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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Design and simulation of a muon detector to characterize geological overburden

This study presents the design, construction, and simulation of a mobile muon detector tailored for geological overburden characterization. The detector employs plastic scintillator paddles with silicon photomultipliers (SiPMs) and a QuarkNet data acquisition system, offering a portable solution suitable for remote field deployment. The simulator’s modular aluminum frame allows for adjustable geometry and directional sensitivity, while its battery system supports over a week of autonomous operation. Preliminary experimental tests confirmed that its muon flux measurements were consistent with theoretical expectations. A comprehensive simulation framework using Geant4 and CORSIKA was developed to model detector response and overburden effects. Analytical and Monte Carlo methods were used to assess quadrant resolution and infer muon directionality. This work lays the foundation for future overburden mapping and supports the development of reconstruction algorithms for geological applications.

72 - PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Predicting Si-Anode Calendar Life Using Machine Learning: Correlating Electrolyte Properties and Electrochemical Signals

This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.

25 - ENERGY STORAGE↗
DOE OSTI2026

Failure Mode and Effects Analysis (FMEA) for Photovoltaic Inverter

Photovoltaic (PV) inverters are critical yet vulnerable components in modern energy systems, often acting as reliability bottlenecks that increase the levelized cost of energy (LCOE). To address this, this paper presents a comprehensive Failure Mode and Effects Analysis (FMEA) tailored for PV inverters. Leveraging field data and literature, we identify failure-prone components, such as capacitors,, and relays, and prioritize their risks based on quantitative Risk Priority Numbers (RPNs). The analysis reveals that surge-induced MOV short circuits, capacitor degradation, and environmental cooling fan failures dominate the risk profile. These findings provide a targeted framework for reliability improvement, guiding future efforts in predictive diagnostics, design optimization, and accelerated life testing strategies.

14 SOLAR ENERGY↗
DOE OSTI2025

Fully Homomorphic Encryption

This code implements a Fully Homomorphic Encryption (FHE) system, enabling secure computation on encrypted data without requiring decryption. It supports encryption, decryption, and homomorphic operations like matrix multiplication and addition. This code is adaptable for integrating FHE into linear-time invariant (LTI) systems, including digital control and filtering. With proper configuration from subject matter expertise, encrypted system parameters and signals can be manipulated to perform tasks like state updates, output calculations, and convolution in the encrypted domain. By preserving the structure of LTI systems while ensuring privacy, the framework facilitates secure applications in areas such as autonomous systems, signal processing, and industrial automation. The code initializes the encryption system using parameters provided in the env dictionary. These parameters include the ciphertext modulus, key dimension, plaintext fixed-point scaling factor, and noise bound. During initialization, a secret key is generated, which is essential for encrypting and decrypting data securely. The modular design allows users to tailor these parameters to specific use cases or security requirements. The code implements multiple cryptographic schemes. The learning with errors (LWE) encryption method encodes cleartext message to their plaintext fixed-point representation then encrypted into ciphertext space with additive noise. This noise ensures the security of the scheme, relying on the computational hardness of the LWE problem. The code also includes the Gentry-Sahai-Waters (GSW) scheme based off the LWE problem. Homomorphic matrix multiplication is performed between the LWE and GSW to encrypted data. This is achieved using a decomposition function on the LWE ciphertext during the multiplication operation. For higher-dimensional data, the code includes a method to encrypt entire matrices (GSWMat) using GSW encryption. These encrypted matrices can then be used for homomorphic matrix multiplications (MatMult). The decryption function uses the secret key to recover the original plaintext, removing the added noise and scaling that was originally applied during encryption.

Lois, Roberts [Idaho National Laboratory (INL), Id↗
DOE OSTI2024

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Effect of Methyl Groups on Formation of Ordered or Layered Graphitic Materials from Aromatic Molecules

Abstract Developing functionally complex carbon materials from small aromatic molecules requires an understanding of how the chemistry and structure of its constituent molecules evolve and crosslink, to achieve a tailorable set of functional properties. Here, molecular dynamics (MD) simulations are used to isolate the effect of methyl groups on condensation reactions during the oxidative process and evaluate the impact on elastic modulus by considering three monodisperse pyrene‐based systems with increasing methyl group fraction. A parameter to quantify the reaction progression is designed by computing the number of new covalent bonds formed. Utilizing the previously developed MD framework, it is found that increasing methylation leads to an almost doubling of bond formation, a larger fraction of the new bonds oriented in the direction of tensile stress, and a higher basal plane alignment of the precursor molecules along the direction of tensile stress, resulting in enhanced tensile modulus. Additionally, via experiments, it is demonstrated that precursors with a higher fraction of methyl groups result in a higher alignment of molecules. Moreover, increased methylation results in the lower spread of single molecule alignment which may lead to smaller variations in tensile modulus and more consistent properties in carbon materials derived from methyl‐rich precursors.

