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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 OSTI2014

Bio-inspired routes for synthesizing efficient nanoscale platinum electrocatalysts

The overall objective of the proposed research is to use fundamental advances in bionanotechnology to design powerful platinum nanocrystal electrocatalysts for fuel cell applications. The new economically-viable, environmentally-friendly, bottom-up biochemical synthetic strategy will produce platinum nanocrystals with tailored size, shape and crystal orientation, hence leading to a maximum electrochemical reactivity. There are five specific aims to the proposed bio-inspired strategy for synthesizing efficient electrocatalytic platinum nanocrystals: (1) isolate peptides that both selectively bind particular crystal faces of platinum and promote the nucleation and growth of particular nanocrystal morphologies, (2) pattern nanoscale 2-dimensional arrays of platinum nucleating peptides from DNA scaffolds, (3) investigate the combined use of substrate patterned peptides and soluble peptides on nanocrystal morphology and growth (4) synthesize platinum crystals on planar and large-area carbon electrode supports, and (5) perform detailed characterization of the electrocatalytic behavior as a function of catalyst size, shape and morphology. Project Description and Impact: This bio-inspired collaborative research effort will address key challenges in designing powerful electrocatalysts for fuel cell applications by employing nucleic acid scaffolds in combination with peptides to perform specific, environmentally-friendly, simultaneous bottom-up biochemical synthesis and patterned assembly of highly uniform and efficient platinum nanocrystal catalysts. Bulk synthesis of nanoparticles usually produces a range of sizes, accessible catalytic sites, crystal morphologies, and orientations, all of which lead to inconsistent catalytic activities. In contrast, biological systems routinely demonstrate exquisite control over inorganic syntheses at neutral pH and ambient temperature and pressures. Because the orientation and arrangement of the templating biomolecules can be precisely controlled, the nanocrystals boast a defined shape, morphology, orientation and size and are synthesized at benign reaction conditions. Adapting the methods of biomineralization towards the synthesis of platinum nanocrystals will allow effective control at a molecular level of the synthesis of highly active metal electrocatalysts, with readily tailored properties, through tuning of the biochemical inputs. The proposed research will incorporate many facets of biomineralization by: (1) isolating peptides that selectively bind particular crystal faces of platinum (2) isolating peptides that promote the nucleation and growth of particular nanocrystal morphologies (3) using two-dimensional DNA scaffolds to control the spatial orientation and density of the platinum nucleating peptides, and (4) combining bio-templating and soluble peptides to control crystal nucleation, orientation, and morphology. The resulting platinum nanocrystals will be evaluated for their electrocatalytic behavior (on common carbon supports) to determine their optimal size, morphology and crystal structure. We expect that such rational biochemical design will lead to highly uniform and efficient platinum nanocrystal catalysts for fuel cell applications.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Wind study for high altitude platform design

An analysis of upper air winds was performed to define the wind environment at potential operating altitudes for high altitude powered platform concepts. Wind conditions of the continental United States, Pacific area (Alaska to Sea of Japan), and European area (Norwegian and Mediterranean Sea) were obtained using a representative network of sites selected based upon adequate high altitude sampling, geographic dispersion, and observed upper wind patterns. A data base of twenty plus years of rawinsonde gathered wind information was used in the analysis. Annual variations from surface to 10 mb pressure altitude were investigated to encompass the practical operating range for the platform concepts. Parametric analysis for the United States and foreign areas was performed to provide a basis for vehicle system design tradeoffs. This analysis of wind magnitudes indicates the feasibility of annual operation at a majority of sites and more selective seasonal operation for the extreme conditions between the pressure altitudes of 100 to 25 mb based upon the assumed design speeds.

