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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Grid Communications Supply Chain & Emerging Regulation Challenges Session 1

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2025

Genesis: A Compiler Framework for Hamiltonian Simulation on Hybrid CV-DV Quantum Computers

We introduce Genesis, the first compiler designed to support Hamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable (DV) quantum computing systems. Genesis is a two-level compilation system. At the first level, it decomposes an input Hamiltonian into basis gates using the native instruction set of the target hybrid CV-DV quantum computer. At the second level, it tackles the mapping and routing of qumodes/qubits to implement long-range interactions for the gates decomposed from the first level. Rather than a typical implementation that relies on SWAP primitives similar to qubit-based (or DV-only) systems, we propose an integrated design of connectivity-aware gate synthesis and beamsplitter SWAP insertion tailored for hybrid CV-DV systems. We also introduce an OpenQASM-like domain-specific language (DSL) named CVDV-QASM to represent Hamiltonian in terms of Pauli-exponentials and basic gate sequences from the hybrid CVDV gate set. Genesis has successfully compiled several important Hamiltonians, including the Bose-Hubbard model, Z2−Higgs model, Hubbard-Holstein model, Heisenberg model and Electron-vibration coupling Hamiltonians, which are critical in domains like quantum field theory, condensed matter physics, and quantum chemistry. Our implementation is available at Genesis-CVDV-Compiler https://github.com/ruadapt/Genesis-CVDV-Compiler

Chen, Henry↗
DOE OSTI2025

A Comprehensive Chemistry Evaluation and Diagnostics Package for E3SM – ChemDyg Version 1.1.0

The Chemistry Evaluation and Diagnostics Package (ChemDyg) is an open-source tool designed for the Energy Exascale Earth System Model (E3SM) developed by the U.S. Department of Energy. ChemDyg facilitates routine evaluation, tailored development, and in-depth analysis of atmospheric chemistry through its modular architecture, allowing users to compare model outputs with observational data. Version 1.1.0 introduces a robust set of diagnostic capabilities, including climatology, time evolution of key tracers, diurnal and annual cycle analyses, and extensive budget diagnostics. These features help identify model discrepancies and enhance the representation of atmospheric chemistry in E3SM. Each self-contained diagnostic set includes dedicated scripts and documentation for ease of use. The interactive HTML output improves data accessibility, accelerating chemistry model development. Additionally, ChemDyg's flexible framework allows for customization, enabling users to create unique diagnostic sets for specific scientific contributions.

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

Enabling Next Generation Reaction Injection Molding (RIM) for Lightweight Structures

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this project, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. This optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption. This research has been performed in collaborations with McClarin Composites. The research outcome has been submitted to the Journal of Manufacturing Processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design Strategies of Spinel Oxide Frameworks Enabling Reversible Mg-Ion Intercalation

In this study, reversible Mg 2+ intercalation in metal oxides frameworks is a key enabler for an operational Mg ion battery with high energy density needed for the next generation of energy storage technologies. While functional Mg-ion batteries have been achieved in structures with soft anions (e.g., S 2- and Se 2- ), they do not meet energy density requirements to compete with the current rechargeable lithium-ion batteries due to their low insertion potentials, emphasizing the necessity of finding an oxide-based cathode that operates at high potentials. A leading hypothesis to explain the limited availability of oxide Mg-ion cathodes is the belief that Mg 2+ has sluggish diffusion kinetics in oxides due to strong electrostatic interactions between the Mg 2+ ions and oxide anions in the lattice. From this assessment, it can be hypothesized that such rate limiting kinetic shortcomings can be mitigated by tailoring an oxide framework through creating less stable Mg 2+ - O 2- coordination.

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

Simulation-driven design optimization of reaction injection molding (RIM) process for polydicyclopentadiene (pDCPD): Minimizing cycle time, defects, and warpage

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this study, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. In conclusion, this optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption.

42 ENGINEERING↗
DOE OSTI2025

Countering Weapons of Mass Destruction (CWMD) Zero Trust Framework: CWMD Zero Trust Principles Model

The research focuses on the critical need for enhanced cybersecurity within the Countering Weapons of Mass Destruction (CWMD) Office, specifically targeting Chemical, Biological, Radiological, and Nuclear devices. Traditional perimeter-based security models are insufficient against modern cyber threats, prompting a shift toward Zero Trust principles (ZTP) that emphasize continuous verification and stringent security for all devices. Federal directives mandate the adoption of Zero Trust (ZT) across agencies, supported by guidelines from National Institute of Standards and Technology (NIST), U.S. Department of Homeland Security (DHS) Cybersecurity and Infrastructure Security Agency (CISA), U.S. Department of Defense (DoD) and National Security Agency (NSA). The research involved mapping ZT guidance from these agencies to develop tailored CWMD ZTP. The study identified gaps and areas for improvement, including clear transitional guidance from traditional to ZT architectures and the focus on explicit cross cutting capabilities. Design improvements are recommended to ensure increased comprehensive protection and resilience against sophisticated cyber threats for Chemical, Biological, Radiological, and Nuclear (CBRN) devices. Collaborative efforts among federal agencies are essential for the successful deployment of an optimized ZT guidance.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Current Understanding of Mechanistic Pathways in Zeolite Crystallization

