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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transport of Delocalized Excitons through DNA-Based Molecular Photonic Wires

Molecular photonic wires conduct electronic energy via their rapid transport properties. In photosynthesis, nature achieves efficient transport across large distances using delocalized excitons, generated by strong excitonic coupling between chromophores. How, or even whether, delocalization facilitates long-distance energy transport in synthetic systems has been challenging to experimentally test and optimize. Thus, far, studies have been limited to strongly coupled, heterogeneous chromophore aggregates or weakly coupled chromophore monomers. Here, in this work, we employed DNA nanostructures to engineer molecular photonic wires constructed from a series of excitonically coupled indocarbocyanine chromophores─achieving the intermediate and strong coupling regimes. Using time-resolved fluorescence spectroscopy and complementary simulations, we demonstrated that an intermediate intermolecular electronic coupling (∼ k B T ) enables up to 40% faster exciton transport as compared to strongly coupled chromophores. The delocalized excitons generated in the intermediate coupling regime exhibited properties conducive to rapid diffusivity, similar to their monomeric counterparts. Thus, intermediate excitonic coupling, analogous to natural systems, achieves long-distance exciton transport with the high chromophore density required for energy capture.

DNA origami↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2022

UrbanPop: A spatial microsimulation framework for exploring demographic influences on human dynamics

Ensuring the social equity of planning measures in social systems requires an understanding of human dynamics, particularly how individual relationships, activities, and interactions intersect with individual needs. Spatial microsimulation models (SMSMs) support planning for human security goals by representing human dynamics through realistic, georeferenced synthetic populations, that a) provide a complete representation of social systems while b) also protecting individual privacy. In this paper, we present UrbanPop, an open and reproducible SMSM framework for analysis of human dynamics with high spatial, temporal, and demographic resolution. UrbanPop creates synthetic populations of demographically detailed worker and student agents, positioning them first at probable nighttime locations (home), then moving them to probable daytime locations (work/school). Summary aggregations of these populations match the granular detail available at the census block group level in the American Community Survey Summary File (SF), providing realistic approximations of the actual population. UrbanPop users can select particular demographic traits important in their application, resulting in a highly tailored agent population. We first lay out UrbanPop's baseline methodology, including population synthesis, activity modeling, and diagnostics, then demonstrate these capabilities by developing case studies of shifting population distributions and high-risk populations in Knox County, TN during the global COVID-19 pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗
DOE OSTI2025

MIC-DP: A Scalable Correlation-Aware Differential Privacy Framework for High-Dimensional Data

Conventional differential privacy (DP) assumes record independence, limiting effectiveness on real-world datasets with temporal, spatial, or structural correlations. These dependencies undermine privacy guarantees and degrade utility in domains like healthcare, IoT, and smart city analytics. We propose Maximum Information Correlated Differential Privacy (MIC-DP), a novel framework that dynamically calibrates noise based on statistical dependencies. MIC-DP uses the Maximum Information Coefficient (MIC) to capture both linear and nonlinear correlations without explicit modeling, enabling adaptive sensitivity adjustment and improved privacy–utility trade-offs. Evaluations on healthcare (MIMIC), demographic (ACI), and synthetic datasets show that MIC-DP reduces mean absolute error (MAE) by up to 5.2% under strict privacy budgets (ϵ≤1), with aggregate utility improvements reaching 18% across datasets and evaluation metrics. MIC-DP provides formal (ϵ,δ)-privacy guarantees, scales efficiently with feature count, and supports deployment in moderate-scale, privacy-sensitive applications. Its tunable performance and runtime efficiency make MIC-DP suitable for privacy-sensitive applications where low-latency analytics and strong privacy guarantees must coexist. These results demonstrate MIC-DP’s effectiveness as a correlation-aware solution for practical DP.

