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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

EFRC Accomplishments: Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC)

The vision of IMASC is to advance the fundamental science necessary to alter fundamental aspects of the chemical industries sector and its carbon footprint. The IMASC mission is to develop design principles for increasing the efficiency of key chemical syntheses using dilute alloy catalysts by quantitatively scaling from model studies to catalytic operation using advanced experiment and theory. One of the major challenges in catalysis is predicting catalytic behavior—especially selectivity. The IMASC team developed a methodology for obtaining critical information for selected catalytic processes using dilute alloy catalysts, that is widely applicable to modeling catalytic processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CMaize: Simplifying inter-package modularity from the build up

There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. Further, the need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dynamic Interfacial Architectures: Cruciferin‐Stabilized Oil/Water Interfaces for Sustainable Emulsions

Stabilizing oil-water interfaces in emulsions by plant-based proteins provides sustainable and tunable ways for designing emulsions with specific properties, for food, healthcare, and pharmaceuticals. Cruciferin, a protein from rapeseed, has great potential as green emulsifier, but details about its structure and mobility at oil-water interfaces are largely unknown. Here, these properties are studied with small angle neutron and x-ray scattering, and neutron spin echo spectroscopy, analyzed by atomistic modelling of scattering curves and coarse-grained modelling, to gain insight into interface coverage, and molecular conformation and mobility at the interface. Cruciferin assumes trimeric conformations at the interface, as in solution, but with its protrusions from the central core of the subunits (“arms”) more compressed. Interfacial mobility is only marginally lower than in solution, indicating the arms still transiently extend and preserve a network, for the first time revealing the mechanism how cruciferin forms highly elastic 2d gel-like oil-water interfaces, as observed in macroscopic rheology. The high interfacial mobility may help in self-repairing non-stabilized interfacial fractions, reducing coalescence. These findings provide a deeper molecular level understanding of proteins at oil-water interfaces, which can stimulate development of new plant-based emulsion products, and contribute to the global protein transition.

cruciferin↗
DOE OSTI2026

Investigating the Role of Polymer Architecture in Poly(methyl methacrylate) Depolymerization

Advanced architectures in polymers have garnered traction within the last two decades due to their distinct and tunable properties compared to linear analogs. However, the effects of architecture on nascent polymethacrylate depolymerization strategies remain underexplored. Herein, we investigate the depolymerization behavior of poly(methacrylate)-based star copolymers synthesized via a core-first approach. By incorporating either labile chain-end or pendent-group triggers, we demonstrate the first direct comparison of bulk depolymerization behaviors in star versus linear methacrylate copolymers under matched conditions. While pendent-triggered systems required higher loadings of N-(methacryloxy)phthalimide methacrylate (PhthMA) to achieve similar mass loss compared to linear copolymers, chain-end-initiated depolymerization showed enhanced efficiency of monomer liberation in the star topology. Furthermore, these findings highlight the importance of considering macromolecular architecture in designing sustainable polymers and provide actionable guidelines for installing depolymerization triggers based on polymer topology.

Copolymers↗
DOE OSTI2025

Coupling magnons to an opto-electronic parametric oscillator

Hybrid magnonic systems have emerged as versatile modular components for quantum signal transduction and sensing applications owing to their capability of connecting distinct quantum platforms. To date, the majority of the magnonic systems have been explored in a local, near-field scheme, due to the close proximity required for realizing a strong coupling between magnons and other excitations. This constraint greatly limits the applicability of magnons in developing remotely coupled, distributed quantum network systems. On the contrary, opto-electronic architectures hosting self-sustained oscillations have been a unique platform for long-haul signal transmission and processing. Here, we integrated an opto-electronic oscillator with a magnonic oscillator consisting of a microwave waveguide and a Y 3 Fe 5 O 12 (YIG) sphere, and demonstrated strong and coherent coupling between YIG’s magnon modes and the opto-electronic oscillator’s characteristic photon modes—revealing the hallmark anti-crossing gap in the measured spectrum. In particular, the photon mode is produced on-demand via a nonlinear, parametric process as stipulated by an external seed pump. Both the internal cavity phase and the external pump phase can be precisely tuned to stabilize either degenerate or nondegenerate auto-oscillations. Our result lays out a new, hybrid platform for investigating the long-distance coupling and nonlinearity in coherent magnonic phenomena, which may be found useful in constructing the future “distributed hybrid magnonic systems.”

36 MATERIALS SCIENCE↗
DOE OSTI2025

OR-AGENT framework – Architecting electrified heavy-duty drayage applications

The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.

