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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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Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity
Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.
High precision fundamental physics experiments using compact spin-transparent storage rings of low energy polarized electron beams
We present a new design of highly specialized small storage rings for low energy polarized electron beams. The new design is based on the transparent spin methodology that cancels the spin precession due to the magnetic dipole moment at any energy while allowing for spin precession induced by the fundamental physics of interest to accumulate. The buildup of the vertical component of beam polarization can be measured using standard Mott polarimetry that is optimal at low electron energy. These rings can be used to measure the permanent electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for dark energy and ultra-light dark matter.
Simultaneous neutron and X-ray tomography for visualization of graphite electrode degradation in fast-charged lithium-ion batteries
Advanced battery characterization using neutron and X-ray-based imaging modalities is crucial to reveal fundamental degradation modes of lithium-ion batteries (LIBs). Taking advantage of the sensitivity of neutrons to some low-Z (Li) and X-rays to high-Z materials (Cu), here we demonstrate the viability of simultaneous neutron and X-ray tomography (NeXT) as a non-destructive imaging platform for ex-situ 3D visualization of graphite electrode degradation following extreme fast charging (XFC). In addition, we underscore the benefits of the simultaneous of NeXT by combining the neutron and X-ray tomography data from the same sample location for material identification and segmentation of one pristine and two XFC-cycled graphite electrodes (9C charge for 450 cycles). Our ex-situ results and methodology development pave the way for the design of NeXT-friendly LIB geometries that will allow operando and/or in-situ 3D visualization of electrode degradation during XFC.
A general strategy for preparing pyrrolic-N 4 type single-atom catalysts via pre-located isolated atoms
Single-atom catalysts (SACs) have been applied in many fields due to their superior catalytic performance. Because of the unique properties of the single-atom-site, using the single atoms as catalysts to synthesize SACs is promising. In this work, we have successfully achieved Co 1 SAC using Pt 1 atoms as catalysts. More importantly, this synthesis strategy can be extended to achieve Fe and Ni SACs as well. X-ray absorption spectroscopy (XAS) results demonstrate that the achieved Fe, Co, and Ni SACs are in a M1-pyrrolic N 4 (M= Fe, Co, and Ni) structure. Density functional theory (DFT) studies show that the Co(Cp) 2 dissociation is enhanced by Pt 1 atoms, thus leading to the formation of Co 1 atoms instead of nanoparticles. These SACs are also evaluated under hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), and the nature of active sites under HER are unveiled by the operando XAS studies. These new findings extend the application fields of SACs to catalytic fabrication methodology, which is promising for the rational design of advanced SACs.
Graph theory and nighttime imagery based microgrid design
Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. Finally, an application of the methodology is presented using real event, location, and asset data.
Guest Editorial: Special section on Resilient Control of Cyber-Physical Power and Energy Systems
Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats. Finally, understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.
Rethinking the soil core microbiome
The concept of a core microbiome emerged from host-associated research to describe microbial members or functions conserved across clearly defined spatial, temporal, and biological boundaries. In soil- and plant-associated microbiome research, however, the term has increasingly shifted toward analytically defined subsets selected using study-specific thresholds or criteria. Synthesizing recent literature and cross-site analyses of bioenergy crop field soils, we show that the original biological meaning of the core microbiome has been blurred by dataset-specific analytical criteria. Taxa designated as ‘core’ were highly sensitive to methodological choices and often reflected explanatory value rather than conserved biological membership. Moreover, many studies that identify taxonomic ‘core’ members interpret their significance in functional terms, suggesting that functional conservation may be the biological interest. Taxonomic conservation may not be the most biologically meaningful target in highly heterogeneous soil and rhizosphere systems, where functional conservation may persist despite taxonomic turnover. Accordingly, ‘core microbiome’ should be reserved for microbial components explicitly demonstrated to be conserved across defined spatial, temporal, and environmental dimensions and linked to conserved ecological functions, while taxa selected for explanatory value are better described as ‘explanatory subsets of taxa’. Greater terminological precision will improve cross-study comparability and strengthen ecological inference in plant–soil microbiome research.
Measuring the Electric Dipole Moment of the Electron in a Two-Energy Spin-Transparent Storage Ring
We will present a new design of a two-energy storage ring for low energy (0.2 to 2 MeV) polarized electron bunches [1]. The new design is based on the transparent spin methodology that cancels the spin precession due to the magnetic dipole moment at any energy while allowing for spin precession induced by the fundamental physics of interest to accumulate. The buildup of the vertical component of beam polarization can be measured using standard Mott polarimetry that is optimal at low electron energy. These rings can be used to measure the permanent electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for dark energy and ultra-light dark matter.
