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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Characterizing Complex Gas–Solid Interfaces with in Situ Spectroscopy: Oxygen Adsorption Behavior on Fe–N–C Catalysts

Electrocatalysts for the oxygen reduction reaction within polymer electrolyte membrane fuel cells based on iron, nitrogen, and carbon elements (Fe–N–C) are receiving significant research attention as they offer an inexpensive alternative to catalysts based on platinum-group metals. Although both the performance and the fundamental understanding of Fe–N–C catalysts have improved over the past decade, there remains a need to differentiate the relative activity of different active sites. Toward this goal, our study is focused on characterizing the interactions between O 2 and a set of five structurally different Fe–N–C materials. Detailed characterization of the Fe speciation was performed with 57 Fe Mössbauer spectroscopy and soft X-ray absorption spectroscopy of the Fe L 3,2 -edge, whereas nitrogen chemical states were investigated with X-ray photoelectron spectroscopy (XPS). In addition to initial sXAS and XPS measurements performed in ultra-high vacuum (UHV), measurements were also performed (at the identical location) in an atmosphere of 100 mTorr of O 2 at 80 °C (O 2 -rich). XPS and sXAS results reveal the presence of several types of FeNxCy adsorption sites. FeNxCy sites that are proposed as the most active ones do not show significant change (based on the techniques used in this study) when their environment is changed from UHV to O 2 -rich. Correlation with Mössbauer and sXAS results suggests that this is most likely due to the persistence of strongly adsorbed O 2 molecules from their previous exposure to air. However, other species do show spectroscopic changes from UHV conditions to O 2 -rich. This implies that these sites have a weaker interaction with O 2 that results in their desorption in vacuum conditions and re-adsorption when exposed to the O 2 -rich environment. The nature of these weakly and strongly O 2 -adsorbing FeN x C y sites is discussed in the context of different synthetic and processing parameters employed to fabricate each of these five Fe–N–C materials.

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

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. Here, the framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗
DOE OSTI2026

Comparing Synthetic Routes and Thermal Characteristics of Alternate APO Variants

Low density, high-strength, temperature-resistant foams see widespread application in the aerospace and weapons fields due to their excellent qualities as structural supports. In these demanding environments, the most common formulation is a three-phase syntactic foam containing APOCure-601, BMI, and carbon or glass microballoons. Of these, APOCure-601 and BMI form the polymer resin amino-poly(oxadiazole) bismaleimide (APO-BMI), also known as Legacy APO or S-1,2-Ethyl-APO. In manufacturing this foam, the selective laser sintering (SLS) additive manufacturing technique is quickly gaining prominence, over more traditional injection molding since SLS allows for 3D printing of materials and reduces overall cost and waste production. That said, SLS also requires a narrow window between the melt and cure temperatures for a successful print. SLS printing of APO-BMI is therefore difficult since the compound possesses a broad window between its melting and curing temperatures, and also requires several post-cure steps or complete polymerization. This work explores synthesis optimization and thermal characteristics for variant Apo-BMI structures by observing the effect that alternative heteroatoms in the APO linkages, geometries 0f the BMI groups, and bridge structure identities impart on the resultant material. Synthetic methods for these altered structures were established in batch, with some others further converted to continuous flow chemistry, a method that produces materials in a continuous stream and is highly reproducible and readily scaled. Additionally, each structural change significantly altered the melt and cure properties for each APO variant, which is advantageous for SLS manufacturing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Investigating and Optimizing the Lysate-Based Expression of Nonribosomal Peptide Synthetases Using a Reporter System