Commercial vehicles↗
DOE OSTI2026

Multiphasic droplet microfluidics platform for controlled bacteria and mammalian cell co-culture

Microfluidics has revolutionized high-throughput miniaturized biological assays. However, co-culture of mammalian cells and bacteria remains challenging in microfluidic systems due to incompatible growth requirements, limited spatial control, and the requirement for a mammalian cell adhesion matrix. Here, we present a microfluidic platform that generates multiphasic droplets which encapsulate mammalian and bacterial cells, enabling their direct and indirect co-culture. By combining photopolymerizable hydrogels with polymer phase separation, we generate core–shell droplets composed of a liquid and a hydrogel compartment. The hydrogel compartment supports mammalian cell adhesion and culture, while the liquid compartment sustains bacterial growth. We demonstrate two droplet architectures that allow physical bacteria–mammalian cell contacts or enforce complete physical separation, representing direct and indirect co-culture. Our multiphasic droplets are stable, customizable, able to sustain co-culture for over 24 hours, and compatible with fluorescence-based cell sorting technologies. Overall, our multiphasic droplet microfluidic platform provides a scalable and versatile tool for high-throughput co-culture and screening of host–microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparative Study on the Photoelectrochemical Hydrogen Production from the Layout Optimization in a Unit Cell

Solar-driven water splitting is achieved by connecting photovoltaics with electrolysis. Recently, integrated approaches of the two configurations for photoelectrochemical reactions have shown great potential in a combined unit, but strategies to address further material and cell development face significant scientific challenges. Here, in this work, we present an experimental demonstration of photoelectrochemical (PEC) unit-cells with efficient separate dual compartments for oxygen evolution and hydrogen evolution reactions. The design and fabrication of cell architectures with various degrees of integration are investigated efficient and sustainable PEC processes. The effect of the nanostructured silicon (Si) photoelectrodes, including the adoption of layer architectures, surface protection layer deposition, and membrane electrode assembly is further studied to optimize the design parameters of the PEC unit-cells. This developed PEC cell with the Si photocathode achieved a photocurrent density of -4.8 mA cm –2 at -2.0 V cell and -2.87 mA cm –2 at -1.6 V cell . It clearly provides a critical milestone for unbiased solar water splitting. Practical solar to hydrogen (STH) can be directly estimated in investigating PEC performance in this unit-cell as assembled with a solar cell. It is a significantly meaningful step forward in practical solar fuel production.

08 HYDROGEN↗
DOE OSTI2025

Learning from Arctic Microgrids: Cost and Resiliency Projections for Renewable Energy Expansion with Hydrogen and Battery Storage

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.

25 ENERGY STORAGE↗
DOE OSTI2026

Applications of Decellularized Plant Tissues in Regenerative Medicine and Tissue Engineering

The development of biomaterials capable of supporting complex tissue growth remains a central challenge in regenerative medicine and tissue engineering, particularly in replicating the structural, mechanical, and transport functions of native extracellular matrices. While decellularized animal tissues have demonstrated significant success as scaffolds for tissue engineering, they are still constrained by cost, immunogenicity, and ethical concerns. In recent years, decellularized plant tissues have emerged as a compelling alternative scaffold platform due to their inherent vascular architectures, ethical sourcing, tunable mechanical properties, cytocompatibility, and sustainability. This review summarizes current strategies for the decellularization of plant tissues, including chemical, enzymatic, and physical approaches, and discusses how these methods preserve plant cell wall structure while removing immunogenic components. Advances in surface loading and functionalization, including protein coatings, oxidation, nanoparticle incorporation, peptide conjugation, and bioactive molecule loading, have further enhanced cell adhesion, differentiation, biodegradability, and immunomodulation. Recent applications of decellularized plant scaffolds in cardiac, skeletal muscle, bone, nerve, and wound healing contexts are reviewed, highlighting proof-of-concept successes and remaining challenges. Beyond therapeutic applications, plant-derived scaffolds have also enabled physiologically relevant in vitro models for vascular biology, mechanotransduction, cancer, metabolic tissues, and drug response studies. Collectively, these advances position decellularized plant tissues as versatile, low-cost, and ethically favorable biomaterials with growing relevance for both regenerative medicine and tissue modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhancing Time Synchronization in Smart Grid With White Rabbit: Theory, Architecture, and Challenges

The smart grid aims to provide economically efficient and sustainable power to consumers with high quality and security. However, the increasing integration of distributed renewable energy sources presents challenges for smart grid protection and control systems. Here, to enhance smart grid operations, this article introduces White Rabbit (WR) as a more precise and accurate time synchronization technique. To explore White Rabbit’s potential applications in the smart grid, this study first explains existing time synchronization techniques and their limitations, followed by an analysis of the role and importance of time synchronization in smart grids. A comprehensive survey is then conducted, covering the theories, principles, implementations, performances, and existing application cases of White Rabbit. The findings suggest that White Rabbit is a promising technique for smart grid deployment. However, several challenges remain on the path to large-scale implementation. These challenges are analyzed in detail, highlighting key areas for future research.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2024

