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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Renewable hydrogen horizon: Geospatial techno-economic feasibility and life cycle greenhouse gas analysis in the Middle East and North Africa

Renewable hydrogen is receiving increasing attention for its potential as a flexible energy carrier in sectors such as transportation and industry. Specific cost and carbon intensity (CI) of renewable hydrogen production vary largely based on the location, owing to differences in renewable energy resources, as well as the supply chain dynamics. This study maps the techno-economic and life cycle greenhouse gas emissions of renewable hydrogen production in the Middle East and North Africa region, leveraging abundant solar and wind resources. The work investigates the variability in hydrogen costs and CI, optimally sizing proton-exchange membrane (PEM) electrolyzers to account for partial and cyclic loading, and explores standalone versus grid-connected systems. PEM capacity ratios of 52 %–63 % for photovoltaic (PV) systems and 28 %–82 % for wind systems were identified as optimal, with hydrogen production costs ranging from $\$3.8$-$\$4.8$/kg for PV and $2.0-$7.0/kg for wind. CIs span from 1.9 to 3.7 kg CO 2 ,eq /kg H 2 for PV and 0.4–7.7 kg CO 2,eq /kg H 2 for wind systems. The study highlights significant cost and CI reductions achievable with technological advancements and co-product revenue from oxygen and excess electricity sales.

Carbon Intensity↗
DOE OSTI2022

Optimal design of multi-stage vacuum membrane distillation and integration with supercritical water desalination for improved zero liquid discharge desalination

Herein this paper proposes a novel concept for the optimal design of multi-stage vacuum membrane distillation (VMD). Generally, a multi-stage VMD is designed with an equal temperature difference between each stage. However, such a design is energy inefficient and increases VMD area. An analytical methodology for calculating the optimal stage temperature is proposed. By selecting the optimal stage temperature, the energy efficiency and required membrane area can be potentially improved by 16% and 30%, respectively. The proposed concept applies to all heat sources, including latent, sensible, and waste heat. To illustrate the method, multi-stage VMD is integrated with the waste heat from a supercritical water desalination (SCWD) system to achieve zero liquid discharge. SCWD is an energy-intensive process, requires high-quality thermal heat (>450 °C), and exhibits high waste heat rejection. The integrated VMD-SCWD approach is approximately 50% more energy-efficient and 35% more cost-efficient than the standalone SCWD system. Compared to the commercially used brine concentrator and crystallizer, the multi-stage VMD-SCWD system is more energy efficient for feed concentrations >5%. VMD-SCWD system is 30% cheaper, due to less expensive membrane distillation modules compared to a brine concentrator. The proposed design concept can replace the brine concentrator and crystallizer as an improved alternative for a zero-liquid discharge desalination system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Overview and technology opportunities for thermochemically-produced bio-blendstocks

Global demand for transportation fuels is projected to increase 40% by 2040, and biomass-derived fuels (biofuels) play a crucial role in substituting fossil fuels and mitigating greenhouse gas emissions. Currently, biofuels are mainly consumed as blendstocks combined with petroleum-based fuels, and effective conversion technologies can address the quality challenges for offering standalone biofuels. Thermochemical conversion process is one of the most promising pathways among existing technologies for biofuel production. However, the major barriers are unwanted characteristics (e.g., thermal instability) of intermediate products, such as bio-oil, and required upgrading treatments for producing compatible fuels. Here, this study highlights the merits and critical challenges of thermochemical conversion and physicochemical upgrading technologies for bio-blendstock production from lignocellulosic biomass. The novelty of this study lies in potential directions for future research through both critical and systematic literature reviews, and the proposed intensified process for lignocellulosic-based fuel blendstocks production. It is concluded that recovery and fractionation strategies (e.g., quenching and stripping) can maximize process yields and add values in the efficient conversion pathways. Effective quenching can stop secondary free radical reactions and improve liquid yields over gas and solid yields. Stripping process can improve process yield, catalyst lifespan, and thermal stability. It is further concluded that physicochemical treatments are not as effective as thermochemical treatments, but have advantages of mild operating conditions and potential for integrated solutions in conjunction with other treatments.

09 BIOMASS FUELS↗
DOE OSTI2025

PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein Data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. Data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (pdb-ihm.org), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology.