Lysate-based cell-free expression (CFE) systems are accessible platforms for expressing proteins that are difficult to synthesize in vivo, such as nonribosomal peptide synthetases (NRPSs). NRPSs are large (>100 kDa), modular enzyme complexes that synthesize bioactive peptide natural products. This synthetic process is analogous to transcription/translation (TX/TL) in lysates, resulting in potential resource competition between NRPS expression and NRPS activity in cell-free environments. Moreover, CFE conditions depend on the size and structure of the protein. Here, a reporter system for rapidly investigating and optimizing reaction environments for NRPS CFE is described. This strategy is demonstrated in E. coli lysate reactions using blue pigment synthetase A (BpsA), a model NRPS, carrying a C-terminal tetracysteine (TC) tag which forms a fluorescent complex with the biarsenical dye, FlAsH. A colorimetric assay was adapted for lysate reactions to detect the blue pigment product, indigoidine, of cell-free expressed BpsA-TC, confirming that the tagged enzyme is catalytically active. An optimized protocol for end point TC/FlAsH complex measurements in reactions enables quick comparisons of full-length BpsA-TC expressed under different reaction conditions, defining unique requirements for NRPS expression that are related to the protein’s catalytic activity and size. Importantly, these protein-dependent CFE conditions enable higher indigoidine titer and improve the expression of other monomodular NRPSs. Notably, these conditions differ from those used for the expression of superfolder GFP (sfGFP), a common reporter for optimizing lysate-based CFE systems, indicating the necessity for tailored reporters to optimize expression for specific enzyme classes. In conclusion, the reporter system is anticipated to advance lysate-based CFE systems for complex enzyme synthesis, enabling natural product discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Scalable Generation of High-fidelity Synthetic Population Ensembles

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the U.S. via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. The study involves two scenarios: creating ensembles for (1) 17 U.S. metropolitan areas in 2019 and (2) full U.S. Census Divisions in 2023, with each scenario consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system within a research cloud, comprised of virtual containerizations, GPU-enhanced functionality, and orchestrated deployments of UrbanPop’s maturing Likeness Python ecosystem. Results demonstrate we maintained high-fidelity approximations of residential totals by areas of interest and the demographic characteristics of neighborhoods while reducing manual workflow burdens. Finally, we discuss plans to fine-tune and further develop our automated workflows for truly distributed job orchestration to increase computational efficiency, as well as provide an outlook for broadening applications of the ensembles.

Cluster computing↗
DOE OSTI2021

Rattlesnake User's Manual

Rattlesnake is a combined-environments, multiple input/multiple output control system for dynamic excitation of structures under test. It provides capabilities to control multiple responses on the part using multiple exciters using various control strategies. Rattlesnake is written in the Python programming language to facilitate multiple input/multiple output vibration research by allowing users to prescribe custom control laws to the controller. Rattlesnake can target multiple hardware devices, or even perform synthetic control to simulate a test virtually. Rattlesnake has been used to execute control problems with up to 200 response channels and 12 drives. This document describes the functionality, architecture, and usage of the Rattlesnake controller to perform combined environments testing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Toward a glycyl radical enzyme containing synthetic bacterial microcompartment to produce pyruvate from formate and acetate

Formate has great potential to function as a feedstock for biorefineries because it can be sustainably produced by a variety of processes that don’t compete with agricultural production. However, naturally formatotrophic organisms are unsuitable for large-scale cultivation, difficult to engineer, or have inefficient native formate assimilation pathways. Thus, metabolic engineering needs to be developed for model industrial organisms to enable efficient formatotrophic growth. Here, we build a prototype synthetic formate utilizing bacterial microcompartment (sFUT) encapsulating the oxygen-sensitive glycyl radical enzyme pyruvate formate lyase and a phosphate acyltransferase to convert formate and acetyl-phosphate into the central biosynthetic intermediate pyruvate. This metabolic module offers a defined environment with a private cofactor coenzyme A that can cycle efficiently between the encapsulated enzymes. To facilitate initial design-build-test-refine cycles to construct an active metabolic core, we used a “wiffleball” architecture, defined as an icosahedral bacterial microcompartment (BMC) shell with unoccupied pentameric vertices to freely permit substrate and product exchange. The resulting sFUT prototype wiffleball is an active multi enzyme synthetic BMC functioning as platform technology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Rattlesnake User's Manual (V.3)

Rattlesnake is a combined-environments, multiple input/multiple output control system for dynamic excitation of structures under test. It provides capabilities to control multiple responses on the part using multiple exciters using various control strategies. Rattlesnake is written in the Python programming language to facilitate multiple input/multiple output vibration research by allowing users to prescribe custom control laws to the controller. Rattlesnake can target multiple hardware devices, or even perform synthetic control to simulate a test virtually. Rattlesnake has been used to execute control problems with up to 200 response channels and 24 shaker drives. This document describes the functionality, architecture, and usage of the Rattlesnake controller to perform combined environments testing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Atomically dispersed metal–nitrogen–carbon catalysts for fuel cells: advances in catalyst design, electrode performance, and durability improvement

