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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Reflection and refraction of directrons at the interface

Reflection and refraction are ubiquitous phenomena with extensive applications, yet minimizing energy loss and information distortion during these processes remains a significant challenge. This study examines the behavior of structurally stable solitons, known as directrons, in nematic liquid crystals interacting with an interface where the director field orientation changes, despite identical physical properties, external potentials, and boundary anchoring in the two regions. During reflection and refraction, the directrons maintain nearly constant structure and velocity, ensuring energy conservation and information integrity. Microscopic analyses of the director field and macroscopic evaluations of effective potential are employed to elucidate the dependence of reflection and refraction probabilities on the directron’s incident angle and the orientation difference across the interface. The findings provide valuable insights into the dynamics of solitary waves in structured liquid crystal systems, offering significant implications for the development of tunable photonic devices, reconfigurable optical systems, and nanoscale material engineering.

Science & Technology - Other Topics↗
DOE OSTI2025

Bioelectrochemical crossbar architecture screening platform for extracellular electron transfer

Electroactive microbes can serve as living components in bioelectronic devices, where their unique ability to transfer electrons enables applications in sensing, energy conversion, and synthesis, but they remain challenging to engineer because the bioelectrochemical systems (BESs) used for characterization are low throughput. Here, we present a bioelectrochemical crossbar architecture screening platform (BiCASP) that uses stacked and orthogonally arrayed electrodes to enable individual sample selection for characterization in arrayed formats. This device reports on the current generated by electroactive bacteria on the minute timescale, decreasing the time for data acquisition by several orders of magnitude compared to conventional BESs. This device increases the throughput of screening engineered biological components in cells, identifying mutants of the membrane protein wire MtrA in Shewanella oneidensis that retain the ability to support extracellular electron transfer (EET). BiCASP may be integrated with bioelectronics that need directed evolution of electroactive proteins.

Shewanella↗
DOE OSTI2024

Novel Microbial Routes to Synthesize Industrially Significant Precursor Compounds

Ethylene is the most widely employed organic precursor compound in industry. The potential to impact ethylene formation via recently discovered microbial processes is tenable using plentiful CO2 feedstocks. The overall long-term objective of this project was to develop an industrially compatible microbial process to synthesize ethylene in high yields. The key objective of this project was to fully define and initially characterized a recently discovered and genetically regulated anaerobic pathway to produce high levels of ethylene called the Dihydroxyacetone Phosphate - Ethylene Pathway in phototrophic bacteria. This was addressed through the following specific aims: 1. Fully probe the catalytic potential of all enzymes of the DHAP ethylene pathway and determine the regulatory mechanism of DHAP-ethylene pathway gene expression. 2. Discover effective and active ethylene enzymes encoded in cultured and uncultured organisms from anoxic environments. 3.Model the thermodynamics and kinetics of ethylene synthetic pathways to guide engineering efforts in integrating best performing DHAP-ethylene pathway enzymes into model bacteria chassis for enhance ethylene yields. Through this project we discovered the initially missing genetic and enzyme component of the DHAP-ethylene pathway that directly synthesized ethylene and other important industrial compounds like methane and ethane from specific substrates. We uncovered and partially characterized a nitrogenase-like reductase that functions in DHAP-ethylene pathway specifically and in methionine synthesis in general. This nitrogenase-like system is called the Methylthio-Alkane Reductase (MAR) for its ability to cleave volatile organic sulfur compounds into methanethiol (CH3-SH) for methionine synthesis and a hydrocarbon byproduct. Key to the DHAP-ethylene pathway, MAR is the essential enzyme that cleaves 2-methylthioethanol (CH3-S-CH2-CH2-OH) into ethylene. Coordinately, we uncovered that the MAR genes and genes associated with conversion of methanethiol (CH3-SH) to methionine are under genetic control of a LysR Type Transcriptional Regulator called SalR, whose activity is dependent upon the amount of sulfate available to the cell. When sulfate as the preferred sulfur source for cell growth drops below 200 micromolar, SalR become active for expressing the MAR and methionine biosynthesis genes to enable the cell to grow from volatile organic sulfur compounds and make ethylene. Metabolic thermos-kinetic modeling revealed that these MAR reactions for ethylene and other hydrocarbon production are highly thermodynamically favorable and are one of the largest driving forces for ethylene production by the DHAP-ethylene pathway for high ethylene yields. Modeling also indicated that a key aldolase and to a lesser extent an isomerase of the DHAP-ethylene pathway for production of the ethylene precursor, 2-methylthioethanol, also would increase ethylene yields. Through metagenomic mining and gene synthesis by the JGI DNA synthesis program, over 500 aldolase and isomerase homologs were synthesized and screened. From this, variants were uncovered with substantially higher activity that increased ethylene yields 5-fold via the aldolase reaction and 1.5-fold via the isomerase reaction. Each of these elements that increase ethylene production were integrated together via plasmid under appropriate gene promoter elements in the phototrophic bacterium, Rhodospirillum rubrum, resulting in at least 3 orders of magnitude increase in ethylene yield from carbon dioxide feedstock.

