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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancement of vibrationally assisted energy transfer by proximity to exceptional points, probed by fluorescence-detected vibrational spectroscopy

Emulation of energy transfer processes in natural systems on quantum platforms can further our understanding of complex dynamics in nature. One notable example is the demonstration of vibrationally assisted energy transfer (VAET) on a trapped-ion quantum emulator, which offers insights for the energetics of light harvesting. In this work, we expand the study of VAET simulation with trapped ions to a non-Hermitian quantum system comprising a 𝒫⁢𝑇-symmetric chromophore dimer weakly coupled to a vibrational mode. We first characterize exceptional points (EPs) and non-Hermitian features of the excitation energy transfer processes in the absence of the vibration, finding a degenerate pair of second-order EPs. Exploring the non-Hermitian dynamics of the whole system including vibrations, we find that energy transfer accompanied by absorption of phonons from a vibrational mode can be significantly enhanced near such a degenerate EP. Our calculations reveal a unique spectral feature accompanying the coalescing of eigenstates and eigenenergies that provides a unique approach to probe the degenerate EP by fluorescence-detected vibrational spectroscopy. Enhancement of the VAET process near the EP is found to be due to maximal favorability of phonon absorption at the degenerate EP, enabling multiple simultaneous excitations. Our work on improving VAET processes in non-Hermitian quantum systems paves the way for leveraging non-Hermiticity in quantum dynamics related to excitation energy transfer.

Non-Hermitian systems↗
DOE OSTI2025

Passive Environment-Assisted Quantum Communication with GKP States

Bosonic pure-loss channel, which represents the process of photons decaying into a vacuum environment, has zero quantum capacity when the channel’s transmissivity is less than 50%. Modeled as a beam splitter interaction between the system and its environment, the performance of bosonic pure-loss channel can be enhanced by controlling the environment state. We show that by choosing the ideal Gottesman-Kitaev-Preskill (GKP) states for the system and its environment, perfect transmission of quantum information through a beam splitter is achievable at arbitrarily low transmissivities. Our explicit constructions allow for experimental demonstration of the improved performance of a quantum channel through passive environment assistance, which is potentially useful for quantum transduction where the environment state can be naturally controlled. In practice, it is crucial to consider finite-energy constraints, and high-fidelity quantum communication through a beam splitter remains achievable with GKP states at the few-photon level.

Wang, Zhaoyou [Univ. of Chicago, IL (United States↗
DOE OSTI2024

Machine Learning (ML) Classifier to Assist Metadata Creation

The Atmospheric Radiation Measurement (ARM) Data Center is responsible for the timely collection, archival, and curation of science data products. These products are freely available through an online data repository. Metadata creation is paramount for scientific users to find and access over seven petabytes of atmospheric science data. The hierarchical metadata structure allows users to search for information at both broad and narrow levels. This project aims to leverage 30 years’ worth of manually created metadata to enable machine predictions of broad-term classifications from narrow-term descriptions. These classification predictions would assist metadata coordinators with their term selections. This paper discusses the cleaning and preprocessing of the training data, the pipeline developed to determine the best model for this task, and the creation of an API metadata classifier for ARM measurement metadata. Our results show that the Linear Support Vector Classification (LinearSVC) algorithm, along with the Term Frequency – Inverse Document Frequency (TF-IDF) vectorizer, is well-suited for our multi-class classification task. Lengthier input training data led to better results, and artificial balancing was unnecessary for this particular use case. This predictive classifier enhances efficiency in metadata creation, as well as supports greater consistency and accuracy in metadata tagging.

Collier, Hannah [ORNL] (ORCID:0000000341284292)↗
DOE OSTI2025

A VSC-HVDC-Assisted Black-Start Strategy in Bulk Power Systems a Case Study in San Diego