Zeolite catalysts and adsorbents have been an integral part of many commercial processes and are projected to play a significant role in emerging technologies to address the changing energy and environmental landscapes. The ability to rationally design zeolites with tailored properties relies on a fundamental understanding of crystallization pathways to strategically manipulate processes of nucleation and growth. The complexity of zeolite growth media engenders a diversity of crystallization mechanisms that can manifest at different synthesis stages. Here, in this review, we discuss the current understanding of classical and nonclassical pathways associated with the formation of (alumino)silicate zeolites. We begin with a brief overview of zeolite history and seminal advancements, followed by a comprehensive discussion of different classes of zeolite precursors with respect to their methods of assembly and physicochemical properties. The following two sections provide detailed discussions of nucleation and growth pathways wherein we emphasize general trends and highlight specific observations for select zeolite framework types. We then close with conclusions and future outlook to summarize key hypotheses, current knowledge gaps, and potential opportunities to guide zeolite synthesis toward a more exact science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Carbon-Based Nanomaterials (CNMs) and Derived Composites and Devices

A review of different modeling techniques, specifically in the framework of carbon-based nanomaterials (CNMs, including nanoparticles such as graphene and carbon nanotubes—CNTs) and the composites and devices that can be derived from them, is presented. The article emphasizes that the overall performance of these materials depends on mechanisms that operate across different time and spatial scales, requiring tailored approaches based on the material type, size, internal structure/configuration, and the specific properties of interest. Far from attempting to cover the entire spectrum of models, this review examines a wide range of analysis and simulation techniques, highlighting their potential use, some of their weaknesses and strengths, and presenting the latest developments and some application examples. In this way, it is shown how modeling can provide key information for tailoring or designing new materials for specific components or systems or to obtain certain functionalities. At the same time, it is revealed to be an area constantly undergoing development and improvement, as evidenced by the progress made by various of these techniques and the new modeling approaches that have emerged in recent years.

Chiminelli, Agustίn (ORCID:0000000230312107)↗
DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Transforming Boron Carbon Nitride: A Carbon-to-Oxygen Switch to Boost Propane Oxidative Dehydrogenation

Hexagonal boron nitride (h-BN) catalysts exhibit high alkene selectivity in the oxidative dehydrogenation of propane (ODHP). Nevertheless, the conversion-selectivity trade-off persisted primarily due to the low density of oxygen-containing boron active species, while simple and controllable modification strategies for h-BN still face challenges. Herein, we developed an in situ carbon-to-oxygen switch strategy within a tailored boron carbon nitride (BCN) framework, in which uniformly embedded B–C 3 were transformed into B–O 3 via oxidative treatment (denoted as BNO x ). The structural evolution from B–C 3 to B–O 3 was well characterized by spectroscopy and soft X-ray absorption techniques. The resulting BNO x catalysts, enriched with B–O 3 units, demonstrated performance in ODHP, achieving a propane conversion of 50.4% with 32.7% olefin yield at 500 °C. Density functional theory (DFT) calculations confirmed that B–O 3 species preferentially lower activation barriers, rendering the process thermodynamically more favorable. In conclusion, this work introduced an in situ reconstruction method for atomic-level heteroatom-engineered h-BN catalysts, opening an avenue for advanced catalyst design across energy conversion systems.

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

An orbital strategy for regulating the Jahn–Teller effect

ABSTRACT The Jahn–Teller effect (JTE) arising from lattice–electron coupling is a fascinating phenomenon that profoundly affects important physical properties in a number of transition-metal compounds. Controlling JT distortions and their corresponding electronic structures is highly desirable to tailor the functionalities of materials. Here, we propose a local coordinate strategy to regulate the JTE through quantifying occupancy in the ${{d}_{{{z}^2}}}$ and ${{d}_{{{x}^2} - {{y}^2}}}$ orbitals of Mn and scrutinizing the symmetries of the ligand oxygen atoms in MnO6 octahedra in LiMn2O4 and Li0.5Mn2O4. The effectiveness of such a strategy has been demonstrated by constructing P2-type NaLixMn1–xO2 oxides with different Li/Mn ordering schemes. In addition, this strategy is also tenable for most 3d transition-metal compounds in spinel and perovskite frameworks, indicating the universality of local coordinate strategy and the tunability of the lattice–orbital coupling in transition-metal oxides. This work demonstrates a useful strategy to regulate JT distortion and provides useful guidelines for future design of functional materials with specific physical properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Multimetallic Metal-Organic Frameworks as Heterogeneous Catalysts for Gas Phase Hydroformylation and Hydrogenation Reactions

