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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An Adsorptive Membrane Platform for Precision Ion Separation: Membrane Design and First‐Principles Studies

One of the key challenges in separation science is the lack of precise ion separation methods and mechanistic understanding crucial for efficiently recovering critical materials from complex aqueous matrices. Herein, first-principles electronic structure calculations and in situ Raman spectroscopy are studied to elucidate the factors governing ion discrimination in an adsorptive membrane specifically designed for transition metal ion separation. Density functional theory calculations and in situ Raman data jointly reveal the thermodynamically favorable binding preferences and detailed adsorption mechanisms for competing ions. How membrane binding preferences correlate with the electronic properties of ligands is explored, such as orbital hybridization and electron localization. The findings underscore the importance of the phenolate group in oxime ligands for achieving high selectivity among competing transition metal ions. In-depth understanding on which specific atomistic site within the microenvironment of metal-ligand binding pockets governs the ion discrimination behaviors of the host will build a solid foundation to guide the rational design of next-generation materials for precision separation essential for energy technologies and environment remediation. In tandem, synthetic controllability is demonstrated to transform 3D micrometer-scale crystals to a 2D crystalline selective layer in membranes, paving the way for more precise and sustainable advances in separation science.

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

Solvent Polarity Independent Symmetry-Breaking Charge Separation in a Slip-Stacked Covalent Terrylene Monoimide Dimer

The design of organic materials capable of efficient photoinduced charge separation in low-polarity environments is a critical challenge for advancing organic photovoltaics. Symmetry-breaking charge separation (SB-CS) offers a promising route to efficient charge separation with minimal energy loss; however, in conventional organic materials this process typically relies on polar solvents to stabilize the charge-separated state. Here, we investigate a slip-stacked terrylene monoimide dimer (TMI 2 ) engineered with intrinsic electronic asymmetry to circumvent this limitation. We demonstrate through a comprehensive suite of ultrafast spectroscopic techniques that TMI 2 undergoes remarkably efficient SB-CS regardless of solvent polarity enabled by the permanent dipole moment of TMI and the accessible intramolecular charge-transfer (ICT) character of its monomer units. Furthermore, analysis of vibronic coherences reveals that the initial state mixing is actively driven by a 193 cm –1 intermolecular mode, while the final SB-CS state is marked by a distinct vibrational fingerprint of the radical ion pair product. Our findings reveal how the intrinsic electronic asymmetry of the TMI monomers that constitute TMI 2 creates an efficient charge separation pathway that precludes the need for external stabilization from a polar solvent. Here, this work establishes a powerful molecular design principle for leveraging intrinsic monomeric asymmetry to achieve efficient charge separation in low-dielectric environments, with significant implications for the development of next-generation organic optoelectronic materials.

Aromatic compounds↗
DOE OSTI2026

Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition

Background Fire is a foundational ecological process that shapes ecosystem structure, diversity, and resilience. Quantifying paleofire regime attributes such as frequency, severity, and intensity is essential for understanding the historical range of variability in fire behavior and its ecological effects. While frequency and severity are often reconstructed in paleofire studies, quantitative reconstructions of fire intensity remain limited. Recent work has shown that maximum pyrolysis temperature—a proxy for fire intensity—and plant species type can be inferred from charcoal using transmission Fourier-transform infrared (FTIR) spectroscopy. However, the sample preparation for transmission FTIR is destructive and time-consuming, limiting application and reuse of materials for other analyses. We evaluated reflectance FTIR spectroscopy as a non-destructive alternative for reconstructing combustion temperature and plant species from laboratory-generated charcoal. We also examined the influence of contrasting airflow environments (ambient air versus nitrogen-rich) on pyrolysis temperature and plant species reconstruction prediction accuracies and compared predictive performance between a novel, neural network–based deep learning model with the traditional modern analogue technique (MAT) using k-nearest neighbor functions. As proof of concept, we apply our enhanced methodology to ancient charcoal to demonstrate applicability at improving long-term fire regime reconstructions and the ability to link paleofire records with contemporary fire ecology. Results Our analysis shows that transmission and reflectance FTIR spectra yield comparable spectral profiles. However, sample preparation for reflectance FTIR is minimal and non-destructive, unlike transmission FTIR which is destructive. We demonstrate that oxygen environments improved reconstruction accuracy relative to nitrogen-rich conditions. Finally, our deep learning neural network (DL) achieved testing accuracies of 98.7% for temperature and 96.2% for species identification, outperforming MAT’s k-NN approach (89.8% and 65.9%, respectively). A Shapley importance analysis identified 5 key spectral regions that greatly influenced the model’s temperature or species categorization. When applied to ancient charcoal, our results show historic fires from the most recent past primarily burned at low intensities (400–500 °C), reflective of natural fire regimes in ponderosa pine forests. Our results corroborate charcoal morphology data that suggests all ancient charcoal originated from burned woody plant types. Conclusions By combining reflectance FTIR spectroscopy with a deep learning approach, we provide the first accuracies high enough to confidently identify both species and temperature from laboratory-produced charcoal, improving quantitative reconstructions of fire intensity and fuel composition from paleofire records. This opens a wide range of research into the link between fire and larger drivers (i.e., climate or human) and greater ecological understanding of fire regimes beyond that of burn scars or recent observations. These methodological improvements have direct relevance for fire management by improving interpretation of historical fire behavior, informing fuel–fire relationships, and providing a scalable analytical framework applicable to both long-term ecological studies and contemporary fire science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An Efficient Storage-Driven Machine Learning Model for Performance in the Era of Multimodal Scientific Data