Yang, Wenjun [Univ. of Washington, Tacoma, WA (Uni↗
DOE OSTI2026

A Scalable, Distribution Network-Aware, Customer Privacy-Preserving Framework for Operation of Virtual Power Plants

This poster presents a hierarchical control framework for a virtual power plant that leverages behind-the-meter resources for grid services while maintaining customer privacy during setpoint disaggregation. Unlike many existing approaches, the virtual power plant model uses a hierarchical control strategy and an iterative approach to determine the optimal set point dis-aggregation without direct load control while maintaining system-level power flow and voltage constraints. The proposed approach is numerically validated on a synthetic distribution feeder in San Francisco, demonstrating the ability of the framework to provide privacy-preserving virtual power plant services.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Dynamic Differential Image Circle Diameter Measurement Precision Assessment: Application to Burning Droplets

Dynamic measurement precision assessment has been achieved for a differential circle measurement application. Differential circle diameter measurement, in image analysis, typically requires fitting a circle model that optimizes for image distortions, defects or occlusions. The differential task occurs when precise measurements of diameter change are required given object size variation with time. An automated system was designed to provide diameter measurements and associated measurement precision of images of a fuel droplet undergoing combustion in zero gravity for the FLEX-2 dataset. An image gradient-based, least-squares boundary point fitting method to a circle or ellipse model is used for diameter measurement. The presence of soot aggregates poses significant challenges for diameter measurements when it occludes part of the droplet boundary. The precision of the diameter measurements depends upon the image quality. Using synthetic image simulations that model the soot behavior, we developed a model based on image quality measures that assesses the measurement precision for each individual diameter measurement. Thus, diameter measurements with precision assessments were made available for follow-up scientific analysis. As a result, the algorithm's success rate for measurable runs was 98%. In cases of limited occlusion, a measurement precision of ±0.2 pixels for the FLEX-2 dataset was achieved.

42 ENGINEERING↗
DOE OSTI2022

Frustrated Coulombic and Cation Size Effects on Nanoscale Boehmite Aggregation: A Tumbler Small- and Ultra-Small-Angle Neutron Scattering Study

To better understand the effects of solution chemistry on particle aggregation in the complex legacy tank wastes at the Hanford (WA) and Savannah River (SC) sites, we have performed a series of tumbler small- and ultra-small-angle neutron scattering experiments on 20 wt % solid slurries of nanoparticulate aluminum oxyhydroxide (boehmite) with M 1+ nitrates of various concentrations and radii. In this study, the solutes consisted of H, Li, Na, K, and Rb nitrates at 10 –5 , 10 –3 , 10 –1 , 2, and 4 molal (m) concentrations, as well as in pure H 2 O. Synthetic boehmite nanoparticles were used with a size range from ~20 to 30 nm. Tumbler cells were used to keep the solids from settling. Although particles initially form individual rhombohedral platelets, once placed in solution, they quickly form well-bonded stacks, primary aggregates, up to ~1500 Å long, and a second level of aggregates whose concentration and structure vary as a function of cation type and concentration. Aggregation generally increases with increased solute concentration and with cation radius up to a concentration somewhat above 10 –1 m, at which point the trend reverses. Primary aggregates become more rodlike and larger. The Kirkwood-like reversal probably reflects a change from Derjaguin–Landau–Verwey–Overbeek (DLVO)/Debye behavior controlled by surface chemistry to a frustrated Coulombic system controlled by the solution structure. These data suggest that an understanding of the effects of salt concentration and chemistry on nanoparticle aggregate structures provides useful physical insights into the microscopic origin of slurry rheology in the Hanford and Savannah River legacy wastes.

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

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗
DOE OSTI2021

Design Considerations for Distributed Energy Resource Honeypots and Canaries

There are now over 2.5 million Distributed Energy Resource (DER) installations connected to the U.S. power system. These installations represent a major portion of American electricity critical infrastructure and a cyberattack on these assets in aggregate would significantly affect grid operations. Virtualized Operational Technology (OT) equipment has been shown to provide practitioners with situational awareness and better understanding of adversary tactics, techniques, and procedures (TTPs). Deploying synthetic DER devices as honeypots and canaries would open new avenues of operational defense, threat intelligence gathering, and empower DER owners and operators with new cyber-defense mechanisms against the growing intensity and sophistication of cyberattacks on OT systems. Well-designed DER canary field deployments would deceive adversaries and provide early-warning notifications of adversary presence and malicious activities on OT networks. In this report, we present progress to design a high-fidelity DER honeypot/canary prototype in a late-start Laboratory Directed Research and Development (LDRD) project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Cell Free and Immobilization Technologies (CFIT)