This project focused on the development bimetallic metal-organic frameworks (MOFs) as gas phase heterogeneous catalysts for hydrogenation and hydroformylation reactions. MOFs are a new class of hybrid inorganic/organic materials that are highly crystalline, with structures consisting of metal nodes of specific geometries connected by organic linkers. Although there have been a number of studies of catalysis at MOF nodes in solution, there is little experimental data in the literature for gas phase reactions despite the fact that industrial heterogeneous catalysis on MOFs is more economically viable than homogeneous catalysis. The use of MOFs as heterogeneous catalysts presents the unique opportunity to carefully control the composition, geometry and ensemble sizes of the active sites, which are all critical factors for the rational design of new catalysts. Specific objectives of the project are as follows: (1) to tailor the geometry, composition and ensemble size of the active sites; (2) to understand how the adsorption of molecules at metal sites can be modified by interactions with a neighboring metal site; (3) to determine how oxidation states of the metal change during reaction and how these states can be modified by metal-metal electronic interactions; and (4) to elucidate reaction mechanisms and intermediates. For these studies, we have chosen to investigate selective hydrogenation of hydrocarbons and hydroformylation, which are industrially relevant reactions. Our interdisciplinary team of Chen, Shustova and Vogiatzis/Henkelman provides critical expertise in novel MOF synthesis (Shustova), atomic-scale surface science and catalysis (Chen) and computational studies of reaction mechanisms (Vogiatzis/Henkelman) that are necessary for the development of these catalysts. We believe that this work will lead directly to the rational design of new catalysts that are versatile, highly active/selective and suitable for industrial processes.

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

3D printable silicone compositions exhibiting high toughness and low durometer

3D printable silicones can be designed with varying crosslink density, network structures, and types of reinforcing additives. Within this design space, one may tailor the uncured material’s rheology and mechanical response to access a wide range of potential applications. However, printable, low modulus silicones, particularly those applicable to direct ink write, are underreported in published literature. To address the need for higher performance, low modulus silicones with demonstrated printability, a new set of ca. 20–50 Shore A hardness silicone elastomers exhibiting ca. 7 MPa ultimate tensile strength and ca. 400–1200% elongation at break is presented. Mechanical properties were analyzed, providing insight to the effects of formulation constituents on mechanical properties. Cyclic mechanical testing of the silicone formulations was also performed, and the energy loss and permanent set throughout cycling were determined. In conclusion, printed structures demonstrate the feasibility of these new silicones as durable frameworks for novel soft device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Designed and biologically active protein lattices

Versatile methods to organize proteins in space are required to enable complex biomaterials, engineered biomolecular scaffolds, cell-free biology, and hybrid nanoscale systems. Here, we demonstrate how the tailored encapsulation of proteins in DNA-based voxels can be combined with programmable assembly that directs these voxels into biologically functional protein arrays with prescribed and ordered two-dimensional (2D) and three-dimensional (3D) organizations. We apply the presented concept to ferritin, an iron storage protein, and its iron-free analog, apoferritin, in order to form single-layers, double-layers, as well as several types of 3D protein lattices. Our study demonstrates that internal voxel design and inter-voxel encoding can be effectively employed to create protein lattices with designed organization, as confirmed by in situ X-ray scattering and cryo-electron microscopy 3D imaging. The assembled protein arrays maintain structural stability and biological activity in environments relevant for protein functionality. The framework design of the arrays then allows small molecules to access the ferritins and their iron cores and convert them into apoferritin arrays through the release of iron ions. The presented study introduces a platform approach for creating bio-active protein-containing ordered nanomaterials with desired 2D and 3D organizations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

CHEQUP v0.1

CHEQUP (Castro-based Hofi Expansion with QUasineutral Plasma) is a simulation code for modeling the formation of hydrodynamic optical-field-ionized (HOFI) plasma channels, which are used as waveguides in laser-plasma acceleration experiments. This includes experiments performed at LBNL's BELLA facility as well as other laser facilities across the world. CHEQUP extends the open-source Castro hydrodynamics framework with physics modules tailored for modeling HOFI plasma channels -- including multi-species ionization and three-body recombination for mixtures of hydrogen, nitrogen, helium, and argon ; a two-temperature model tracking electron and heavy-species temperatures separately ; and coupling with other codes of the BLAST ecosystem (https://blast.lbl.gov/) such as WarpX, via the openPMD standard. CHEQUP inherits from Castro the ability to run on modern GPU architectures (NVIDIA CUDA, AMD HIP) and supports adaptive mesh refinement (AMR) for efficient multi-scale resolution. Compared to existing tools, CHEQUP would be, to our knowledge, the first open-source code implementing the full HOFI channel formation physics, and the first implementation capable of running on GPUs. This enables significantly faster, large-scale parameter scans critical for the design of next-generation LPA-based accelerators and light sources.

Lehe, Remi [Lawrence Berkeley National Laboratory ↗
DOE OSTI2026

Cybersecurity Center for Offshore Wind Energy (Final Project Report)

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models with SCADA infrastructure, and deployed a scaled physical turbine and associated sensors. High-resolution operational and side-channel data streams were collected and used to refine machine-learning (ML)-based attack detection systems and to extend the WindCRAFT framework to multi-turbine threat scenarios. The project demonstrated a realistic, scalable environment for evaluating cyber threats, validated attack detection approaches using enriched datasets, and identified new multi-turbine and inter-turbine communication attack vectors. The resulting testbed, models, and security mechanisms provide a foundation for ongoing R&D and deployment of cyber-resilient offshore wind energy systems.

17 WIND ENERGY↗