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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

Li, He↗
DOE OSTI2025

Synthesis challenges, thermodynamic stability, and growth kinetics of La–Si–P ternary compounds

Although many new compounds have been recently predicted with the help of machine learning, the successful experimental synthesis of these compounds remains challenging. Computational insights about the thermodynamic stability and phase formation kinetics among the ground state and competing metastable phases are highly desirable to rationalize and attempt to overcome synthesis challenges experimentally. In this work, we explore synthetic challenges within ternary La–Si–P compounds through feedback between experimental and computational studies. We discuss the experimental challenges in forming three computationally predicted ternary phases (La 2 SiP, La 5 SiP 3 , and La 2 SiP 3 ). To understand the synthetic challenges, we performed molecular dynamics (MD) simulations using an accurate and efficient artificial neural network machine learning (ANN-ML) interatomic potential. We study the phase stability and formation kinetics of these ternary phases in relation to the reported and synthesized La 2 SiP 4 phase. While the growth of the La 2 SiP 4 phase can be reproduced by our MD simulation, our results indicate that the rapid formation of a Si-substituted LaP crystalline phase is a major barrier to the synthesis of the predicted La 2 SiP, La 5 SiP 3 , and La 2 SiP 3 ternary compounds, agreeing well with experimental observations. Our simulations also suggest that there is a narrow temperature window in which the La 2 SiP 3 phase can be grown from the solid–liquid interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluating the contribution of plant metabolic pathways in the light to the ATP:NADPH demand using a meta-analysis of isotopically non-stationary metabolic flux analyses

Abstract Balancing the ATP: NADPH demand from plant metabolism with supply from photosynthesis is essential for preventing photodamage and operating efficiently, so understanding its drivers is important for integrating metabolism with the light reactions of photosynthesis and for bioengineering efforts that may radically change this demand. It is often assumed that the C3 cycle and photorespiration consume the largest amount of ATP and reductant in illuminated leaves and as a result mostly determine the ATP: NADPH demand. However, the quantitative extent to which other energy consuming metabolic processes contribute in large ways to overall ATP: NADPH demand remains unknown. Here, we used the metabolic flux networks of numerous recently published isotopically non-stationary metabolic flux analyses (INST-MFA) to evaluate flux through the C3 cycle, photorespiration, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and starch/sucrose synthesis and characterize broad trends in the demand of energy across different pathways and compartments as well as in the overall ATP:NADPH demand. These data sets include a variety of species including Arabidopsis thaliana , Nicotiana tabacum , and Camelina sativa as well as varying environmental factors including high/low light, day length, and photorespiratory levels. Examining these datasets in aggregate reveals that ultimately the bulk of the energy flux occurred in the C3 cycle and photorespiration, however, the energy demand from these pathways did not determine the ATP: NADPH demand alone. Instead, a notable contribution was revealed from starch and sucrose synthesis which might counterbalance photorespiratory demand and result in fewer adjustments in mechanisms which balance the ATP deficit.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Thermal Activation of Zirconium(IV) Acetylacetonate Catalysts to Enhance Polyurethane Synthesis and Reprocessing

Carbamate formation and exchange catalysts enable efficient polyurethane (PU) manufacturing, as well as emerging recycling and reprocessing methods for PU thermosets. Zirconium β-diketonate complexes, such as Zr acetylacetonate [Zr(acac) 4 ], are effective alternatives to toxic organotin catalysts that have been used for PU reprocessing. Here, we report that Zr(acac) 4 undergoes a thermally activated process in the PU network during reprocessing that transforms it into a more active carbamate exchange catalyst. This process is associated with the irreversible loss of acetylacetonate ligands and is not observed for the more sterically hindered Zr 2,2,6,6-tetramethyl-3,5-heptanedione [Zr(tmhd) 4 ] complex. Crossover experiments between PU thermoplastics indicated enhanced carbamate exchange after the thermal activation of Zr(acac) 4 in the presence of one of the PUs, whereas a sample of Zr(acac) 4 activated in the absence of the PU had no catalytic activity. Thermal gravimetric analysis suggested that this process is associated with the loss of one protonated acac ligand. Stress relaxation analysis of PU thermosets indicated a distinct change in the characteristic relaxation time associated with the thermal activation of Zr(acac) 4 at temperatures above 140 °C; no such change was observed for samples reprocessed using Zr(tmhd) 4 . Density functional theory and molecular experiments suggest that irreversible ligand exchange of acac with alkoxide or carbamate reduces the activation energy for urethane formation and reversion. Furthermore, the Zr(acac) 4 catalyst activated in the presence of a PU’s polyol precursor provided more porous and less dense PU foams compared to those made using the unactivated Zr(acac) 4 catalyst. Furthermore, these findings are important for developing improved PU synthesis and recycling processes. Thermally activating a catalyst during reprocessing may provide more nuanced control of the in-use and reprocessing characteristics of PU thermosets.