Scientific workflows are increasingly relying on machine learning (ML), simulation, and hybrid techniques to predict, understand, and optimize the behavior of complex experiments. High-performance computing has greatly improved researchers’ ability to acquire diverse data modalities in these workflows. Recent studies suggest that the performance of machine learning models can be improved by integrating data from various sources. Unfortunately, these workloads pose unprecedent pressure on the network storage to meet the demands associated with accessing these multimodal data. To mitigate the impact of intensive IO, we propose a solution that utilizes a multi-tier High-Performance Computing (HPC) distributed storage and data processing framework, placing computation where the data resides for better performance. By adopting this project, the scientific community will gain new opportunities to explore multimodal storage-driven possibilities, integrating multiple scientific data sources with advanced streaming frameworks. Additionally, our framework effectively utilizes computing resources and bridges the gaps identified by HPC experts. Our proposed approach tackles scalability and persistence challenges by leveraging native persistency, which has posed difficulties in traditional approaches. Furthermore, we seek to enhance fault-tolerance and load-balance of computations by leveraging real-time streaming in diverse scientific computing environments, thereby propelling advanced scientific computing research into the next generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

RaDIATE Collaboration Thermal shock studies

As next-generation accelerator target facilities (High Energy Physics, Spallation Sources, ... ) become increasingly more powerful and intense, high power target systems face key technical challenges, such as radiation damage and thermal shock. Those ultimately degrade the performance and lifetime of targets and have been identified as the leading cross-cutting challenges of high-power target facilities. In order to operate reliable beam-intercepting devices in the framework of energy and intensity increase for next generation accelerators, the RaDIATE Collaboration (Radiation Damage In Accelerator Target Environment), established in 2012 and managed by Fermilab, brings together existing expertise in nuclear material and accelerator targets from 20 international institutions, including CERN, to execute a coordinated strategy for high power targetry R&D. In this context, several thermal shock studies were performed at CERN's HiRadMat (High-Radiation to Materials) facility, that took a key step towards improving our knowledge on target damage tolerance. The first experiment HRMT-24, supported by EURCARD2 and completed in 2015, successfully validated the Johnson-Cook strength model developed at SwRI on beryllium S200FH (used for beam window material), providing a better confidence in simulating the thermal shock response of current and future S200FH beryllium components. HRMT-43, supported by ARIES and completed in 2018, tested various materials (Be, C, SiC, Si, Ti and ceramic nanofiber). It was a first and unique test which included pre-irradiated specimens from high energy proton beam irradiation to identify thermal shock response differences between non-irradiated and previously irradiated materials. Real-time measurement of dynamic thermomechanical response of graphite helped to benchmark numerical simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Creating the Distributed Energy Resources Education Center (DEREC)

