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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Distributed Sensor Fusion of Ground and Air Nodes Using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds

High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. This work collected data using a distributed radar and camera sensor framework during the NASA Advanced Air Mobility High Density Vertiplex project SUAS flight operation. Node locations include on an onboard SUAS, affixed to the Landing and Impact Research Facility with approximate 200ft elevation, on a tripod on first-floor roof with an approximate elevation of 20 ft, and on a tripod on a concrete pad that is approximately 4 ft above sea level. Each node includes camera, radar, and GPS.

Beyond Visual Line of Sight↗
NASA NTRS1994

A Permutation Engine Switching Node

We describe an asynchronous, strictly non-blocking crossbar node topology and distributed routing algorithm that is particularly suited to optoelectronic implementation.

Permutation engine permutation node topology routi↗
NASA NTRS2003

Smart Data Node in the Sky (SDNITS): communications system

In this paper we will discuss: the steps for adequately designing such a complex telecommunications system 'Smart Data Node In The Sky (SDNITS)'; algorithm development for this process; specifications to be levied on the interfacing subsystems; type of the system e.g., the usual Radio Frequency system or a laser communications system.

communication smart node technology system archite↗
DOE OSTI2026

Nanocluster Rearrangement Forms a Family of Ordered Cerium–Titanium Bimetallic Metal–Organic Frameworks with Three Different Nodes, Nanocavities, and Thermal Stabilities

Metal–organic frameworks (MOFs) provide a versatile platform for incorporating multiple metal ions within a single crystalline framework, yet achieving spatial and stoichiometric order in heterometallic nodes remains a synthetic challenge. Building on our previously reported, highly thermally stable Ce/Ti bimetallic MOF NU-3000, we identified and isolated two additional crystalline phases, NU-2998 and NU-2999, that arise from the same Ce/Ti nanocluster precursor under modified solvothermal conditions. Systematic variation of reaction temperature, time, solvent ratio, and modulator concentration directs the assembly of these distinct frameworks. Structural analysis and comprehensive characterization studies reveal that these MOFs each feature an unreported nodal geometry with nanocavities of different sizes. NU-2998 even adopts an unreported topology, denoted nui , that features an elongated pore spanning 4 nm. Together, these findings establish a synthesis route that starts with a nanocluster and ends with a set of bimetallic MOFs, offering a glimpse into the pathway-dependent assembly of multimetallic porous materials. Finally, we evaluated the thermal stability of each additional analogue and compared them to NU-3000, providing further insight into material stability. NU-3000 maintained the highest thermal stability and was evaluated as a catalyst for CO oxidation at elevated temperatures.

bimetallic MOFs↗
DOE OSTI2025

..delta..-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features

In this work, we present a ..delta..-learning approach for predicting the eigenvalues calculated with the hybrid functional HSE06 (..epsilon..nkHSE) for a set of metal and nitrogen doped graphene catalysts (MNCs) from Perdew-Burke-Ernzerhof (PBE) inputs. The model presented here incorporates electronic scalar features along with structural information in a graph neural network (GNN). In particular, the PBE eigenvalues for different bands and k-points and orbital-resolved projectors are combined with the applied potential as node-level features along with structural information within the Atomistic Line Graph Neural Network (ALIGNN) architecture. These features enable flexibility for systems with electrified interfaces, such as in electrocatalysts and achieves mean absolute error (MAE) of less than 0.1 eV. The machine learning model reported here achieves a strong generalization to left-out adsorbates (MAE = 0.074 eV) and leave-one-chemical-space-out (MAE = 0.08 eV) and completely left-out metals (MAE = 0.072 eV), confirming the robustness of the machine learning (ML) model in predicting ..epsilon..nkHSE.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Facile Generation of Active Sites in Nodes of Ni-MFU-4l Metal–Organic Framework for Hydrogenation Reaction

Metal–organic frameworks (MOFs) represent a well-defined class of materials capable of incorporating catalytically active sites for gas-phase catalysis. However, the reducing conditions of hydrogenation catalysis can lead to nanoparticle formation in MOFs, which can significantly diminish the catalytic activity of single-site metals and reduce the longevity of MOF-based hydrogen solutions. In this work, we present a straightforward approach to accessing catalytically active single metal sites in a robust Ni-MFU-4l MOF for gas-phase hydrogenation without the formation of Ni nanoparticles. By carefully tuning the local node chemistry through postsynthetic exchange of the terminal ligand coordinated to the Ni(II) centers in the MOF, from −Cl to −OH or −HCOO, we can readily generate Ni–H active species. We further demonstrate, using in situ pair-distribution function analysis, that these Ni–H sites are the sole catalytically active sites in the terminal ligand-exchanged counterparts, whereas nanoparticles readily form in the parent Ni-MFU-4l-Cl under otherwise identical catalytic conditions.

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

From Oxo to Oxyl to Biradical: Systematic Multireference Calculations of Methane Activation at MOF Nodes

Methane C–H activation at transition-metal sites often involves electronic structures that challenge conventional single-reference electronic structure descriptions. Although Kohn–Sham density functional theory (DFT) is widely used to study catalytic trends, its reliability for reactions involving strongly correlated species remains uncertain. Here we present a systematic multireference investigation of methane activation at metal–organic framework (MOF) node catalysts across the 3d transition-metal series. We introduce an automated workflow for active space selection to enable consistent application of multireference methods, including multiconfiguration pair-density functional theory and n-electron valence state perturbation theory, to these catalytic systems. These calculations show substantial static correlation in the C–H activation reaction step and predict activation barriers that differ from DFT by 30–70 kJ mol–1, with DFT often qualitatively disagreeing in barrier height trends across transition metals. Analysis of multireference wave functions shows that reactivity is governed by the electronic structure of the M–O moiety along a continuum from metal–oxo to oxyl radical and O biradical character. Increased oxygen-centered spin density and weakened M–O bonding are identified as descriptors of catalytic activity which correlate with lower activation barriers.