With the worldwide growth in deploying high-voltage direct current (HVDC) transmission systems, their ability to facilitate black-start (BS) restoration has been a research topic of interest. In this context, voltage source converter (VSC)-HVDC is regarded as a BS resource, and this paper proposes a VSC-HVDC-assisted parallel BS restoration strategy in bulk power systems. The proposed strategy consists of two stages: 1) determination of the VSC and generator startup sequence and 2) load restoration simulation. In the first stage, the entire blackout system is sectionalized into multiple subsystems. Each subsystem includes a VSC-HVDC station or traditional BS unit, it independently determines its generator startup timeline and the energization timelines for buses and lines. The second stage involves load restoration, conceptualized as a modified unit commitment problem, with the timelines established in the first stage work as critical inputs. The proposed BS restoration strategy is tested on the San Diego power system to simulate the 2011 Southwest blackout. The simulation results validate the effectiveness of using VSC-HVDC links as a BS resource which not only speeds up the restoration process but also reduces both energy and economic losses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Direct HCN synthesis via plasma-assisted conversion of methane and nitrogen

Hydrogen cyanide (HCN) is synthesized from ammonia (NH 3 ) and methane (CH 4 ) at ~1200°C over a Pt catalyst. Ammonia synthesis entails several complex, highly emitting processes. Plasma-assisted HCN synthesis directly from CH 4 and nitrogen (N 2 ) could be pivotal for on-demand HCN production. Here, we evaluate the potential of dielectric barrier discharge (DBD) N 2 /CH 4 plasma for decentralized catalyst-free selective HCN production. We demonstrate a single-step conversion of methane and nitrogen to HCN with a 72% yield at <300°C. HCN is favored at low CH 4 concentrations with ethane (C 2 H 6 ) as the secondary product. We propose a first-principles microkinetic model with few electron impact reactions. The model accurately predicts primary product yields and elucidates that methyl radical (·CH 3 ) is a common intermediate in HCN and C 2 H 6 synthesis. Compared to current industrial processes, N 2 /CH 4 DBD plasma can achieve minimal CO 2 emissions.

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

Role of slip in hydrogen-assisted crack initiation in Ni-based alloy 725

We conduct in situ tensile straining experiments to investigate the role of hydrogen and slip in crack initiation in nickel-based alloy 725. Our experiments reveal no tendency for hydrogen to enhance localized slip and no necessity of slip for crack initiation. We use electrochemical charging to introduce hydrogen into samples, melt extraction to measure hydrogen content, and digital image correlation to analyze localized plastic strains during in situ tensile tests in a scanning electron microscope. Cracks initiate both in regions with and without nearby localized slip. Moreover, the fraction of cracks initiating with no nearby slip is greater at higher hydrogen content. Slip-assisted crack initiation generally occurs at locations where intergranular slip is arrested, especially at intersections of slipping coherent twin boundaries with thin twin lamellae. Cracks that initiate without nearby slip occur at a wider variety of microstructural features, including inclusions, triple junctions, and surface flaws.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AI Design Assistant

SAND2025-01930O The AI Design Assistant uses ChatGPT to provide a natural language interface to airfoil analysis tools (XFOIL). Most of the code base is glue code, connecting XFOIL (a tool for analyzing airfoils) to the OpenAI interface. Among the more novel features are an airfoil geometry class and methods on how to extract detailed data from XFOIL. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Karcher, Cody↗
DOE OSTI2026

MADA: Multi-Agent Design Assistant

MADA (Multi-Agent Design Assistant) is a Large Language Model (LLM) powered multi-agent framework that coordinates specialized agents for complex design workflows. The system was designed for HPC workflows with the following agents in mind: 1) A Job Management Agent (JMA) launches and manages ensemble simulations on HPC systems, 2) a Geometry Agent (GA) generates meshes, and 3) an Inverse Design Agent (IDA) proposes new designs informed by simulation outcomes. Our framework reduces cumbersome manual workflow setup, and enables automated design exploration at scale. However, the software also enables users to rapidly create new multi-agent systems. Simply define new agents in a configuration file, giving each their own set of tools (via MCP), and then chat and prompt your new multi-agent system. Is

Gunnarson, BrianS [Lawrence Livermore National Lab↗
DOE OSTI2024

Subsurface Spectroscopy of Thermal Degradation Inside an Inert Plastic Bonded Explosive (PBX) Simulant Using Feedback-Assisted Wavefront Shaping