The built environment in the United States consumes 40% of the energy generated and emits roughly the same percentage of total carbon footprint. Distributed energy resources (DER), small or modular energy generation and storage technologies, present the nation with an opportunity to substantially improve those metrics while securing the nation’s energy independence. As opportunities increase for implementing such technologies, they also continue to evolve and often outpace the nation’s traditional building practices. In an effort to effectively and proactively incorporate distributed energy resources into the nation’s energy supply, Southface Energy Institute convened with national and regional partners to create the Distributed Energy Resources Education Center (DEREC). Using national model codes and their regionally amended versions as a collective starting point, the DEREC team collaborated with industry experts and identified impediments to effective implementation of DERs, developing discipline-specific curriculum to eliminate those impediments. The center, developed in collaboration with Interstate Renewable Energy Committee (IREC) and National Buildings Institute (NBI), leverages existing DER education content as well as new and dynamic training materials and online courses that collectively engage the many roles necessary for DER implementations, including designers, code officials, builders and skilled trades, and building owners who specify, inspect, build, operate, and maintain buildings with DERs.

14 SOLAR ENERGY↗
DOE OSTI2025

The Effect of Metal Promoters in an Mo-Supported HZSM-5 Catalyst for Microwave-Assisted Methane Dehydroaromatization to Aromatics

Microwave (MW)-assisted methane dehydroaromatization (MDHA) using an Mo-supported HZSM-5 catalyst (Mo/HZ5) can convert methane into value-added aromatic products in modular microwave reactor systems, enabling producers to generate revenue from an otherwise wasted resource. Modifying the local environments of active Mo species with metal promoters potentially regulates the reaction/deactivation pathways and improves the heating properties of the Mo/HZ5 under microwaves. Herein, metal promoters (M), including monovalent K+ and bivalent Co2+ and Ni2+, were incorporated to form M-Mo/HZ5 and their MDHA performance was investigated.

metal promoters↗
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

Plasma instabilities dominate radioactive transients magnetic fields: the self-confinement of leptons in Type Ia and core-collapse supernovae, and kilonovae

The light curves of radioactive transients, such as supernovae and kilonovae, are powered by the decay of radioisotopes, which release high-energy leptons through $\beta ^+$ and $\beta ^-$ decays. These leptons deposit energy into the expanding ejecta. As the ejecta density decreases during expansion, the plasma becomes collisionless, with particle motion governed by electromagnetic forces. In such environments, strong or turbulent magnetic fields are thought to confine particles, though the origin of these fields and the confinement mechanism have remained unclear. Using fully kinetic particle-in-cell (PIC) simulations, we demonstrate that plasma instabilities can naturally confine high-energy leptons. These leptons generate magnetic fields through plasma streaming instabilities, even in the absence of pre-existing fields. The self-generated magnetic fields slow lepton diffusion, enabling confinement, and transferring energy to thermal electrons and ions. Our results naturally explain the positron trapping inferred from late-time observations of thermonuclear and core-collapse supernovae. Furthermore, they suggest potential implications for electron dynamics in the ejecta of kilonovae. We also estimate synchrotron radio luminosities from positrons for Type Ia supernovae and find that such emission could only be detectable with next-generation radio observatories from a Galactic or local-group supernova in an environment without any circumstellar material.

instabilities↗
DOE OSTI2025

Polymer nanoparticle photocatalysts realized in non-aqueous solvents

Colloidal organic nanoparticles (oNPs) have emerged as a promising category of photocatalyst, thanks to their long-lived surface-bound charges, electronic tunability, and strong absorption in the visible spectrum. Our previous research has established a direct correlation between charge generation in oNPs and their photocatalytic activity, highlighting their effectiveness as a framework for stable, long-lived free carriers. However, oNPs have been restricted to use only in aqueous environments as a result of being synthesized via either nano-emulsion or nano-precipitation procedures. Herein, we present a method for transferring oNP photocatalysts from water into polar non-aqueous solvents while retaining their long-term colloidal stability. We observed that the polymer chains in the solvent-transferred oNPs rearrange from a predominantly H-aggregate structure in water to a combination of H- and J-aggregate characteristics in N,N-dimethylformamide, suggesting a dynamic rearrangement in response to the new solvent environment. Importantly, transient absorption and time-resolved microwave conductivity measurements confirm that the solvent-transferred oNPs maintain their ability to generate free charges at an internal heterojunction. This development opens unique opportunities for eventually leveraging light-generated, long-lived electrons and holes in synthetic redox chemistry across diverse solvent environments, a direction that will be explored in future studies.