The urgent need to address the high-cost issue of proton-exchange membrane fuel cell (PEMFC) technologies, particularly for transportation applications, drives the development of simultaneously highly active and durable platinum group metal-free (PGM-free) catalysts and electrodes. The past decade has witnessed remarkable progress in exploring PGM-free cathode catalysts for the oxygen reduction reaction (ORR) to overcome sluggish kinetics and catalyst instability in acids. Among others, scientists have identified the newly emerging atomically dispersed transition metal (M: Fe, Co, or/and Mn) and nitrogen co-doped carbon (M–N–C) catalysts as the most promising alternative to PGM catalysts. Here, we provide a comprehensive review of significant breakthroughs, remaining challenges, and perspectives regarding the M–N–C catalysts in terms of catalyst activity, stability, and membrane electrode assembly (MEA) performance. A variety of novel synthetic strategies demonstrated effectiveness in improving intrinsic activity, increasing active site density, and attaining optimal porous structures of catalysts. Rationally designing and engineering the coordination environment of single metal MN x sites and their local structures are crucial for enhancing intrinsic activity. Increasing the site density relies on the innovative strategies of restricting the migration and agglomeration of single metal sites into metallic clusters. Relevant understandings provide the correlations among the nature of active sites, nanostructures, and catalytic activity of M–N–C catalysts at the atomic scale through a combination of experimentation and theory. Current knowledge of the transferring catalytic properties of M–N–C catalysts to MEA performance is limited. Rationally designing morphologic features of M–N–C catalysts play a vital role in boosting electrode performance through exposing more accessible active sites, realizing uniform ionomer distribution, and facilitating mass/proton transports. We outline future research directions concerning the comprehensive evaluation of M–N–C catalysts in MEAs. The most considerable challenge of current M–N–C catalysts is the unsatisfied stability and rapid performance degradation in MEAs. Therefore, we further discuss practical methods and strategies to mitigate catalyst and electrode degradation, which is fundamentally essential to make M–N–C catalysts viable in PEMFC technologies.

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

Path-Integrated X-Ray Digital Image Correlation using Synthetic Reference Images

X-rays can provide images when an object is visibly obstructed, allowing for motion measurements via x-ray digital image correlation (DIC). However, x-ray images are path-integrated and contain data for all objects between the source and detector. If multiple objects are present in the x-ray path, conventional DIC algorithms may fail to correlate the x-ray images. A new DIC algorithm called path-integrated (PI)-DIC addresses this issue by reformulating the matching criterion for DIC to account for multiple, independently-moving objects. PI-DIC requires a set of reference x-ray images of each independent object. However, due to experimental constraints, such reference images might not be obtainable from the experiment. Here, this work focuses on the reliability of synthetically-generated reference images, in such cases. A simplified exemplar is used for demonstration purposes, consisting of two aluminum plates with tantalum x-ray DIC patterns undergoing independent rigid translations. Synthetic reference images based on the “as-designed” DIC patterns were generated. However, PI-DIC with the synthetic images suffered some biases due to manufacturing defects of the patterns. A systematic study of seven identified defect types found that an incorrect feature diameter was the most influential defect. Synthetic images were re-generated with the corrected feature diameter, and PI-DIC errors were improved by a factor of 3-4. Final biases ranged from 0.00-0.04 px, and standard uncertainties ranged from 0.06-0.11 px. In conclusion, PI-DIC accurately measured the independent displacement of two plates from a single series of path-integrated x-ray images using synthetically-generated reference images, and the methods and conclusions derived here can be extended to more generalized cases involving stereo PI-DIC for arbitrary specimen geometry and motion. This work thus extends the application space of x-ray imaging for full-field DIC measurements of multiple surfaces or objects in extreme environments where optical DIC is not possible.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

N -Terminal Octylated Peptoid Hydrogels as 3D-Printable Cell Scaffolds and Proteolytically Robust Cargo Depots