Alcohols↗
DOE OSTI2024

Techno-economic analysis and network design for CO 2 conversion to jet fuels in the United States

The conversion of carbon dioxide (CO 2 ) into jet fuel holds significant potential for reducing CO 2 emissions, providing an alternative to carbon-based resources, and offering a renewable means of energy storage. The objective of this study is to conduct a techno-economic analysis and optimize the supply chain network for converting CO 2 to jet fuel in the United States, aiming to minimize total costs while assessing the environmental and economic feasibility of two CO 2 conversion pathways. This first pathway is based on Fischer-Tropsch synthesis (FTS), and the other one is based on the valorization and upgrading of light methanol (MeOH). Incorporating spatial and techno-economic data, a mixed-integer linear programming model was developed to select source plants and conversion pathways, locations of conversion refinery sites, and the amount of captured CO 2 across the United States. The optimal results indicate that the FTS pathway is adopted at all selected refineries when the hydrogen price is 1000 dollars/t and the operating cost, mainly electricity used in conversion, is reduced to 5 % of its current level. Under this scenario, the total annual profit is 8 billion dollars, and the net carbon emissions are -88,783,284 tons. The sensitivity analyses reveal that the prices of electricity and hydrogen significantly contribute to total production costs. The CO 2 recycle percentage of the FTS pathway influences the choice of applied pathways at refineries. Additionally, a higher conversion rate holds a substantial promise for reducing the total production cost and can make the MeOH pathway a viable choice.

10 SYNTHETIC FUELS↗
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 OSTI2024

Building a controlled-NOT gate between polarization and frequency

By harnessing multiple degrees of freedom (DoFs) within a single photon, controlled quantum unitaries, such as the two-qubit controlled-NOT ( cnot ) gate, play a pivotal role in advancing quantum communication protocols such as dense coding and entanglement distillation. In this work, we devise and realize a cnot operation between polarization and frequency DoFs by exploiting directionally dependent electro-optic phase modulation within a fiber Sagnac loop. Alongside computational basis measurements, we validate the effectiveness of this operation through the synthesis of all four Bell states in a single photon, all with fidelities greater than 98%. This demonstration opens new avenues for manipulating hyperentanglement across these two crucial DoFs, marking a foundational step toward leveraging polarization-frequency resources in fiber networks for future quantum applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
DOE OSTI2024

Bacterial microcompartments as a next-generation metabolic engineering tool: utilizing nature's solution for confining challenging catabolic pathways

Advancements in synthetic biology have facilitated the incorporation of heterologous metabolic pathways into various bacterial chassis, leading to the synthesis of targeted bioproducts. However, total output from heterologous production pathways can suffer from low flux, enzyme promiscuity, formation of toxic intermediates, or intermediate loss to competing reactions, which ultimately hinder their full potential. The self-assembling, easy-to-modify, protein-based bacterial microcompartments (BMCs) offer a sophisticated way to overcome these obstacles by acting as an autonomous catalytic module decoupled from the cell's regulatory and metabolic networks. More than a decade of fundamental research on various types of BMCs, particularly structural studies of shells and their self-assembly, the recruitment of enzymes to BMC shell scaffolds, and the involvement of ancillary proteins such as transporters, regulators, and activating enzymes in the integration of BMCs into the cell's metabolism, has significantly moved the field forward. These advances have enabled bioengineers to design synthetic multi-enzyme BMCs to promote ethanol or hydrogen production, increase cellular polyphosphate levels, and convert glycerol to propanediol or formate to pyruvate. These pioneering efforts demonstrate the enormous potential of synthetic BMCs to encapsulate non-native multi-enzyme biochemical pathways for the synthesis of high-value products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Synthesis of boron-carbide aerogels

We present the synthesis of boron carbide aerogels utilizing nano-boron powder and resorcinol–formaldehyde (RF) organic aerogels as precursors. Monolithic aerogels were fabricated from suspensions of boron nanoparticles and RF via an organic sol-gel process, enabling effective distribution of boron in the gel network. The resulting gels underwent supercritical drying, thermal reduction, and subsequent heat treatment to yield boron carbide aerogels with densities ranging from 37 to 55 mg/cm³. By tuning the boron-to-carbon ratio, heat treatment temperature, and dwell time, surface areas up to 53 m²/g were obtained. X-ray diffraction analysis confirmed the formation of the boron carbide phase and detected the presence of residual carbon within the structure.