Today, several key factors negatively impact the production of fuels and chemicals from renewable sources. Common hindrances in the biological production of biochemicals are: (1) end-product or intermediate toxicity to the microbial biocatalyst, (2) the diversion of carbon to biomass formation, and (3) co-production of undesired byproducts. A particularly attractive alternative is to eliminate the biocatalyst entirely and instead operate the desired metabolic pathways in isolation, thus circumventing the roadblocks of biological toxicity, lower yields, and lack of specificity. However, cell-free enzyme systems still suffer from low productivities owing in part to the effects of free diffusion of intermediates, lack of long term enzyme stability, cofactor cost or inefficient recycling rates, and finally, the cost of enzyme production/purification. This project represents a new effort to propose innovative and cost competitive routes to producing biochemicals from a variety of feedstocks using cell free approaches. These routes will help reduce the current risk and cost associated with classical cell free production. Cell free technologies show promise for application to the production of toxic/inhibitory products or products difficult to separate from microbial growth media and can help reduce the production barriers in multiple areas of biological conversion of feedstocks to biochemicals. More specifically, we are developing new technologies and routes that could be used to produce high value biochemicals such as 2,3 BDO and terpenes (among many others) from biomass derived C5/C6 sugars or lignin but also from waste byproducts such as glycerol. This project will lead to significant innovation and also lead to new concepts and rational design of pathways and enzymes. Within this project, we will develop new metabolic enzyme cascades that will represent natural or artificial combinations of enzymes to produce the desired biochemicals from a variety of feedstocks. We are also developing basic design principals for constructing synthetic metabolons, using fusion proteins and synthetic protein scaffolds, to promote substrate channeling and stability while conserving peak activity. Additionally, our efforts include a techno economic analysis (TEA) of cell free approaches to provide the sensitivities of the process to enzyme loading, activity, pH, reactor volumes, cofactor recycling rates. Finally, we are focusing on further increasing stability, operating lifetime and efficiency of the pathway enzymes by immobilization on support surfaces. We are also focus on immobilizing pathway enzymes or combinations of enzymes on several different conducting polymers and evaluate the effect on stability and operating lifetime. As more combinations become available we will conduct a more systematic study of the means of immobilizing these enzymes. This preliminary work will enable the in-depth study of cofactor recycling at these interfaces using mediators for electron transfer. Taken together, these approaches will enable process intensification, continuous operation, lower capital and separations costs and end-product flexibility, and thus has the potential to contribute significantly to BETO's goals of cost competitive biofuels and bioproducts. To date we have demonstrated 1) the conversion of pyruvate to 2,3 BDO (4 enzymes with cofactor recycling, no additional cofactors needed) at >100g/L (>3g/L/h) using our enzyme tethering approach, 2) Generated mutants of a key redox enzyme with >180- fold improvement in NADH utilization over WT to >85% of NADPH utilization. 3) Produced mevalonate at >10g/L from glucose, limonene at >10g/L from mevalonate and more than >5g/L from glucose with crude enzyme preparations and complete cofactor recycling. 4) successfully generated fully active cross-linked aggregates (CLEAs) of NOX, a cofactor-regenerating water-forming oxidase that is key to our process with increased stability 5) Engineered and identified enzymes able to use the biomimetic cofactor NMN and synthesized new synthetic cofactors.

biocatalysis↗
DOE OSTI2021

Poly(vinyl alcohol) Hydrogels with Broad-Range Tunable Mechanical Properties via the Hofmeister Effect