IHMCIF↗
DOE OSTI2024

A few-degree calorimeter for the future electron-ion collider

In this study, measuring the region 0.1 < Q 2 < 1.0 GeV 2 is essential to support searches for gluon saturation at the future Electron-Ion Collider. Recent studies have revealed that covering this region at the highest beam energies is not feasible with current detector designs, resulting in the so-called Q 2 gap. In this work, we present a design for the Few-Degree Calorimeter (FDC), which addresses this issue. The FDC uses SiPM-on-tile technology with tungsten absorber and covers the range of -4.6 < η < -3.6. It offers fine transverse and longitudinal granularity, along with excellent time resolution, enabling standalone electron tagging. Our design represents the first concrete solution to bridge the Q 2 gap at the EIC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A virtual Frisch-grid geometry-based CZT gamma detector for in-field radioisotope identification

Here, we present a Virtual Frisch-Grid geometry-based CZT gamma detector developed for identifying different radioisotopes over an energy range from a few keV up to 2 MeV, and useful for efficient characterization of CZT crystals. The detector is built with a 3 x 3 matrix of CZT crystals, each measuring approximately 6 mm x 6 mm x 15 mm. The charge generated within the sensor’s active volume is read out via an anode connected directly to the AVG3_Dev integrated circuit. A current signal induced by charge drift is collected on side pads of the crystals, enabling reconstruction of a 3D interaction position. This paper discusses the design, development, and performance of the standalone, mobile detector system, which integrates the AVG3_Dev readout IC developed at Brookhaven National Laboratory, a high-speed FPGA-based with per-channel digital signal processing, and embedded system capabilities. The device is compact, battery-powered, and supports wireless data streaming, making it suitable for field operations for radioisotope identification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Sensitivity analysis of thermal contact conductance modeling to inform MiniFuel irradiation capsule designs

The MiniFuel irradiation platform has been developed by Oak Ridge National Laboratory as a flexible, high-throughput separate effects testing capability within the High Flux Isotope Reactor (HFIR). Finite element thermal models are relied upon to design MiniFuel experiments to achieve a specific time-averaged irradiation temperature for experimental objectives. A previous study identified that uncertainty in the component heat generation rates and thermal contact conductance (TCC) model are the most significant contributors to predicted fuel temperature variance. To address both sources of uncertainty, this work performs sensitivity analysis on the TCC model to identify high-impact, high-uncertainty parameters that contribute to fuel temperature variance. The TCC model is analyzed in increasing detail, first using a standalone Python code, then again after coupling Python to the BISON fuel performance code. Furthermore, the parameters with the largest contributions to fuel temperature variance which can be reduced through design changes are identified as the initial subcapsule gas pressure, contact pressure between the fuel and dish, and the effective surface roughness of the interface. A set of design recommendations for future capsule designs has been established and applied to reduce the previously quantified average fuel temperature uncertainty ranges of ± 40 °C in the HFIR vertical experiment facilities (VXF) and ± 80 °C in the removable beryllium (RB) reflector to approximately ± 32 °C and ± 53 °C, respectively. This equates to a 21 % and 33 % reduction in the uncertainty range of the average fuel temperature for VXF and RB, respectively.

BISON↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Closed-loop pressure retarded osmosis draw solutions and their regeneration processes: A review

Pressure-Retarded Osmosis (PRO) is an osmotic process that has been used to harvest energy from salinity gradients using a semi permeable membrane. A comparison between open-loop PRO (OLPRO) and closed-loop PRO (CLPRO) was made regarding their performance and costs. In CLPRO, where the diluted draw solution is re-concentrated in the regeneration system to be reutilized in the process, has recently received an intensive focus as the most viable configuration for a standalone power plant. The choice of the PRO draw solution in CLPRO is crucial to garner a high osmotic pressure as the key for the feasibility of the process. Here, in this review, the draw solutions are critically evaluated in the literature in terms of energy output as well as the method of regeneration used to recirculate them. A set of practical criteria has been suggested to appraise the adequacy of the solution for CLPRO application. It was concluded that NH 3 – CO 2 theoretically can produce 170 W/m 2 of power density. Inorganic draw solutes such as NaCl can generate high power density up to 87 W/m 2 . Organic draw solutes with their remarkably low reverse salt flux (RSF) have promising potential for future application in PRO. Similarly, the regeneration systems of the diluted draw solutions have also been reviewed and discussed. How the energy consumption of the regeneration process affects the feasibility of CLPRO is explained. For the specific case of osmotic heat engines (OHEs), when the energy of the regeneration process is supplied by heat waste, the range of applicability of the heat waste in CLPRO in terms of efficiency is defined and compared to Organic Rankine Cycle (ORC). The results showed that CLPRO has better efficiency than ORC for temperatures T < 80 °C, which makes it a promising process or low-grade heat energy recovery. In addition, a PRO-RO hybrid system coupled with solar power can reduce the net specific energy consumption (SEC) to 0.39 kWh/m 3 . The conditions that regeneration processes should operate under to make PRO viable are discussed in the last section. Overall, the study indicates the key factors for optimizing the performance of CLPRO process.