High altitude powered platform studies↗
NASA NTRS2010

FPGA Coprocessor Design for an Onboard Multi-Angle Spectro-Polarimetric Imager

A multi-angle spectro-polarimetric imager (MSPI) is an advanced camera system currently under development at JPL for possible future consideration on a satellite-based Aerosol-Cloud-Environ - ment (ACE) interaction study. The light in the optical system is subjected to a complex modulation designed to make the overall system robust against many instrumental artifacts that have plagued such measurements in the past. This scheme involves two photoelastic modulators that are beating in a carefully selected pattern against each other. In order to properly sample this modulation pattern, each of the proposed nine cameras in the system needs to read out its imager array about 1,000 times per second. The onboard processing required to compress this data involves least-squares fits (LSFs) of Bessel functions to data from every pixel in realtime, thus requiring an onboard computing system with advanced data processing capabilities in excess of those commonly available for space flight. As a potential solution to meet the MSPI onboard processing requirements, an LSF algorithm was developed on the Xilinx Virtex-4FX60 field programmable gate array (FPGA). In addition to configurable hardware capability, this FPGA includes Power -PC405 microprocessors, which together enable a combination hardware/ software processing system. A laboratory demonstration was carried out based on a hardware/ software co-designed processing architecture that includes hardware-based data collection and least-squares fitting (computationally), and softwarebased transcendental function computation (algorithmically complex) on the FPGA. Initial results showed that these calculations can be handled using a combination of the Virtex- 4TM Power-PC core and the hardware fabric.

Pingree, Paula J.↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models

Understanding the processes that determine above-ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A critical review on questionnaire surveys in the field of energy-related occupant behaviour

Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social and local comfort parameters. In this study, 33 projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field, and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Retrieval Augmented Generation for Robust Cyber Defense

In cybersecurity, the ability to efficiently analyze and respond to vulnerabilities, weaknesses, attack patterns, and threat tactics is critical for effective defense strategies. With the increasing complexity and volume of cybersecurity data, traditional methods of querying and retrieving information are often inadequate. To address this challenge, we implemented Retrieval-Augmented Generation (RAG) systems—CyRAG and GraphCyRAG—that integrate large language models (LLMs) with both structured data from relational databases and knowledge graphs such as Neo4j. CyRAG is designed to handle structured data, focusing on CVE (Common Vulnerabilities and Exposures) and CWE (Common Weakness Enumeration) entities to generate accurate and context-rich responses. In contrast, GraphCyRAG leverages Neo4j knowledge graphs to retrieve interconnected information from CVE, CWE, CAPEC (Common Attack Pattern Enumeration and Classification), and ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) datasets. By utilizing Neo4j’s graph-based framework, GraphCyRAG enables deeper traversal of relationships between vulnerabilities and attack patterns, providing cybersecurity analysts with more comprehensive insights into potential attack vectors and mitigation strategies. Our preliminary results demonstrate that integrating knowledge graphs with RAG significantly enhances both the accuracy and depth of threat analysis, allowing for the retrieval of dynamic, real-time data and the generation of contextually aware responses. This approach helps analysts uncover hidden relationships between cyber entities, predict exploit paths, and prioritize mitigation efforts effectively. The integration of RAG with cybersecurity knowledge graphs represents a significant advancement in cybersecurity threat intelligence, enabling more informed decision-making and stronger defense strategies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Active Pattern Factor Control for Gas Turbine Engines

Small variations in fuel/air mixture ratios within gas turbine combustors can result in measurable, and potentially detrimental, exit thermal gradients. Thermal gradients can increase emissions, as well as shorten the design life of downstream turbomachinery, particularly stator vanes. Uniform temperature profiles are usually sought through careful design and manufacturing of related combustor components. However, small componentto-component variations as well as numerous aging effects degrade system performance. To compensate for degraded thermal performance, researchers are investigating active, closed-loop control schemes.

May, James E.↗
NASA NTRS1988

Requirements analysis, domain knowledge, and design

Two improvements to current requirements analysis practices are suggested: domain modeling, and the systematic application of analysis heuristics. Domain modeling is the representation of relevant application knowledge prior to requirements specification. Artificial intelligence techniques may eventually be applicable for domain modeling. In the short term, however, restricted domain modeling techniques, such as that in JSD, will still be of practical benefit. Analysis heuristics are standard patterns of reasoning about the requirements. They usually generate questions of clarification or issues relating to completeness. Analysis heuristics can be represented and therefore systematically applied in an issue-based framework. This is illustrated by an issue-based analysis of JSD's domain modeling and functional specification heuristics. They are discussed in the context of the preliminary design of simple embedded systems.

