment and disposal. Existing treatment technologies predominantly rely on energy-intensive thermal methods, large-area evaporation strategies, or deep-well disposal, which often result in the loss of water value. Electrified membrane brine treatment offers a promising alternative by utilizing renewable energy sources, enabling complete water recycling. This work proposes the development of next-generation bipolar membrane electrodialysis (BMED) systems capable of desalinating brine while simultaneously recovering valuable resources. However, several critical challenges remain in the areas of membrane engineering for BMED. These include: 1) decreased current efficiency during desalination due to the low conductivity of feed chambers, 2) fouling and scaling from multivalent ions and contaminants in the feed, and 3) the retention and carryover of ROC contaminants, including PFAS compounds. Our research focuses on the development of novel anion exchange membranes (AEMs) and cation exchange membranes (CEMs) based on synthesized multiblock copolymers. We investigate the effects of block length, ion exchange capacity (IEC), and polymer backbone structure on cation and anion transport (e.g., Na+, Cl-, OH-, H+) for both our synthesized membranes and commercial membranes. Fundamental properties of these membranes, such as water uptake, IEC, and ionic conductivity, are also evaluated to optimize performance for BMED applications.

08 HYDROGEN↗

Controlling Oxidation of Nb in Oxygen Abundant Environments

Modern particle accelerators depend on Superconducting Radio Frequency (SRF) cavities made from high-purity niobium (Nb) to achieve optimal performance, including high quality factors and strong accelerating gradients. However, when exposed to air, niobium naturally forms a complex oxide layer that can introduce surface imperfections and carbon contamination. This work examines an alternative oxidation strategy under oxygen-rich conditions to better regulate the oxide formation process. The ultimate objective is to improve surface uniformity and cleanliness, thereby reducing defect density and enhancing performance. We used Confocal Microscopy, Scanning Electron Microscopy (SEM), and X-Ray Photoelectron Spectroscopy (XPS) to analyze surface changes. We used standard metrics, like Arithmetic Average Roughness (Ra) and Root Mean Square (Rq). Three oxidation methods were applied: short, extended, and a repeated HF and H₂O₂ oxidation process. Preliminary results show promising reductions in carbon contamination and surface defects.

Romero, Juan [Fermilab]↗

High polarization InAlGaAs/AlGaAs photocathodes grown using MBE

Strained superlattices of GaAs/GaAsP grown using molecular beam epitaxy (MBE) have been used for more than 20 years to generate high polarization electron beams for nuclear physics. GaAs/GaAsP superlattices have several manufacturing challenges, including the thick graded layer required for the virtual GaAsP substrate, the significant difference in optimal growth temperatures for GaAs and GaAsP, and the scarcity of MBE systems using phosphorus. InAlGaAs/AlGaAs strained superlattice photocathode are an alternative structure that eliminates many of hurdles to growing GaAs/GaAsP in MBE systems. Measurements of quantum efficiency and polarization from InAlGaAs/AlGaAs will be presented. These include studies on a variety of growth parameters to optimize performance, including digital alloys, growth temperature variations, and variation in structure.

Stutzman, Marcy L↗

Measurement of Resistance, Porosity, and Water Contact Angle of Porous Transport Layers for Low-Temperature Electrolysis Technologies

The porous transport layer is an important component of low-temperature electrolysis devices, such as proton exchange membrane water electrolyzers or anion exchange membrane water electrolyzers. PTLs have significant influence on the cell performance as their bulk resistance can impact the ohmic resistance, their contact resistance can impact electrode performance, and their structure can impact the liquid flow to the cell, which could cause mass-transport losses. In order to improve cell performance, optimization of the PTL is critical. Standardized protocols should be utilized to adequately compare PTLs being developed from different institutions. This method will detail a standardized protocol for measuring the resistance of the PTL using a four-wire setup and will also detail a process for measuring the porosity and water contact angle of the PTL using capillary flow porometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading

The aging pipeline infrastructure around the world necessitates immediate rehabilitation. Internal replacement pipe (IRP) is a trenchless system offering a versatile and cost-effective solution across a variety of industries, including oil, natural gas, water, and wastewater. As a structural pipeline repair system, IRPs are subject to lateral deformation because of surface traffic loading. The present study evaluates the impact of adhesion between the host pipe and the IRP, with a focus on assessing the debonding effect on the behavior of the repair system under lateral deformation and bending. This was achieved using a comprehensive approach, including experimental, numerical, and analytical techniques. Varying levels of adhesive strength resulting from different methods of surface preparation were considered. The effectiveness of the IRP system on both discontinuous host pipes with various crack widths and continuous host pipes was also investigated. The results demonstrate that adhesive strength exerts a significant influence on the repair system, especially in the case of narrow circumferential cracks, while its impact on the continuous system is minimal. For optimal performance, it is essential to choose adhesives that possess sufficient shear strength while also accounting for the required debonding length. This approach ensures that minor discontinuities are effectively controlled, thereby enhancing the system′s fatigue life. The reliable determination of the maximum allowable shear strength for the adhesive or the debonding length can ensure that it does not negatively affect fatigue life. The findings presented in this study offer new insights into the development of trenchless repair techniques that can enhance system performance and extend service life.

Tien, Tri C. M.↗

Simulated Impact of Shortened Strings in Commercial and Utility-Scale Photovoltaic Arrays

The deliberate removal of photovoltaic modules from a string can occur for various reasons encompassing maintenance, measurements, theft, or failure, reducing that string length relative to others when replacement modules are not available and there are not any viable alternative makes and models that could be inserted. This phenomenon, delineated in our prior experimentally validated research, manifests two significant effects: (1) a shift in the ideal maximum power point and (2) the induction of potentially substantial reverse currents in the shortened strings at open-circuit voltage, VOC. However, the scalability and asymptotic limits of these observed behaviors concerning array size remained undetermined. In this study, we elucidate the operational dynamics of such arrays by manipulating two mismatch-contributing variables in simulated arrays of up to 900 strings: the number of removed modules per string (indicative of the level of mismatch, ranging up to 5) and the quantity of shortened strings (1 to 60). Simulation outcomes underscore that mismatch severity impacts array operation more than the proportion of shortened strings. This research delves into the practical ramifications of operating with shortened strings, including implications for low-irradiance operation and the manifestation of deleterious reverse currents (>35 A in specific cases), emphasizing the need for careful array configuration for optimal performance and safety in these implementations.

Energy & Fuels↗

Influence of Carboxymethyl Cellulose as a Thickening Agent for Glauber’s Salt-Based Low Temperature PCM

This work is focused on a novel, promising low temperature phase change material (PCM), based on the eutectic Glauber’s salt composition. To allow phase transition within the refrigeration range of temperatures of +5 °C to +12 °C, combined with a high repeatability of melting–freezing processes, and minimized subcooling, the application of three variants of sodium carboxymethyl cellulose (Na-CMC) with distinct molecular weights (700,000, 250,000, and 90,000) is considered. The primary objective is to optimize the stabilization of this eutectic PCM formulation, while maintaining the desired enthalpy level. Preparation methods are refined to ensure repeatability in mixing components, thereby optimizing performance and stability. Additionally, the influence of Na-CMC molecular weight on stabilization is examined through differential scanning calorimetry (DSC), T-history, and rheology tests. The PCM formulation of interest builds upon prior research in which borax, ammonium chloride, and potassium chloride were used as additives to sodium sulfate decahydrate (Glauber’s salt), prioritizing environmentally responsible materials. The results reveal that CMC with molecular weights of 250 kg/mol and 90 kg/mol effectively stabilize the PCM without phase separation issues, slowing crystallization kinetics. Conversely, CMC of 700 kg/mol proved ineffective due to the disruption of gel formation at its low gel point, hindering higher concentrations. Calculations of ionic concentration indicate higher Na ion content in PCM stabilized with 90 kg/mol CMC, suggesting increased ionic interactions and gel strength. A tradeoff is discovered between the faster crystallization in lower molecular weight CMC and the higher concentration required, which increases the amount of inert material that does not participate in the phase transition. After thermal cycling, the best formulation had a latent heat of 130 J/g with no supercooling, demonstrating excellent performance. This work advances PCM’s reliability as a thermal energy storage solution for diverse applications and highlights the complex relationship between Na-CMC molecular weight and PCM stabilization.

25 ENERGY STORAGE↗

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography–Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

59 BASIC BIOLOGICAL SCIENCES↗

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