We characterize the subsurface thermal degradation of an inert analog of high-explosive molecular crystals (Eu:Y(acac) 3 (DPEPO)) (EYAD) embedded inside of a plastic bonded explosive simulant using feedback-assisted wavefront shaping-based fluorescence and Raman spectroscopies. This technique utilizes wavefront shaping to focus pump light inside a heterogeneous material onto a target particle, which significantly improves its spectroscopic signature. We find that embedding the EYAD crystals in the heterogeneous polymer results in improved thermal stability, relative to bare crystal measurements, with the crystal remaining fluorescent to >612 K inside of the heterogeneous material, while the bare crystal’s fluorescence is fully quenched by 500 K. We hypothesize that this improvement is due to the polymer restricting the effects of EYAD melting, which occurs at 400 K and is the primary mechanism for spectroscopic changes in the temperature range explored.

Anderson, Benjamin R.↗
DOE OSTI2025

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗
DOE OSTI2025

Synergistic effects of Al, Ga, and In doping on ZnO nanorod arrays grown via citrate-assisted hydrothermal technique for highly efficient and fast scintillator screens

To be used as efficient alpha particle scintillator in the fields of nuclear security, nuclear nonproliferation and high-energy physics, scintillator screens must have high light output and fast decay properties. While there has been a great deal of progress in scintillation efficiency, achieving fast decay time properties are still a challenge. In this work, the near band edge (NBE) UV luminescence and alpha particle induced scintillation properties of vertically aligned densely packed ZnO nanorods (NRs) doped with Al, Ga, and In have been thoroughly investigated. The high crystalline hexagonal wurtzite structure with a strong orientation through the c -axis plane (002) and aspect ratios in the range 13–22 have been observed for all ZnO NRs. Electron paramagnetic resonance (EPR) analysis exhibited paramagnetic signals at g ≈ 1.96 for all ZnO NRs. A cost effective green hydrothermal synthesis technique was employed to grow well-aligned NRs. Using citrate as an additive acting as a strong reducing agent in the solution during the crystal growth, defects on the surface are significantly suppressed, thereby enhancing the NBE UV emission. Significantly higher NBE UV emission was observed from the top surface of ZnO NRs in cathodoluminescence (CL) microscopy. Results show that citrate assisted donor doping of ZnO NRs not only reduces the defect emission and NBE self-absorption, but also induces fast decay time (~ 600–700 ps), which makes ZnO NRs a good candidate for fast alpha particle scintillator screens used in associated particle imaging for time and direction tagging of individual neutrons generated in D–T and D–D neutron generators.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ancilla-assisted process tomography of a deployed fiber link

We perform ancilla-assisted process tomography over a deployed 1.6 km fiber-optic link using Bayesian inference, confirming a stable, high-fidelity polarization channel. Overall, our approach should offer value for in situ analyses of entanglement networks.

Rahman, Arefur [Arizona State University]↗
DOE OSTI2025

Photon-magnon coupling using gain-assisted spoof-localized surface plasmons

Improving the photon-magnon coupling strength can be done by tuning the structure of microwave resonators to better interact with the magnon counterpart. Planar resonators accommodating unconventional photon modes beyond the half- and quarter-wavelength designs have been explored due to their optimized mode profiles and potentials for on-chip integration. Here, we designed and fabricated an actively controlled ring resonator supporting the spoof localized surface plasmons (LSPs), and implemented it in the investigation of photon-magnon coupling for hybrid magnonic applications. We demonstrated gain-assisted photon-magnon coupling with the YIG magnon mode under several different sample geometries. The achieved coupling amplification largely benefits from the high quality factor (Q-factor) due to the additional gain provided by a semiconductor amplifier, which effectively increases the Q-factor from a nearly null state (passive resonance) to more than 1000 for a quadrupole LSP mode. Our results suggest an additional control knob for manipulating photon-magnon coupled systems exploiting external controls of gain and loss.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Deployed quantum link characterization via Bayesian ancilla-assisted process tomography

The development of large-scale quantum networks requires reliable quantum channels, the quality of which can be quantified by the framework of quantum process tomography. Here, in this work, we leverage ancilla-assisted process tomography (AAPT) and Bayesian inference to probe a 1.6 km deployed fiber-optic link. We send one of the two polarization-entangled photons at Alice in one building to Bob in another, exploiting the local qubit as an ancilla system to characterize the corresponding quantum channel. Monitoring over a 24 h period returns a steady process fidelity of 97.6(1)%, while controllable spectral filtering with passbands from 0.025 to 4.38 THz finds fidelities that first increase, and then level off with bandwidth, suggesting both stable operation with time and minimal polarization mode dispersion. To our knowledge, these results represent the first AAPT of a deployed quantum link, revealing a valuable tool for in situ analysis of entanglement-based quantum networks.