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

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2024

PV-BESS DC-Series Integration for Regulated DC Systems

DC-series integration introduces a novel approach to seamlessly integrate a solar photovoltaic (PV) array and a battery energy storage (BES) in series. This system, referred to as the PV-integrated battery energy storage system—dc series (PVBESS-DCS), simplifies integration and enhances power density by leveraging the inherent voltage-source characteristics of batteries and adopting the concept of partial power processing. However, addressing voltage variations of a PV array and a BES under different operational conditions remains a challenge to apply the PVBESS-DCS to a regulated dc system, where the dc-bus voltage is fixed. This article proposes a PVBESS-DCS solution tailored for regulated dc systems. Within this framework, we present a controllable series voltage source compensating for the voltage differences between the PV and BES sources and the dc bus. Thus, the system can perform PV maximum power point tracking and curtailment control while charging and discharging BES and connected to a regulated dc bus. Additionally, we present a single-stage multiport partial power processing dc–dc converter designed for the proposed PVBESS-DCS. It utilizes a triple active bridge dc–dc converter topology. Finally, this article presents operating principles, control strategies, simulation results, and experimental findings of the proposed PVBESS-DCS for regulated dc systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Revealing and Engineering Assembly Pathways of 3D DNA Origami Crystals

Recent developments in nanomaterial self-assembly demonstrate the capability to create tailored nanostructures by engineering both the binding coordination and specificity of interactions between material subunits. DNA origami frames allow for the design and fabrication of a broad variety of ordered 3D nanoscale architectures through self-assembly, facilitated by frame-to-frame bonds with designable strength and specificity. While the bond design is critical to lattice formation, the assembly process itself is often dependent on a thermal pathway. Highly ordered nanoscale frameworks, assembled from DNA frames, are predominantly crystallized through thermal annealing pathways that typically follow a “slow” cooling approach, with experiments on the time scale of days yielding DNA origami crystals in the range of 1−10 μm. This extended assembly time scale hinders the study of crystal formation pathways, necessitating a deeper understanding of factors governing successful annealing. Lack of insight into time scale also presents a practical limitation for material fabrication. Here, we investigate key factors affecting lattice assembly pathways and demonstrate that precise engineering of assembly conditions greatly reduces assembly times by up to nearly 2 orders of magnitude. We evaluate the nucleation and growth of crystals via optical and electron microscopy, and small-angle X-ray scattering techniques, mapping the time−temperature-transformation of superlattices from the melt through single-crystal optical tracking. The results show that origami frame assembly can be described by classical nucleation and growth theory, which can, in turn, be used to prescribe the growth of the crystals. Lastly, these findings are applied to demonstrate thermal pathway-dependent assembly, forming distinct assemblies based on different thermal annealing profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Trace Adsorptive Removal of PFAS from Water by Optimizing the UiO‐66 MOF Interface

Abstract The confluence of pervasiveness, bioaccumulation, and toxicity in freshwater contaminants presents an environmental threat second to none. Exemplifying this trifecta, per‐ and polyfluoroalkyl substances (PFAS) present an alarming hazard among the emerging contaminants. State‐of‐the‐art PFAS adsorbents used in drinking water treatment, namely, activated carbons and ion‐exchange resins, are handicapped by low adsorption capacity, competitive adsorption, and/or slow kinetics. To overcome these shortcomings, metal–organic frameworks (MOFs) with tailored pore size, surface, and pore chemistry are promising alternatives. Thanks to the compositional modularity of MOFs and polymer–MOF composites, herein we report on a series of water‐stable zirconium carboxylate MOFs and their low‐cost polymer‐grafted composites as C 8 –PFAS adsorbents with benchmark kinetics and “parts per billion” removal efficiencies. Bespoke insights into the structure–function relationships of PFAS adsorbents are obtained by leveraging interfacial design principles on solid sorbents, creating a synergy between the extrinsic particle surfaces and intrinsic molecular building blocks.

Ilić, Nebojša↗
DOE OSTI2022

Increasing risk of cascading hazards in the central Himalayas

Cascading hazards are becoming more prevalent in the central Himalayas. Primary hazards (e.g., earthquakes, avalanches, and landslides) often trigger secondary hazards (e.g., landslide dam, debris flow, and flooding), compounding the risks to human settlements, infrastructures, and ecosystems. Risk management strategies are commonly tailored to a single hazard, leaving human and natural systems vulnerable to cascading hazards. In this commentary, we characterize diverse natural hazards in the central Himalayas, including their cascading mechanisms and potential impacts. A scientifically sound understanding of the cascading hazards, underlying mechanisms, and appropriate tools to account for the compounding risks are crucial to informing the design of risk management strategies. Here, we also discuss the need for an integrated modeling framework, reliable prediction and early warning system, and sustainable disaster mitigation and adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ChemComp: Compiling and Computing with Chemical Reaction Networks

The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.