All-Source Competitive Solicitations: State and Electric Utility Practices
This report provides an overview of principles, practices, and emerging issues in all-source competitive solicitations. It reviews the history of electric utility all-source solicitations and recent results; provides an overview of goals, design tradeoffs, and stages; and examines solicitation design and implementation options and issues, including methodological issues around bid evaluation. The report draws on a review of regulatory documents, resource plans, solicitation materials, independent evaluator reports, and scholarly literature. While the report focuses on solicitations by investor-owned utilities for bulk power system needs, it also briefly discusses solicitations for non-wires alternatives for distribution system needs, as well as nascent practices for integrating both types of procurements.
Instrumentation Design and Testing Thoughts [Slides]
Slides for a presentation discussing methodologies and instruments used to test radiation in the lunar environment.
Measuring the Electric Dipole Moment of the Electron in a Two-Energy Spin-Transparent Storage Ring
We will present a new design of a two-energy storage ring for low energy (0.2 to 2 MeV) polarized electron bunches [1]. The new design is based on the transparent spin methodology that cancels the spin precession due to the magnetic dipole moment at any energy while allowing for spin precession induced by the fundamental physics of interest to accumulate. The buildup of the vertical component of beam polarization can be measured using standard Mott polarimetry that is optimal at low electron energy. These rings can be used to measure the permanent electric dipole moment of the electron, relevant to CP violation and matter-antimatter asymmetry in the universe, and to search for dark energy and ultra-light dark matter.
Machine learning for optimizing the manufacture and operation of intermediate temperature fuel cells
The project developed machine learning and artificial intelligence (ML/AI) methodologies for guiding and accelerating the materials design and processing of new high temperature proton conducing electrode ionomer binders and reinforced membranes for high-temperature proton exchange membrane fuel cell (HT-PEMFC; aka intermediate-temperature polymer electrolyte membrane fuel cells).
Front-end engineering design (FEED) studies: a quantitative analysis
NETL has devised a methodology for normalizing FEED study metrics of interest and allowing cautious quantitative comparison across FEED studies. This presentation introduces the novel quantitative comparison methodology and presents results from utilizing this methodology to examine data presented in recent FEED study reports. The quantitative methodology developed for the examination of FEED study performance and cost also allows comparison of real-world performance and costs against NETL TEA model predicted performance and cost. Learnings from examining NETL model predicted performance and cost versus real world reported values are highlighted. These learnings provide insight into NETL TEA model uncertainty and highlight opportunities for further model development.
Energy, Exergy, Entropy Generation Minimization, and Exergoenvironmental Analyses of Energy Systems-A Mini-Review
Meeting the growing energy demands has become a crucial challenge, which should employ energy integration and benefit from the extraction of the maximum thermodynamic potential of the resources consumed. This brief review presents an overview of the essential elements of the methods of energy, exergy, entropy generation minimization, and exergoenvironmental analyses. In combination, these methodologies constitute a powerful toolbox for the design, analysis, and optimization of energy systems. The quantification of energy and environmental impacts provided by them is essential in guiding toward system designs that are consistent with current energy and environmental needs.
Optimization and Performance Characterization of the Second Generation Fermilab Constant Fraction Discriminator Readout ASIC
We present the optimization and performance characterization of the second-generation Fermilab Constant Fraction Discriminator ASIC (FCFD), designed for the readout of AC-coupled low-gain avalanche detector (LGAD) strip-sensors. The FCFD is explicitly engineered to be insensitive to signal-amplitude variations, thereby removing the need for time-walk correction that is required in other LGAD time-stamping readout ASICs. This updated version, referred to as FCFD1.1, incorporates several enhancements over the first iteration to address key challenges in AC-coupled LGAD front-end design. We outline the primary readout-ASIC design considerations for these applications, describe the methodology used to evaluate critical sensor and system parameters, and summarize the additionally implemented features. Performance measurements using injected charge signals and minimum-ionizing particles in test-beams demonstrate a time resolution of approximately 40 ps and a position resolution of roughly \SI{15}{\micro\meter} when tested with beam particles uniformly over the active area of the sensor.
Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques
Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.
Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure‐Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx
Abstract A novel ab initio methodology based on high‐throughput simulations has permitted designing unique biselective organic structure‐directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well‐established commercial CHA‐type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.