Wardzala, Jacob↗
DOE OSTI2025

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗
DOE OSTI2025

Type-II Weyl nodes, flat bands, and evidence for a topological Hall-effect in the new ferromagnet FeCr 3 Te 6

The interplay between linearly dispersing, or Dirac-like, and flat electronic bands, for instance, in the kagome ferromagnets, has attracted attention due to a possible interplay between topology and electronic correlations. Here, we report the synthesis, structural, electrical, and magnetic properties of a single-crystalline ferromagnetic compound, namely Fe 1/3 ⁢CrTe 2 or FeCr 3 ⁢Te 6 , which crystallizes in the 𝑃$\overline{3}$⁢𝑚⁢1 space group instead of the 𝐼⁢2/𝑚 previously reported for FeCr 2 ⁢Te 4 . Electronic band structure calculations reveal type-II Dirac nodes and relatively flat bands near the Fermi level (ε 𝐹 ). This compound shows onset Curie temperature 𝑇 c ≃ 120K, followed by an additional ferromagnetic transition near 𝑇 c2 ∼ 92.5K. Below 𝑇 c , FeCr 3 ⁢Te 6 displays a pronounced anomalous Hall effect, as well as sizable coercive fields that exceed 𝜇 0 ⁢𝐻=1 T at low 𝑇⁢s. However, a scaling analysis indicates that the anomalous Hall effect results from a significant intrinsic contribution, as expected from the calculations, and also from the extrinsic mechanism, i.e., scattering. The extrinsic contribution probably results from occupational disorder at the 1b Fe-site within the van der Waals gap of the CrTe 2 host. We also observe evidence for a topological Hall component superimposed onto the overall Hall response, suggesting the presence of chiral spin textures akin to skyrmions in this centrosymmetric system. Their possible presence will require experimental confirmation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Anomalous Hall effect emerging from field-induced Weyl nodes in SmAlSi

The intrinsic anomalous Hall effect (AHE) has been reported in numerous ferromagnetic Weyl semimetals. However, the AHE in the antiferromagnetic (AFM) or paramagnetic (PM) state of Weyl semimetals has rarely been observed experimentally. Different mechanisms have been proposed to account for the emergence of the AHE from different types of magnetic order. Here, in this Letter, we propose a new model that explains the observed AHE in both the AFM and PM states of the noncentrosymmetric Weyl semimetal SmAlSi. The newly proposed mechanism is based on magnetic-field-induced Weyl node evolution, which qualitatively explains the temperature dependence of the anomalous Hall conductivity, which displays unconventional power-law behavior in both the AFM and PM states of SmAlSi.

Gao, Yuxiang [Rice University, Houston, TX (United↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2024

M-node Polarization Control (MPC) v1.0

The M-node Polarization Control program interfaces with electronic polarization controllers and polarimeters to adjust and compensate for the polarization drift of light within optical fiber. The code implements a gradient ascent control loop to calibrate to a desired state of polarization. A key advantage of this program is that it provides a TCP socket interface that allows external clients to drive the calibration routines.

Kissel, Ezra↗
DOE OSTI2026

Quantum entanglement distribution coexisting with high-rate, broadband classical optical communications over a real-world fiber connecting remote, synchronized nodes

Compatibility with existing classical network infrastructure offers a scalable path towards deploying large-scale quantum networks. Here, we demonstrate O-band polarization-encoded quantum entanglement distribution over an installed 24.4-km fiber while coexisting with a state-of-the-art fully loaded C-band classical communications line system and a picosecond-level precision L-band synchronization signal. The classical system carries two 800-Gbps channels while the remainder of the C-band is filled with amplified spontaneous emission, as is standard for such state-of-the-art communications systems. We examine the spontaneous Raman scattering spectrum generated from this broadband C-band light and offer insights into wavelength allocation for O-band quantum channels. Optimal wavelength selection and narrow filtering enable well-preserved Bell state fidelity when coexisting with 21.4-dBm aggregate launch power across the C-band suitable for 36-Tbps transmission. To the best of our knowledge, this is the first implementation of entanglement-based quantum communications between two remote nodes coexisting with independent classical communications traffic. We demonstrate coexistence of quantum entanglement with ultra-high power levels and record classical bandwidth, offering promise for real-world entanglement-based networking integrated within high-capacity communications infrastructure.

Talcott, Gina M. [Northwestern U.] (ORCID:00000002↗
DOE OSTI2026

Airports as Energy Nodes (ÆNodes) Project Report

Aviation is experiencing an influx of electrified aircraft in the advanced air mobility (AAM) space. The goal of Airports as Energy Nodes (ÆNodes) is to evaluate the impact of AAM and other advanced aircraft adoption growth on the electrical system of existing small to medium hub regional airports to better prepare them for the future while also enhancing resiliency and helping surrounding communities. AAM small passenger air service encompasses vertical takeoff and landing (VTOL), general aviation (GA) and flight training, and nine to thirty passenger conventional aircraft for regional air mobility (RAM) applications all of which include electrical, hybrid, or hydrogen powered varieties. Some challenges that ÆNodes is trying to address include how to utilize existing airport infrastructure to meet the energy needs of AAM growth in a way that is robust to aid in the growth of AAM while also leveraging any airport-hosted energy assets for the surrounding community in emergency situations.

24 POWER TRANSMISSION AND DISTRIBUTION↗