42 ENGINEERING↗
DOE OSTI2025

Marine energy supported multi-energy system planning and operation optimization for sustainable coastal community

The growing need for sustainable energy solutions in coastal areas necessitates the development of integrated systems that leverage abundant marine resources. In this study, a standalone Marine Energy Supported Multi-Energy System (MRE-MES) is designed for sustainable coastal community development, utilizing renewable marine resources, including offshore wind, wave, and solar energy, to address the energy needs of electricity, heat, freshwater, and hydrogen. The proposed MRE-MES incorporates a co-optimization model that simultaneously balances capacity planning and operational efficiency to minimize costs and environmental impacts. The system is tested under different renewable energy penetration levels and demand uncertainties, using a two-stage stochastic programming to account for variability in renewable resources and consumption needs. The experimental results indicate that in the optimal system capacity configuration, the percentage of total renewable energy generation is around 80 %, with or without capacity limitation constraints on PV, water tank, and hydrogen storage. Compared to the worst-case scenario in Monte Carlo experiments, two-stage stochastic optimization results in a more robust decision that effectively mitigates the risks posed by future uncertain demand conditions. In conclusion, the findings highlight the viability of marine energy for providing a resilient, comprehensive energy solution to coastal communities.

Capacity planning↗
DOE OSTI2023

The MOOSE electromagnetics module

The Multiphysics Object-Oriented Simulation Environment (MOOSE) electromagnetics module has been developed to increase MOOSE physics module capabilities, enabling standalone and coupled computational electromagnetics within the MOOSE multiphysics ecosystem. The module is actively being utilized in the areas of plasma physics and advanced manufacturing, and it currently provides initial demonstrated capability in multi-dimensional, complex-valued electromagnetic wave propagation, electrostatic contact, reflection and transmission, and electromagnetic eigenvalue problems. Two-dimensional wave propagation and one-dimensional wave reflection and transmission are showcased as examples in this work. The modularity, parallelism, and plug-in infrastructure for custom future development is inherited from MOOSE itself, and the module can be used with both MOOSE-based and external codes, giving great flexibility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Pressurized in situ X-ray diffraction insights into super/subcritical carbonation reaction pathways of steelmaking slags and constituent silicate minerals

This study explores mineral carbonation of industrial stainless steelmaking slags and relevant synthetic constituent minerals via in situ pressurized X-ray diffraction, to clarify carbonation reaction pathways and efficiency for carbon storage and waste valorization. The primary mineral phases of Argon Oxygen Decarburization (AOD) and Continuous Casting (CC) slags, namely, åkermanite, bredigite, cuspidine, merwinite, and β- and γ-C 2 S, were reacted in a custom-built beryllium-capped XRD reactor filled with either water-saturated (wet) subcritical CO 2 (g), or wet supercritical CO 2(SC) , in a series of carbonation experiments. Formation of calcite and aragonite was observed for most Ca-bearing minerals, transient precipitation of metastable vaterite was observed, while carbonation of AOD and CC slags in CO 2(SC) resulted in hydrated crystalline calcium carbonates and indirect evidence of amorphous carbonate. Overall, results obtained suggest that low-pressure subcritical carbonation routes are attractive for standalone slag carbonation processes, while high-pressure supercritical carbonation routes are amiable to symbiotic integration with CO 2(SC) -generating green technologies.

42 ENGINEERING↗
DOE OSTI2021

LaNi x Fe 1– x O 3–δ as a Robust Redox Catalyst for CO 2 Splitting and Methane Partial Oxidation

The current study reports LaNi 0.5 Fe 0.5 O 3–δ as a robust redox catalyst for CO 2 splitting and methane partial oxidation at relatively low temperatures (~700 °C) in the context of a hybrid redox process. Specifically, perovskite-structured LaNi x Fe 1–x O 3–δ (LNFs) with nine different compositions (x = 0.05–0.5) were prepared and investigated. Among the samples evaluated, LaNi 0.4 Fe 0.6 O 3–δ and LaNi 0.5 Fe 0.5 O 3–δ showed superior redox performance, with ~90% CO 2 and methane conversions and >90% syngas selectivity. The standalone LNFs also demonstrated performance comparable to that of LNF promoted by mixed conductive Ce 0.85 Gd 0.1 Cu 0.05 O 2–δ (CGCO). Long-term testing of LaNi 0.5 Fe 0.5 O 3–δ indicated that the redox catalyst gradually loses its activity over repeated redox cycles, amounting to approximately 0.02% activity loss each cycle, averaged over 500 cycles. This gradual deactivation was found to be reversible by deep oxidation with air. Further characterizations indicated that the loss of activity resulted from a slow accumulation of iron carbide (Fe 3 C and Fe 5 C 2 ) phases, which cannot be effectively removed during the CO 2 splitting step. Reoxidation with air removed the carbide phases, increased the availability of Fe for the redox reactions via solid-state reactions with La 2 O 3 , and decreased the average crystallite size of La 2 O 3 . As a result, reactivating the redox catalyst periodically, e.g., once every 40 cycles, was shown to be highly effective, as confirmed by operating the redox catalyst over 900 cumulative cycles while maintaining satisfactory redox performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Cost and Carbon Intensity Implications of Coprocessing Sustainable Aviation Fuel at Petroleum Refineries