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the “Second Green Revolution”. To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Genomic approaches to accelerate American chestnut restoration

More than a century after two introduced pathogens killed billions of American chestnut trees, introgression of resistance alleles from Chinese chestnuts has contributed to the recovery of self-sustaining populations. However, progress has been slow because of the complex genetic architecture of resistance. To better understand blight resistance, we compared reference genomes, gene expression responses, and stem metabolite profiles of the resistant Chinese and susceptible American chestnut species. To accelerate resistance breeding, we conducted large-scale phenotyping and genotyping in hybrids of these species. Simulation and inoculation experiments suggest that significant resistance gains are possible through selectively breeding trees with an average of 70 to 85% American chestnut ancestry. In conclusion, the resources developed in this work are foundational for breeding to create diverse restoration populations with sufficient disease resistance and competitive growth.

Westbrook, Jared W. [The American Chestnut Foundat↗
DOE OSTI2024

Neuro-Spark: A Submicrosecond Spiking Neural Networks Architecture for In-Sensor Filtering

Neuro-Spark, which is a new neuromorphic architecture with a field-programmable gate array (FPGA) implementation for ultrafast spiking neural network (SNN) inference at the edge, facilitates smart-pixel in-sensor filtering for high-energy physics experiments at the Large Hadron Collider (LHC). Utilizing the evolutionary optimization for neuromorphic systems (EONS) training method, we generate compact SNN models with 91% signal efficiency, akin to convolutional neural networks but with half the parameters. However, deploying near the detector poses a challenge because the SNN must handle a sustained input data rate exceeding 1013 GB/s. To overcome this, we propose a novel hardware architecture that uses high-level synthesis to construct a tuned architecture for the EONS-trained SNN. In addition to the analysis and validation with an AMD Xilinx Artix-A7 FPGA, our solution consumes only ç24% of FPGA LUT and flipflops. We also introduce an innovative quantization method that reduces FPGA resource utilization by ç15% without compromising accuracy. Our FPGA implementation achieves computing latency of ç10 ns for smart-pixel application inference on an edge FPGA.

Miniskar, Narasinga Rao↗
DOE OSTI2025

ReSpike: A Co-Design Framework for Evaluating SNNs on ReRAM-Based Neuromorphic Processors

With Moore’s law approaching its end, traditional von Neumann architectures are struggling to keep up with the exceeding performance and memory requirements of artificial intelligence and machine learning algorithms. Unconventional computing approaches such as neuromorphic computing that leverage spiking neural networks (SNNs) to perform computation are gaining traction and seek the paradigm shift necessary to sustain the increasing demands of modern applications. Novel memory technologies, such as resistive RAM (ReRAM), employ a crossbar architecture that possesses the inherent capability of efficiently computing vector-matrix multiplication—a dominant operation in SNNs. The prospect of naturally mapping SNNs to the crossbar structures provides a unique opportunity for achieving a high-performance, power-efficient neuromorphic system. In this work, we present ReSpike, which is a new framework, behavioral simulator, and architectural design based on ReRAM crossbar architectures, enabling modeling and co-design to achieve efficient execution of SNNs. We drive this co-design forward by quantifying the impact that ReRAM cell nonidealities have on the corresponding accuracy of an SNN application.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗
DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗
DOE OSTI2025

Cryo-EM structure of a methanogen nitrogenase-PII protein supercomplex

Abstract Nitrogenases are metalloenzymes that catalyze the reduction of atmospheric dinitrogen to ammonia, sustaining the global nitrogen cycle. While bacterial nitrogenase has been extensively characterized, the architecture and regulation of archaeal nitrogenases remain unknown despite longstanding evidence of nitrogen fixation in methanogens. Here we report a 3.1 Å cryo-electron microscopy structure of a native nitrogenase–PII protein supercomplex from Methanosarcina acetivorans. The structure reveals an unprecedented assembly of three NifDK heterotetramers bridged by six NifI1,2 heterotrimeric PII complexes, which sterically block NifH association and lock the enzyme in an inactive state. The NifI complexes display asymmetric binding of ADP and 2-oxoglutarate, coupling nitrogenase inhibition directly to cellular energy and nitrogen status. Addition of 2-oxoglutarate and ATP releases the NifI complexes, stimulating a threefold increase in NifDK activity in vitro. This higher-order architecture uncovers a previously unrecognized regulatory strategy in methanogens, in which PII proteins drive nitrogenase oligomerization to control activity. The discovery that nitrogenase activity may be modulated through direct assembly into higher-order structures opens new avenues for exploring nitrogenase evolution, regulation, and biotechnological applications. One sentence summary Discovery of a nitrogenase-PII protein supercomplex in methanogens, uncovering a metabolite-gated assembly mechanism for nitrogenase inhibition.

Nitrogenase, Electron Transfer, Methanogen↗