Rahman, Arefur [Arizona State Univ., Tempe, AZ (Un↗
DOE OSTI2025

Automated tuning of a ring-assisted MZI-based interleaver for Kerr frequency combs

We demonstrate a compact ring-assisted Mach–Zehnder interferometer (RAMZI)-based silicon photonic interleaver with a 400 GHz free spectral range (FSR), featuring flat passbands exceeding a spectral range of 50 nm. Additionally, we introduce a novel, to the best of our knowledge, add-on structure and tuning method enabling automated compensation for fabrication imperfections, precise shaping of the RAMZI flat-top passbands, and alignment with Kerr comb lines. Experimental results have shown successful interleaving of eight channels of distributed-feedback (DFB) lasers as well as a 200 GHz Kerr comb, both achieving an extinction ratio of approximately 20 dB.

Wang, Songli (ORCID:0009000626380795)↗
DOE OSTI2024

Energy Efficiency Assistance for Galena Alaska

The City of Galena sits along the north bank of the Yukon River with no road access. Temperatures regularly reach negative 20°F in the winter and sunlight extends for up to 21 hours a day in the summer. Galena’s 400 residents, including members of the Koyukuk Athabascan culture, primarily use fuel oil and locally forested wood for heating and electricity generation. Because these resources are costly to obtain, local households have a high energy burden, spending 7% of their annual income on energy (averaging $4,000 per year). To reduce this burden, the City of Galena joined the U.S. Department of Energy’s (DOE) Remote Alaska Community Energy Efficiency (RACEE) Competition, where they received financial and technical assistance to implement energy savings solutions across their community, including building envelope improvements, solar panels, and LED lighting. Additionally, Galena installed new energy monitoring equipment to continue measuring the impacts of this work beyond the RACEE project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models

Collisional-radiative (CR) models describe the atomic processes in a plasma by tracking the population density in the ground and excited states for each charge state of the atom/ion. These models predict important plasma properties such as charge state distributions and radiative emissivity and opacity. Accurate descriptions of the CR balance of the plasma are essential in fusion whole device modeling, especially when significant impurities are introduced into the plasmas. In an integrated fusion plasma and CR simulation, a CR model, which is a high-dimensional stiff ODE, needs to be solved on each grid point in the configuration space, and can overwhelm the overall computational cost. In this work, we propose a machine-learning-based method that discovers a latent space and learns its corresponding latent dynamics, which can capture the essential physics to make accurate predictions at much lower online computational cost. The proposed approach is physics-assisted, due to its combination of a physical latent space and a data-driven latent space. It has been demonstrated that the proposed architecture can predict both the full-order CR dynamics and a physical quantity of interest accurately.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Microwave-Assisted Catalytic Conversion of Tar Using Iron Catalyst Doped with Ni/La/Ce

The goal of this research is to study the microwave-assisted catalytic dry reforming of toluene using Fe/Al2O3 as a catalyst in the presence of various dopants such as Ni/La/Ce in a conventional fixed-bed reactor. Initial studies show that both CO2 and toluene conversion can be achieved up to 85% at 500 °C. Higher toluene conversion and syngas production was achieved under microwave conditions compared to conventional thermal conditions. The pre and post-reaction catalyst characterizations were performed using X-ray diffraction analysis (XRD) and thermogravimetric analysis (TGA). The tar analysis was performed using gas chromatography–mass spectrometry (GC-MS). The product gas mainly consisted of H2, CO, methane, propane, ethylene, and ethane along with small amounts of benzene, and naphthalene. The production of benzene indicates the hydrodemethylation (HDM) process of toluene, which requires hydrogen to initiate the reaction. The reaction pathway was proposed based on the observed product gas.

Linge Gowda, Anitha Shankara↗