Potts, Colin↗
DOE OSTI2015

Design and Measurement of a Low-Noise 64-Channels Front-End Readout ASIC for CdZnTe Detectors

Cadmium zinc telluride (CdZnTe) detectors, as one of the principal detectors for the next-generation X-ray and γ-ray imagers, have high energy resolution and supporting electrode patterning in the radiation environment at room-temperature. In the present, a number of internationally renowned research institutions and universities are actively using these detector systems to carry out researches of energy spectrum analysis, medical imaging, materials characterization, high-energy physics, nuclear plant monitoring, and astrophysics. As the most important part of the readout system for the CdZnTe detector, the front-end readout application specific integrated circuit (ASIC) would have an important impact on the performances of the whole detector system. In order to ensure the small signal to noise ratio (SNR) and sufficient range of the output signal, it is necessary to design a front-end readout ASIC with very low noise and very high dynamic range. In addition, radiation hardness should be considered when the detectors are utilized in the space applications and high energy physics experiments. In this paper, we present measurements and performances of a novel multi-channel radiation-hardness low-noise front-end readout ASIC for CdZnTe detectors. The readout circuits in each channel consist of charge sensitive amplifier, leakage current compensation circuit (LCC), CR-RC shaper, S-K filter, inverse proportional amplifier, peak detect and hold circuit (PDH), discriminator and trigger logic, time sequence control circuit and driving buffer. All of 64 readout channels' outputs enter corresponding inputs of a 64 channel multiplexer. The output of the mux goes directly out of the chip via the output buffer. The 64-channel readout ASIC is implemented using the TSMC 0.35 μm mixed-signal CMOS technology. The die size of the prototype chip is 2.7 mm x 8 mm. At room temperature, the equivalent noise level of a typical channel reaches 66 e{sup -} (rms) at zero farad for a power consumption of 8 mW per channel. The linearity error is lower than 1% and the overall gain of the readout channel is 165 V/pC. The crosstalk between the channels is less than 3%. By connecting the readout ASIC to a CdZnTe detector, we obtained a γ-ray spectrum, the energy resolution is 5.1% at the 59.5-keV line of {sup 241}Am source. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Mechanical mismatch-driven rippling in carbon-coated silicon sheets for stress-resilient battery anodes

High gravimetric/volumetric capacity and low working potential make Si as one of the ideal candidate anode materials for lithium ion batteries. However, the large volume change of Si upon lithiation/delithiation poses a critical challenge for stable battery operations. Here, we introduce a novel designing concept, which takes advantage of such a deformation and ensures the structural stability of the material by developing a 2D Si nanosheet coated with a thin carbon layer (2DSi@C). Upon electrochemical cycling, this 2DSi@C exhibits unique deformation patterns, featuring accommodation of deformation in the thickness direction upon lithiation, while forming ripples upon delithiation, as consistently demonstrated by in-situ TEM observation and chemomechanical simulation. The ripple formation presents a unique mechanism for releasing the cycling induced stress, rendering the 2DSi@C chemomechanically much more stable and durable than the bare 2DSi counterparts. This work demonstrates a general principle as how to take the advantage of the large deformation materials for designing high capacity electrode.

25 ENERGY STORAGE↗
DOE OSTI2015

Testing density wave theory with resolved stellar populations around spiral arms in M81

Stationary density waves rotating at a constant pattern speed Ω{sub P} would produce age gradients across spiral arms. We test whether such age gradients are present in M81 by deriving the recent star formation histories (SFHs) of 20 regions around one of M81's grand-design spiral arms. For each region, we use resolved stellar populations to determine the SFH by modeling the observed color–magnitude diagram constructed from archival Hubble Space Telescope F435W and F606W imaging. Although we should be able to detect systematic time delays in our spatially resolved SFHs, we find no evidence of star formation propagation across the spiral arm. Our data therefore provide no convincing evidence for a stationary density wave with a single pattern speed in M81, and instead favor the scenario of kinematic spiral patterns that are likely driven by tidal interactions with the companion galaxies M82 and NGC 3077.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Module-Fluidics: Building Blocks for Spatio-Temporal Microenvironment Control