Hydrogels, exhibiting wide applications in soft robotics, tissue engineering, implantable electronics, etc., often require sophisticately tailoring of the hydrogel mechanical properties to meet specific demands. For examples, soft robotics necessitates tough hydrogels; stem cell culturing demands various tissue-matching modulus; and neuron probes desire dynamically tunable modulus. Herein, a strategy to broadly alter the mechanical properties of hydrogels reversibly via tuning the aggregation states of the polymer chains by ions based on the Hofmeister effect is reported. An ultratough poly(vinyl alcohol) (PVA) hydrogel as an exemplary material (toughness 150 ± 20 MJ m -3 ), which surpasses synthetic polymers like poly(dimethylsiloxane), synthetic rubber, and natural spider silk is fabricated. With various ions, the hydrogel's various mechanical properties are continuously and reversibly in situ modulated over a large window: tensile strength from 50 ± 9 kPa to 15 ± 1 MPa, toughness from 0.0167 ± 0.003 to 150 ± 20 MJ m -3 , elongation from 300 ± 100% to 2100 ± 300%, and modulus from 24 ± 2 to 2500 ± 140 kPa. Importantly, the ions serve as gelation triggers and property modulators only, not necessarily required to remain in the gel, maintaining the high biocompatibility of PVA without excess ions. Overall, this strategy, enabling high mechanical performance and broad dynamic tunability, presents a universal platform for broad applications from biomedicine to wearable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A High-Throughput Solver for Marginalized Graph Kernels on GPU

Here, we present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs. To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory. This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. We propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to further exploit sparsity. We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Understanding Thermal Stability in Zirconia-Based Aerogels

For lightweight, thermal insulation needs above 1000ºC, the high surface to volume ratios of ceramic-based aerogels are practically unparalleled. Yet realizing the full potential of aerogel-based thermal protection systems requires not only increased thermal stability, but also a broadened menu of stabilizing mechanisms. Here we will review our recent efforts to understand the impact of synthetic parameters and dopant chemistry on the evolution of zirconia-based ceramics. A broad parametric window for thermal stability is established, in which the more sensitive variable of composition has been probed. However, dopant chemistry is actually an aggregate for many closely related material parameters including surface energy, enthalpy of formation, each of which may contribute uniquely to aerogel destabilization. A truly predictive design framework for ceramic-based aerogels requires further decoupling of these underlying properties through systematic and comprehensive evaluation of thermodynamic properties.

aerogel↗
DOE OSTI2026

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗
DOE OSTI2021

Hydroxylpyromorphite, a mineral important to lead remediation: Modern description and characterization

Abstract Hydroxylpyromorphite, Pb5(PO4)3(OH), has been documented in the literature as a synthetic and naturally occurring phase for some time but has not previously been formally described as a mineral. It is fully described here for the first time using crystals collected underground in the Copps mine, Gogebic County, Michigan. Hydroxylpyromorphite occurs as aggregates of randomly oriented hexagonal prisms, primarily between about 20–35 μm in length and 6–10 μm in diameter. The mineral is colorless and translucent with vitreous luster and white streak. The Mohs hardness is ~3½–4; the tenacity is brittle, the fracture is irregular, and indistinct cleavage was observed on {001}. Electron microprobe analyses provided the empirical formula Pb4.97(PO4)3(OH0.69F0.33Cl0.06)Σ1.08. The calculated density using the measured composition is 7.32 g/cm3. Powder X-ray diffraction data for the type material is compared to data previously reported for hydroxylpyromorphite from the talc mine at Rabenwald, Austria, and from Whytes Cleuch, Wanlockhead, Scotland. Hydroxylpyromorphite is hexagonal, P63/m, at 100 K, a = 9.7872(14), c = 7.3070(10) Å, V = 606.16(19) Å3, and Z = 2. The structure [R1 = 0.0181 for 494 F>4σ(F) reflections] reveals that hydroxylpyromorphite adopts a column anion arrangement distinct from other members of the apatite supergroup due to the presence of fluorine and steric constraints imposed by stereoactive lone-pair electrons of Pb2+ cations. The F– anion sites are displaced slightly from hydroxyl oxygen anions, which allows for stronger hydrogen-bonding interactions that may in turn stabilize the observed column-anion arrangement and overall structure. Our modern characterization of hydroxylpyromorphite provides deeper understanding to a mineral useful for remediation of lead-contaminated water.

Geochemistry & Geophysics↗