Sustainable aviation fuel (SAF) will play a critical role in decarbonizing the aviation industry. Among SAF production pathways, alcohol-to-jet (ATJ) stands out for its scalability, supported by abundant feedstock availability and a well-established bioethanol industry. However, significant reductions in SAF carbon intensity (CI) require the use of future feedstocks (e.g., cellulosic) whose adoption is hindered by high capital costs for feedstock processing and ethanol upgrading. Here, we evaluate the financial viability and environmental implications of integrating an ATJ SAF biorefinery within a petroleum refinery, utilizing miscanthus and switchgrass as example feedstocks. Three scenarios are evaluated: standalone (benchmark), colocated, and repurposing (coprocessing SAF within the petroleum refinery). Results show repurposing reduces baseline capital costs by 36% and SAF minimum selling price (MSP) by 12% to 8.14 USD·gal −1 ; the superior performance of repurposing is consistent across both feedstocks. Integration has a limited effect on SAF CI, which remains stable across scenarios, whereas using cellulosic feedstocks reduces CI by over 70% relative to corn, with baseline values of 17.01 g CO 2 e· MJ −1 for miscanthus and 12.23 g CO 2 e·MJ −1 for switchgrass. Global sensitivity analysis reveals MSP declines with greater coprocessing levels.

09 BIOMASS FUELS↗
DOE OSTI2021

Pretreatment of Biomass by Selected Type-III Deep Eutectic Solvents and Evaluation of the Pretreatment Effects on Hydrothermal Carbonization

Hydrothermal carbonization (HTC) is a novel thermochemical conversion that converts wet biomass into energy dense solid fuel. Residual moisture under subcritical conditions reacts with a lignin-cellulose-hemicellulose matrix with the major reactions being identified as dehydration and decarboxylation. Among other reaction parameters (e.g., temperature, time, pressure), biomass morphology often plays a key role in HTC. The hypothesis of this study was enhancing the porous structure of biomass without significantly affecting biopolymer composition would augment hydrothermal carbonization (HTC). To prove the hypothesis, two type-III deep eutectic solvents (DESs), namely choline chloride:urea (ChCl:Urea, 1:2 mol/mol) and methyltriphenylphosphonium bromide:ethylene glycol (MTPB:EG, 1:4 mol/mol), were studied to pretreat loblolly pine at room temperature and ambient pressure for 1 h. DES pretreatment shows swelling of the biomass, increasing the surface fiber-to-fiber gap length by 52% and 185% for ChCl:Urea and MPTB:EG pretreatments, respectively. The total pore volume remained intact (2.6 × 10 –3 cm 3 /g), although new small pores evolved, and existing pores were abated with DES pretreatment. Hydrochars prepared from DES pretreated loblolly pine showed a high O/C and H/C ratio resulting in a significant increase of energy content (up to 42%) and a decrease of mass yield (up to 50 wt %), indicating an enhancement of HTC severity due to the alteration of surface morphology by DES. Finally, a preliminary process economics revealed that integrated DES pretreatment-HTC would increase fixed capital investment but decrease the cost of operation and manufacturing than the standalone HTC process.

09 BIOMASS FUELS↗
DOE OSTI2023

Optimization Stability in Excited-State-Specific Variational Monte Carlo

Here, we investigate the issue of optimization stability in variance-based state-specific variational Monte Carlo, discussing the roles of the objective function, the complexity of wave function ansatz, the amount of sampling effort, and the choice of minimization algorithm. Using a small cyanine dye molecule as a test case, we systematically perform minimizations using variants of the linear method as both a standalone algorithm and in a hybrid combination with accelerated descent. We demonstrate that adaptive step control is crucial for maintaining the linear method's stability when optimizing complicated wave functions and that the hybrid method enjoys both greater stability and minimization performance.

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

A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials

Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, in this study, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.

Kim, Dongjin [University of California, Berkeley, ↗