10 SYNTHETIC FUELS↗
DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
DOE OSTI2024

Synthesis and growth of solution-processed chiral perovskites

In materials science, chiral perovskites stand out due to their exceptional optoelectronic properties and the versatility in their structure and composition, positioning them as crucial in the advances of technologies in spintronics and chiroptical systems. This review underlines the critical role of synthesizing and growing these materials, a process integral to leveraging their complex interplay between structural chirality and distinctive optoelectronic properties, including chiral-induced spin selectivity and chiroptical activity. The paper offers a comprehensive summary and discussion of the methods used in the synthesis and growth of chiral perovskites, delving into extensive growth techniques, fundamental mechanisms, and strategic approaches for the engineering of low-dimensional perovskites, alongside the creation of novel chiral ligands. The necessity of developing new synthetic approaches and maintaining precise control during the growth of chiral perovskites is emphasized, aiming to enhance their structural chirality and boost their efficiency in spin and chiroptical selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2026

Firmware Tampering Detection in Heavy-Duty Vehicles through J1939 CAN Analysis

Modern heavy-duty vehicles rely on complex networks of Electronic Control Units (ECUs) that communicate using the J1939 protocol. While this system makes it easier to update and configure vehicle components, it also opens the door to serious cybersecurity risks if not properly secured. This work investigates the potential for firmware tampering through the J1939 communication protocol, which enables ECU configuration and reprogramming over the Controller Area Network (CAN) bus. By monitoring CAN traffic during legitimate maintenance operations and reverse-engineering OEM diagnostic software, we identified common and proprietary J1939 message identifiers, authentication patterns, and vulnerabilities within Unified Diagnostic Services (UDS). These findings demonstrate that inadequate authentication mechanisms can allow malicious actors to alter ECU firmware or disable safety functions, posing severe operational and safety risks. Our analysis contributes to the development of vehicle intrusion detection systems capable of recognizing abnormal reprogramming activity and future firmware fingerprinting methods to verify software integrity across ECUs. This work highlights the importance of standardizing secure firmware authentication across manufacturers to strengthen cyber resilience in heavy-duty vehicle systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Intern Poster Session 08/13: Autonomous Nuclear Robotics: Applications in nuclear waste inspection and hot cell experiments

The nuclear industry is experiencing renewed interest in autonomous robotics, yet most deployed systems remain teleoperated with limited autonomy. This work presents two contributions toward fully autonomous nuclear robotic systems: autonomous waste inspection at the Hanford Site and an autonomous hot cell laboratory framework. Inspections of Hanford's underground waste storage tanks are performed manually at significant cost and personnel exposure. We developed a reinforcement-learning (RL) training pipeline for a custom-built inspection arm. In parallel, we are designing an autonomous laboratory framework for post-irradiation examination in hot cells at the Specimen Preparation Laboratory (SPL) that integrates computer vision, task and motion planning, hardware execution, and operator-in-the-loop control. These systems demonstrate a path toward safer, more efficient nuclear operations by reducing human exposure while maintaining rigorous human oversight at critical decision points.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Carbon Capture Pilot at Dry Fork Power Station (Final Technical Report)