Bohm Agostini, Nicolas↗
DOE OSTI2024

Support size regulated ruthenium-sulfoacid-nitrogen sites intensify cellulose hydrogenolysis to 1,2-propylene glycol

Rational design of metal-acid-base multifunctional catalysts for upgrading cellulose to 1,2-propylene glycol (1,2-PG) is of great significance for building a sustainable world. However, it is time-consuming and tedious to regulate metal-acid-base sites to balance major reactions to render a high 1,2-PG yield. We herein report support size simultaneously regulated ruthenium-sulfoacid-nitrogen (Ru–SO 3 H–N) sites for cellulose hydrogenolysis to high yield 1,2-PG. Originated from the depolymerization and reassembly of zinc-1,3,5-benzenetricarboxylic acid (ZnBTC) fiber with zeolitic imidazolate framework (ZIF-8) in water, 2-methylimidazole infiltrated nanorod (ZnBTC(mIM)) with a varied aspect ratio was fabricated by varying the feed ratio of ZIF-8/ZnBTC. Upon being pyrolyzed, sulfonated and impregnated with Ru ions, the supported sites were tailored in terms of Ru single-atom/nanocluster ratio, SO 3 H acidity and N basicity. Further, the elaborately fabricated catalyst delivers 32.3% yield of 1,2-PG, corresponding to a high productivity of 67.71 mol h -1 g Ru -1 and a large turnover number of 34193, two and three orders of magnitude higher than those by using other Ru-containing catalytic systems for cellulose hydrogenolysis. The excellent performance can be attributed to optimized electronic and molecular structure of Ru–SO 3 H–N sites that can improve rate-determining cellulose hydrolysis/fructose hydrogenolysis, pivotal glucose isomerization with others to proceed at a matched rate. This study opens a new avenue to facilely tailor the metal-acid-base sites by rational design of size controlled supporting matrix.

1,2-Propylene glycol↗
DOE OSTI2022

Crystallization of potassium-zeolites in organic-free media

A ubiquitous phenomenon in zeolite synthesis is the generation of polymorphs leading to either single or multi-phase products. The use of organic structure-directing agents (OSDAs) often ensures a high degree of phase purity for a desired zeolite; however, OSDAs can be economically and environmentally prohibitive. The use of alkali metals as inorganic structure-directing agents (ISDAs) is a common alternative, although reductions in polymorph specificity and/or purity in organic-free media are significant limitations. Developing more controlled, facile synthesis strategies without the aid of OSDAs relies on understanding how zeolite growth conditions impact polymorph selection and alter the physicochemical properties of crystalline products. Relatively few studies of organic-free syntheses have systematically explored potassium growth media. Here, in this study, we examine synthesis conditions that promote the formation of five K-zeolites: CHA, LTL, MER, EDI, and BPH frameworks. Time-resolved analyses reveal distinct trends in crystallization kinetics and interzeolite transformations for potassium compared to prior studies that have largely focused on Na-zeolites. We also highlight methods to achieve nanosized crystals and markedly reduce crystallization times for zeolites, such as chabazite, which are notoriously difficult to synthesize in organic-free media at reasonable timescales. Collectively, these findings identify routes to control K-zeolite polymorphism and provide general design strategies to tailor their crystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

CReST-VCT System Integration Framework

In this paper, we propose a system integration framework for deploying the Coordinated Real-time Sub-Transmission Volt-var Control Tool (CReST VCT) functionality between the transmission system, the distribution system, and DERs. This represents one possible set of design choices to implement the coordinated optimization capabilities in an operating environment. The framework provides the following: An architectural reference model that describes how the CReST VCT solution maps into a broader grid architecture that accommodates and enables higher levels of DER penetration and utilization: A system integration reference model that defines a possible allocation of CReST VCT functionality within a utility’s operational environment. This includes one possible system design approach for allocation of functionality within software and protocol selections across the various interfaces; A list of potential performance and security issues that should be considered during an implementation. This framework does not represent a specific implementation of the CReST VCT algorithm or integration into a specific utility environment. It is intended to serve as a reference for those who plan to implement such a solution into their environment. The general model provided by the framework can be tailored to a specific environment based on its specific constraints and requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

What is in a name: Defining “high entropy” oxides

High entropy oxides are emerging as an exciting new avenue to design highly tailored functional behaviors that have no traditional counterparts. Study and application of these materials are bringing together scientists and engineers from physics, chemistry, and materials science. The diversity of each of these disciplines comes with perspectives and jargon that may be confusing to those outside of the individual fields, which can result in miscommunication of important aspects of research. In this Perspective, we provide examples of research and characterization taken from these different fields to provide a framework for classifying the differences between compositionally complex oxides, high entropy oxides, and entropy stabilized oxides, which is intended to bring a common language to this emerging area. We highlight the critical importance of understanding a material’s crystallinity, composition, and mixing length scales in determining its true definition.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sparse Matrix-Based HPC Tomography

Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Furthermore, flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. Furthermore, the solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.

97 MATHEMATICS AND COMPUTING↗