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

Steam generator model design parameter sensitivity study for small modular reactor system

Here, this study focuses on design parameter sensitivity studies pertaining to several Once-Through Steam Generator (OTSG) model cases both with and without a riser using python and advanced risk assessment and optimization tool, i.e. Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), to support a Small Modular Reactor (SMR) system. The presented Steam Generator (SG) python-based model is a mathematical representation of a steam-generating unit for a Pressurized Water Reactor (PWR)-type SMR system, including fluid flow and heat transfer equations, models, and correlations. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system, such as the Heat Transfer Coefficient (HTC), Reynolds number, Nusselt number, and heat transfer performance. Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in the input parameters. By using RAVEN, detailed design parametric sensitivity studies. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10 % relative changes) for 600 samples. The analysis results give valuable insights into SG system performance, and provide justification for further research and development such as optimized sensor placement, design verification, validation, and optimization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ghost imaging second harmonic generation microscopy

A system and methods for ghost imaging second harmonic generation microscopy. Imaging data is collected in parallel, providing faster imagine reconstruction and enabling reconstruction in scattering environments. Ghost imaging, split light beam interacting with a target and a second light beam unimpeded and not required to pass through the same background. A second harmonic generation image is reconstructed from the detected photons.

Wiederrecht, Gary P.↗
DOE OSTI2024

Understanding Formation of Irradiation-Induced Defects through 4D-STEM, Electron Tomography, and WBDF-STEM

A major challenge in advancing nuclear materials for next-generation fission and proposed fusion reactors is to comprehensively understand the formation of irradiation-induced defects. Here it is essential to correlate the evolution of irradiation-induced defects and the degradation of mechanical properties, as they collectively dictate the material's lifespan and ensure nuclear safety. Scanning transmission electron microscopy (STEM) based techniques have emerged as indispensable tools for irradiation-induced defect characterization, offering high spatial resolution imaging and chemical analysis, such as electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDXS). These techniques have been effectively used to obtain an atomic-scale view of the defect structure. Recent advances in electron microscopy, particularly in 4D-STEM, offer detailed insight into microstructural evolution by capturing full 2D diffraction patterns at every pixel position. Using high-speed direct electron detectors, this technology generates a four-dimensional dataset, overcoming the limitations of traditional STEM imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Expanding Solar Research and Generation for a Bright Energy Future (Final Technical Report)

This project supported the construction of an outdoor solar research facility for the primary purpose of characterizing panel and system lifetimes in the northern New England climate which features cold winters and warm, humid summers. The facility is designed to be integrated into future efforts to explore the impacts of the New England climate on energy storage and grid integration, as well as methods to mitigate factors like snow and dust coverage in the industrial and agricultural environment of the Intervale farming community and the McNeil wood-fired power generating station. It will provide year-round renewable energy research opportunities for faculty and students. As renewable energy generation and storage have become research priorities for UVM, for the state of Vermont, and for the United States as a whole, this facility will benefit the public on a broad range of scales.

14 SOLAR ENERGY↗
DOE OSTI2025

Spatial profile of argon (1s 5 ) metastables in an electron beam generated plasma

Electron beams with an applied magnetic field generate a secondary cold plasma with a selective chemical composition, featuring low-energy ions and metastable species in the discharge periphery, ideal for low-damage plasma treatment of material substrates. In this work, we studied the plasma generated by an e-beam using a 4 kV voltage in a pure argon gas environment under a magnetic field of 150 G and in the pressure range of 25–90 mTorr. We measured the absolute spatial density profile of argon (1 s 5 ) metastables in an electron beam generated plasma by laser-induced fluorescence and found it to be of the order of 10 16 m −3 . The electron temperature and the electron density measured by a Langmuir probe were of the order of 10 16 m −3 and less than an eV respectively. Electron-impact quenching was identified as a significant loss mechanism for the Ar(1s 5 ) state, leading to the saturation of the metastable density at higher pressures. Outside the primary ionization region, the spatial distribution of argon metastables followed a linear diffusion profile, indicating negligible additional production in those regions.

EEDF↗
DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