Generating the desired solute concentration signal in micro-environments is vital to many applications ranging from micromixing to analyzing cellular response to a dynamic microenvironment. We propose a new modular design to generate targeted temporally varying concentration signals in microfluidic systems while minimizing perturbations to the flow field. The modularized design, here referred to as module-fluidics, similar in principle to interlocking toy bricks, is constructed from a combination of two building blocks and allows one to achieve versatility and flexibility in dynamically controlling input concentration. The building blocks are an oscillator and an integrator, and their combination enables the creation of controlled and complex concentration signals, with different user-defined time-scales. We show two basic connection patterns, in-series and in-parallel, to test the generation, integration, sampling and superposition of temporally-varying signals. All such signals can be fully characterized by analytic functions, in analogy with electric circuits, and allow one to perform design and optimization before fabrication. Such modularization offers a versatile and promising platform that allows one to create highly customizable time-dependent concentration inputs which can be targeted to the specific application of interest.

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

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Toward Ultraflat Surface Morphologies During Focused Electron Beam Induced Nanosynthesis: Disruption Origins and Compensation

Here we explore emerging applications for nanoscale materials demand precise deposit shape retention from design to deposition. This study investigates the effects that disrupt high-fidelity shapes during focused electron beam induced nanosynthesis. It is shown that process parameters, patterning strategies and deposit topography can impose lateral precursor coverage gradients during growth resulting in unwanted topographic artifacts. The study classifies the evolving surface shapes into four general types and explains the formation and transition from a fundamental point of view. Continuum model calculations and simulations expand the experimental results to provide a comprehensive insight into understand the disruption mechanism. The findings demonstrate that the well-established concept of growth regimes has to be expanded by its lateral gradients as they strongly influence final shape fidelities. Finally, the study is complemented by a compensation strategy that improves the edge fidelity on the lower nanoscale to further push this technique toward the intrinsic limitations.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Glow discharge based device for solving mazes

A glow discharge based device for solving mazes has been designed and tested. The device consists of a gas discharge chamber and maze-transformer of radial-azimuth type. It allows changing of the maze pattern in a short period of time (within several minutes). The device has been tested with low pressure air. Once switched on, a glow discharge has been shown to find the shortest way through the maze from the very first attempt, even if there is a section with potential barrier for electrons on the way. It has been found that ionization waves (striations) can be excited in the maze along the length of the plasma channel. The dependancy of discharge voltage on the length of the optimal path through the maze has been measured. A reduction in discharge voltage with one or two potential barriers present has been found and explained. The dependency of the magnitude of discharge ignition voltage on the length of the optimal path through the maze has been measured. The reduction of the ignition voltage with the presence of one or two potential barriers has been observed and explained.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Learning to Predict Material Structure from Neutron Scattering Data

Understanding structural properties of materials and how they relate to its atomic structure, while extremely challenging, is a key scientific quest that has dominated the landscape of materials research for decades. Neutron and X-ray scattering is a state-of-the-art method to investigate material structure on the atomic scale. Traditional methods of processing neutron scattering data to decipher the structure of target materials have relied on computing scattering patterns using physics-based forward models and comparing them with experimentally gathered scattering profiles within a computationally expensive optimization loop. Here, we report an initial design of a data-driven machine learning pipeline for material structure prediction that is computationally faster (once trained) and potentially more accurate. We describe the architecture of the ML pipeline and a preliminary benchmarking study of shallow machine learning models in terms of their prediction accuracy and limitations. We show that material structure prediction from neutron scattering data using shallow learning models is feasible to within 90% prediction accuracy for certain classes of materials but deeper models are required for more general material structure predictions.

Garcia Cardona, Cristina↗

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