Supramolecular hydrogels that mimic the extracellular matrix (ECM) represent promising materials for tissue engineering and drug delivery. However, conventional hydrogels formed via the self-assembly of natural or synthetic building blocks often face a trade-off between biological functionality and biochemical stability, limiting their utility in long-term or protease-rich environments. Peptoids, a class of peptide-inspired, sequence-defined polymers, offer a compelling alternative due to their exceptional proteolytic resistance and bioactivity. Despite this potential, the development of supramolecular peptoid hydrogels has been hindered by the absence of backbone hydrogen bond donors, which limits long-range ordering necessary for efficient hydrogel formation. This work describes a short peptoid functionalized at the N-terminus with an octyl chain that readily self-assembles into hydrogels. Hydrophobic interactions among pendant octyl groups promote directional peptoid packing into highly ordered nanosheets, which interconnect to form a porous hydrogel network. These hydrogels exhibit tunable viscoelasticity, shear-thinning, and self-healing properties, enabling their use as inks for extrusion-based 3D printing. They support NIH-3T3 fibroblast adhesion, spreading, and proliferation, maintaining greater than 95% cell viability over 4 days. Moreover, the hydrogels retain their macroscopic integrity under protease-rich conditions, enabling sustained cargo release and uniform cellular uptake. Together, this study demonstrates a class of supramolecular peptoid hydrogelators that integrate biocompatibility, 3D printability, and proteolytic stability, providing a versatile platform for ECMmimetic scaffolds in regenerative medicine and long-term therapeutic delivery.

cargo delivery↗
DOE OSTI2024

Electrochemical Reactivity of Hydrogen Storage Materials: Exploring Borohydride and Hydrazinium Salt Mixtures

Electrochemical characterization of hydrogen storage materials was conducted in a non-aqueous environment to investigate the direct electrochemical release and consumption of hydrogen and the potential for regeneration. We first address the challenge of minimal solubility of the synthetic precursors, sodium borohydride (NaBH 4 ) and hydrazinium bromide (N 2 H 5 Br), in both organic and inorganic solvents. We next determine and calibrate a reference electrode formulation compatible with our non-aqueous media and analytes that demonstrates a stable reference potential. We employ cyclic voltammetry (CV) to characterize the precursors and mixtures thereof. Each CV peak is assigned to a corresponding electrochemical reaction. Using the rate-dependent CV method and Randles–Ševčík equation, we calculate the diffusion coefficient of each chemical (NaBH 4 and N 2 H 5 Br). Analysis of the CVs, coupled with 11B NMR analysis, reveals a room temperature chemical transformation of NaBH 4 and N 2 H 5 Br mixtures into hydrazine borane (N 2 H 4 BH 3 ). These results are particularly significant, considering the limited information available on the electrochemical characterization of metal borohydride and hydrazinium salt in non-aqueous media. This work establishes a foundation for adapting a non-aqueous electrochemical system to further study the borohydride family of chemistries and to design and develop electrochemical devices for direct electrical and chemical energy interconversion with hydrogen storage materials.

08 HYDROGEN↗
DOE OSTI2026

Modeling The Nucleosynthetic Imprint of Stellar Merger Phenomena - Final Technical Report

This DOE CAREER project developed a comprehensive, multi-physics framework for modeling stellar mergers and their observable consequences. The work successfully integrated analytical models, three-dimensional hydrodynamic simulations, stellar evolution calculations, nucleosynthesis, and radiation transport into a unified pipeline. Key scientific advances include demonstrating stellar mergers as a unifying explanation for systems such as Betelgeuse and R Coronae Borealis stars, quantifying merger-driven chemical signatures, and establishing the connection between merger physics and circumstellar environments. A major outcome of the project is the development of SuperLite, an open-source Monte Carlo radiation transport code that enables the generation of synthetic spectra for astrophysical transients. This work bridges dynamical, thermal, and radiative timescales and provides a powerful, DOE-relevant computational framework for interpreting observations of supernovae and related transient phenomena, while also contributing to workforce development through the training of postdoctoral researchers and graduate students.

Chatzopoulos, Emmanouil [Louisiana State Universit↗
DOE OSTI2025

Current and candidate additives for environmentally acceptable lubricants—A review

The world consumes approximately 40 million tons of lubricants annually, and nearly half of them end up in the environment because of leaks, discharges, and evaporation, causing significant environmental and economic impacts. The need for moving toward environmentally acceptable lubricants (EALs) is increasingly being recognized. Synthetic ester, polyalkylene glycol, vegetable oil, and water are among the base fluids approved by the U.S. Environmental Protection Agency for EALs. However, neat base fluids cannot fully meet the lubrication requirements without incorporating functional additives such as anti-wear, antioxidant, corrosion inhibitor, and viscosity modifiers. Both the performance and environmental compatibility of lubricant additives are critical. In this article, current and candidate EAL additives, both in liquid and solid forms, are reviewed, with a focus on their functionalities in friction and wear reduction, toxicity, and biodegradability. Finally, a perspective for future research on EAL additives is discussed.

42 ENGINEERING↗