Materials science↗
DOE OSTI2025

Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting

This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24 years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. In conclusion, by introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.

Demographic modeling↗
DOE OSTI2026

Achieving ultrahigh modulus of resilience and enhanced thermal stability in ZnO x /SU-8 interpenetrating network polymer nanocomposite nanopillars

The modulus of resilience, a mechanical property that quantifies the maximum strain energy density a material can store during elastic deformation, is a crucial parameter for materials used in flexible displays, micro/nano-electro-mechanical system (M/NEMS) actuators, and ultra-sensitive pressure sensors. In this study, ZnO x /SU-8 nanocomposite nanopillars with a diameter of 300 nm, fully infiltrated with a uniformly distributed, interpenetrating amorphous ZnO x filler network, were synthesized via vapor-phase infiltration (VPI). In-situ uniaxial nano-compression tests revealed that the modulus of resilience of ZnO x /SU-8 reaches ∼ 12 MJ/m 3 , which is an ultrahigh value among all engineering materials with comparable strength. In addition, the synthesis fidelity, inorganic infiltration depth, and mechanical performance were all significantly improved compared to VPI-synthesized AlO x nanocomposites. Thermal stability, another key requirement for M/NEMS device materials operating under extreme environments, was also notably enhanced. Furthermore, partial crystallization of the amorphous ZnO x fillers during annealing contributed to an additional increase in modulus of resilience, reaching up to ∼ 13.9 MJ/m 3 . This work presents an effective fabrication strategy for producing nanostructured organic–inorganic hybrid nanocomposites with ultrahigh modulus of resilience and superior thermal stability, paving the way for their integration into next-generation flexible displays and high-performance M/NEMS devices working under harsh environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing stability, magnetic anisotropy, and coercivity of manganese aluminum: Machine learning, ab initio , and micromagnetic modeling

The binary manganese aluminum (MnAl) alloy with L⁢1 0 crystal structure is a promising rare earth (RE) element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, in this study, an alternative method of stabilizing the material, a pathway for experimental synthesis and validation, is proposed and theoretically verified. This is done by partially substituting Mn and Al sites with Fe and Ni and identifying its enhanced phase stability, saturation magnetization density, magnetic anisotropy, and coercivity from density functional theory (DFT), machine learning (ML) crystal graph convolution neural network (CGCNN), and micro-magnetic modeling. When considering a fixed 50% Ni, the magnetic anisotropy increases with the increasing Fe content but decreases the formation energy. The calculated formation energies, elastic constants, and phonon frequencies demonstrate that the binary and quaternary compositions are stable. Most importantly, in 50% Fe and Ni-substituted-equiatomic phase, magnetic anisotropy constants and saturation magnetization density increase by 56% and 23% as compared to the MnAl. Further, the coercivity of the equiatomic phase predicted with micro-magnetic modeling is higher by 17% than the parent compound.

Bhandari, Churna [Ames National Laboratory, and Io↗
DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗
DOE OSTI2025

A stable 15-member bacterial SynCom promotes Brachypodium growth under drought stress