The objective of this project was to design, seek necessary approvals, build and operate a large-scale pilot sorbent-based post combustion carbon capture system (CCS) at a coal fired power generation facility. TDA’s CCS uses a highly stable, low-cost, high-capacity physical adsorbent to effectively remove CO 2 via a combination vacuum and concentration swing adsorption (VCSA) process. The CCS is integrated with the power plant flue gas exhaust, which is rich in CO 2 (~13% vol. CO 2 ) and removes more than 90% of the plant’s overall carbon emissions.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Cybersecurity Standards, Certification, and Best Practices for DERs

Distributed energy resources (DERs) are becoming increasingly important to the electric grid, including solar energy systems. However, DERs also introduce new cybersecurity risks, including those posed by cloud computing. Standards harmonization is essential for ensuring that DERs are secure and can be safely integrated into the grid. This panel will discuss cyber standards harmonization for solar security. The panel will feature experts from the S2G Program, National Labs and Industry who will discuss the following topics: the cybersecurity risks and future benefits posed by ubiquitous solar energy systems, the development and implementation of cloud-based security solutions for DERs, including solar energy systems, the challenges and opportunities for harmonizing DER cybersecurity standards, and Cyber Informed Engineering and the solar security implementations The panel will also discuss the following specific initiatives: the S2G Program's DER Cybersecurity Framework, UL's DER Cybersecurity Certification Program, and IEEE 1547 Updates. The panel will conclude with a discussion of the future of standards harmonization for DER cybersecurity.

14 SOLAR ENERGY↗
DOE OSTI2024

Integrated Off-Gas System Tests on the DM1200 Melter with RPP-WTP LAW Sub-Envelope C1 Simulants (Final Report)

The report documents melter and off-gas performance results obtained on the DM1200 during processing of LAW Sub-Envelope Cl feed. The principal objectives of the DM1200 melter testing were to demonstrate suitable processing and production rates for LAW Cl feed; characterize the glass product for elemental composition, the presence of organic compounds, and TCLP response for metals; and measure destruction and removal efficiencies (DREs) for organic compounds across the melter and off-gas treatment components. The feed was spiked with organic compounds for part of the testing duration. Data were collected for both process engineering needs and to support regulatory activities. The data needed for regulatory support were collected using EPA methods and independent data analyses and data checking were performed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Crop models: integrating systems from the molecular to global for agricultural productivity and sustainability

Mathematical models that simulate crop growth in response to environmental conditions and management practices are essential tools for exploring agriculture-based strategies to address food security and environmental sustainability challenges. Early applications of crop models focused on supporting farmers in making management decisions. Applications have since expanded to estimating future impacts on local and global food production from changing climates. Emerging applications of crop models aim to leverage how these models integrate plant processes across biological scales to identify engineering or breeding strategies that account for environmentally-responsive dynamics at field scales and for exploring solutions to improve sustainability. In this review, we highlight recent studies across these four broad application areas and highlight potential future directions for the crop modeling field.

Piao, Ximin [Univ. of Illinois at Urbana-Champaign↗
DOE OSTI2024

Advanced Mineral Extraction and Water Processing: Application Development of Mesofluidic Separation Technology

This project explored the development of mesofluidic devices and methods to provide a range of new capabilities with a focus on selective mineral extraction and cost-effective water treatment systems. Mineral separation and water treatment technologies are typically slow, bulky, expensive, time and energy intensive processes. This leads to very large, expensive processing plants that are often inefficient and produce low quality products and large volumes of waste. Technologies used in mineral extraction and water treatment have changed little in the last 50-100 years. The new technology, based upon mesofluidic separation devices, could disruptively change the state of the art and alter long established economics. The new technologies explored will provide systems with much smaller footprints, higher throughput, modular components easily integrated into existing industrial processes and plants, lower cost, and novel separation capabilities. The results from this effort contributed to a wide range of new capabilities and numerous inventions disclosures.

42 ENGINEERING↗
DOE OSTI2026

Equipment Self-Assessment Guide Checklist

This Equipment Self-Assessment Checklist is designed for asset owners and operators (AOOs) responsible for the deployment, operation, maintenance, or cybersecurity oversight of grid systems and digital energy technologies. It provides a structured inspection checklist for evaluating the security, integrity, and operational trustworthiness of equipment across substations, generation sites, distributed energy resources (DERs), and control environments.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2026

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