Introduction: Rhizosphere microbiomes are known to drive soil nutrient cycling and influence plant fitness during adverse environmental conditions. Field-derived robust Synthetic Communities (SynComs) of microbes mimicking the diversity of rhizosphere microbiomes can greatly advance a deeper understanding of such processes. However, assembling stable, genetically tractable, reproducible, and scalable SynComs remains challenging. Methods: Here, we present a systematic approach using a combination of network analysis and cultivation-guided methods to construct a 15-member SynCom from the rhizobiome of Brachypodium distachyon. This SynCom incorporates diverse strains from five bacterial phyla. Genomic analysis of the individual strains was performed to reveal encoded plant growth-promoting traits, including genes for the synthesis of osmoprotectants (trehalose and betaine) and Na+/K+ transporters, and some predicted traits were validated by laboratory phenotypic assays. Results: The SynCom demonstrates strong stability both in vitro and in planta. Most strains encoded multiple plant growth-promoting functions, and several of these were confirmed experimentally. The presence of osmoprotectant and ion transporter genes likely contributed to the observed resilience of Brachypodium to drought stress, where plants amended with the SynCom recovered better than those without. We further observed preferential colonization of SynCom strains around root tips under stress, likely due to active interactions between plant root metabolites and bacteria. Discussion: Our results demonstrate that trait-informed construction of synthetic communities can yield stable, functionally diverse consortia that enhance plant resilience under drought. Preferential colonization near root tips points to active, localized plant-microbe signaling as a component of stress-responsive recruitment. This stable SynCom provides a scalable platform for probing mechanisms of plant-microbe interaction and for developing microbiome-based strategies to improve soil and crop performance in variable environments.

Yadav, Archana↗
DOE OSTI2026

Anisotropic magnetism and Kondo-lattice behavior in the frustrated antiferromagnet Ce 3 ⁢MgBi 5

Here, we report the synthesis and physical characterization of single-crystalline Ce 3 ⁢MgBi 5 , a previously unexplored member of the Ce 3 ⁢𝑀⁢𝑃⁢𝑛 5 family. This compound crystallizes in the hexagonal 𝑃⁢6 3 /𝑚⁢𝑐⁢𝑚 structure, featuring an anisotropic Ce sublattice composed of zigzag chains along the 𝑐 axis and a distorted kagome-like network in the basal plane. Magnetization measurements reveal antiferromagnetic order below 𝑇 𝑁 ≈ 4.2K with strong magnetic anisotropy and multiple field-induced metamagnetic transitions for fields applied perpendicular to [001], leading to a dome-shaped 𝐻–𝑇 phase diagram. Electrical transport exhibits characteristic signatures of a Ce-based Kondo lattice, including broad resistivity maxima and pronounced field-dependent anomalies in the magnetoresistance and Hall response that track the magnetic phase boundaries. Specific-heat measurements confirm the magnetic transition and show that the full R ⁢ln⁡ 2 entropy expected for a Ce 3+ Kramers doublet is recovered by 20 K, indicating an extended temperature range of magnetic fluctuations consistent with Kondo correlations. Our results establish Ce 3 ⁢MgBi 5 as a platform within the Ce 3 ⁢𝑀⁢𝑃⁢𝑛 5 family for exploring the interplay of geometric frustration, magnetic anisotropy, and Kondo-lattice physics under applied magnetic fields.

Kondo effect↗
DOE OSTI2025

Atomic-scale structure of ZrO 2 : Formation of metastable polymorphs

Metastable phases can exist within local minima in the potential energy landscape when they are kinetically “trapped” by various processing routes, such as thermal treatment, grain size reduction, chemical doping, interfacial stress, or irradiation. Despite the importance of metastable materials for many technological applications, little is known about the underlying structural mechanisms of the stabilization process and atomic-scale nature of the resulting defective metastable phase. Investigating ion-irradiated and nanocrystalline zirconia with neutron total scattering experiments, we show that metastable tetragonal ZrO 2 consists of an underlying structure of ferroelastic, orthorhombic nanoscale domains stabilized by a network of domain walls. The apparent long-range tetragonal structure that can be recovered to ambient conditions is only the configurational ensemble average of the underlying orthorhombic domains. This structural heterogeneity with a distinct short-range order is more broadly applicable to other nonequilibrium materials and provides insight into the synthesis and recovery of functional metastable phases with unique physical and chemical properties.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

SPARTA: High-Level Synthesis of Parallel Multi-Threaded Accelerators

This article presents a methodology for the Synthesis of PARallel multi-Threaded Accelerators (SPARTA) from OpenMP annotated C/C++ specifications. SPARTA extends an open-source HLS tool, enabling the generation of accelerators that provide latency tolerance for irregular memory accesses through multithreading, support fine-grained memory-level parallelism through a hot-potato deflection-based network-on-chip (NoC), support synchronization constructs, and can instantiate memory-side caches. Our approach is based on a custom runtime OpenMP library, providing flexibility and extensibility. Experimental results show high scalability when synthesizing irregular graph kernels. The accelerators generated with our approach are, on average, 2.29x faster than state-of-the-art HLS methodologies.

Design automation↗