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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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DOE OSTI2024

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

Johnson, Andrew E.↗
DOE OSTI2025

HunStat2 – a simple and low-cost potentiostat with electrochemical impedance spectroscopy capability

We have developed a low-cost (30 USD), simple do-it-yourself (DIY) potentiostat with cyclic voltammetry (CV), open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) capability. The HunStat2 potentiostat is based on Analog Devices' AD5941 Analog Front End chip, which significantly simplifies the construction of potentiostats for both direct and alternating current (DC and AC, respectively) techniques. Interested readers are provided with circuit diagrams and a bill of materials to build the potentiostat on their own. In addition, control software is also provided free of charge. The software enables acquisition and visualization of data. In summary, HunStat2 introduces a simple and low-cost DIY potentiostat recommended for both analytical and educational purposes.

Vamos, Istvan [Lajos Petrik Vocational Chemistry S↗
DOE OSTI2024

Faraday: A High-temperature Electrolysis Data Explorer

Faraday is a high-temperature electrolysis data visualization tool, which reveals the performance of various button cells under test conditions. These tests and the resulting analytics on their data constitute a state of the industry as the US Department of Energy pushes for the production of hydrogen. Faraday leverages the Idaho National Laboratory's DeepLynx data warehouse to standardize and query button cell data. Faraday programmatically accesses this data in DeepLynx by traversing the schema, represented by a custom ontology. The user interface queries DeepLynx for timeseries data associated with specific button cells in the warehouse, and renders them using JavaScript charts. Additional charting and data analysis techniques are made possible by an auxiliary Python server.

Woodruff, Nathan↗
DOE OSTI2026

AIF for Vis (Active Inference for simulating human interpretation of data visualization) [SWR-26-084]

AIF for Vis contains the Active Inference models and analysis scripts used to study a simple visualization-interpretation task: estimating the average value of two bars in a bar chart. The work is a proof of concept for translating hypothesized cognitive strategies into executable, inspectable process models. We implement two idealized strategies inspired by dual-process accounts of visualization-aided decision making: *Fast model: a compressed, heuristic strategy that estimates the visual midpoint of the two bars and maintains a single belief over their average. *Slow model: a sequential, analytic strategy that estimates the two bar heights separately and maintains them in working memory before computing an average. Both models use a common Active-Inference-inspired framework for sequential perception, belief updating, action selection, and reporting. Their different internal representations produce distinct predicted vulnerabilities: *the Fast model is more susceptible to tick-salience bias; *the Slow model is more susceptible to working-memory decay. The repository includes the model implementations, scripts used for the experiments reported in the paper, precomputed trial-level results, and plotting scripts.

Goldwyn, Harrison [National Laboratory of the Rock↗
DOE OSTI2024

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗
DOE OSTI2025

Immersive Analytics in Critical Spatial Domains: From Materials to Energy Systems

Immersive analytics (IA) leverages virtual reality, augmented reality, and mixed reality to transform how users interact with complex datasets across domains such as science, industry, and education. These immersive technologies offer spatial and multimodal environments that foster intuitive exploration, but they also introduce challenges related to cognitive load, interface design, and system performance. Here, this article presents a comprehensive review of visualization techniques, interaction models, and multimodal inputs utilized in IA. Drawing on case studies in scientific visualization, industrial training, and educational communication, we examine both the potential and limitations of current systems. Finally, we propose future research directions, focusing on real‐time collaboration, adaptive user interfaces, and scalable data exploration strategies to advance the field.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Liquid-to-gas transfer of sodium in a liquid cathode glow discharge

Abstract Plasma-liquid interactions have been extensively studied with a focus on the transport of reactive species from the plasma to the liquid phase and their induced liquid phase chemistry and resulting applications. While solute transfer from the liquid to the gas phase in plasmas has been widely used in analytical chemistry, the underlying processes remain relatively unexplored. We report spatially and temporally resolved absolute density measurements of sodium in a plasma with a NaCl solution cathode using two-photon absorption laser induced fluorescence (TaLIF). The observed non-linear increase in sodium density with solution conductivity is shown to correlate with droplet generation as visualized by Mie scattering. The findings are explained by droplet generation by electrospray induced by Taylor cone formation as underpinning mechanism for the introduction of sodium in the plasma. An analytical sheath model combined with a scaling law shows an increase in electric field force with solution conductivity that is consistent with the observed non-linear increase in sodium density in the plasma with solution conductivity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

OpenSAMPL: An Open Source Library for Timing and Synchronization Measurements and Analytics

Today's power grid operators are implementing timing and synchronization solutions that provide resilience to Global Navigation Satellite System (GNSS) vulnerabilities. These vendor-specific solutions often come with additional software applications that are designed to monitor that vendor's synchronization performance data. However, resilient timing architectures often resulting in multi-vendor solutions, including approaches that blend terrestrial clocks with space-based subscription services. In such an environment, collecting, analyzing, and visualizing data from a variety of sources within a single platform was heretofore not possible. To address this need, the US Department of Energy's Center for Alternative Synchronization and Timing (CAST) developed OpenSAMPL, the Open Synchronized Analytics and Monitoring Platform, an open-source Python framework for processing, loading, and observing clock measurement data from distributed devices. OpenSAMPL enables the ingestion of diverse clock-probe sources into a scalable time-series database and applies robust analytics. OpenSAMPL currently supports two vendor data pipelines, and will be extended to more in the near future, enabling seamless monitoring of a variety of timing and synchronization devices in a common environment.

Grant, Josh [ORNL] (ORCID:0000000163475060)↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
DOE OSTI2023

Electron Microscopy Studies of Soft Nanomaterials

This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. Here, the even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

Imaging↗
DOE OSTI2024

Stress field measurements using quantitative schlieren

Quantitative schlieren analysis is extended here to optically transparent solids in quasi-static and dynamic experiments to measure stress distributions. The quasi-static experiments in polymethyl methacrylate (PMMA) compared refraction angles and stress gradients calculated from schlieren images to the analytical Flamant solution of a line load on a half-space. The quantitative schlieren measurements of the stress field in the thin sample with a load compared well to the analytical solution. The analysis method was then extended to explosive induced shock waves in PMMA. The explosive induced response of PMMA was experimentally studied using high-speed schlieren to visualize the shock propagation in conjunction with Photon Doppler Velocimetry (PDV) to record surface velocity histories. The stress state estimated from the schlieren images was compared to the stress calculated from the PDV measurements. High-speed imaging limitations caused the shock wave to not be fully resolved in the images, but was resolved in the PDV measurement. The stress state behind the shock calculated from the high-speed images followed a similar trend to the stress calculated from the PDV measurements.

Physics↗
DOE OSTI2026

CRADA Number NFE-24-10110 with Qubit Engineering Inc. (CRADA Final Report)

Over the past year, the Qubit Engineering team has pushed the frontiers of power‑grid optimization, working in close collaboration with Oak Ridge National Laboratory (ORNL) and the Tennessee Valley Authority (TVA). Their progress is reflected in three newly submitted conference papers, “Unified Relational GNN Architecture for AC Optimal Power Flow Calculations in Electric Grids,” “Graph‑Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical‑Power Networks,” and “Enhanced Power‑Grid Maintenance Planning and Quantum‑Inspired Combinatorial Prospects.” These publications showcase state‑of‑the‑art graph‑neural‑network methods for AC‑OPF and novel quantum‑inspired heuristics for maintenance scheduling. Beyond the academic results, the Qubit team has converted the research into two production‑grade tools built on TVA data: Neuro‑Grid, an AI‑driven power‑flow simulator that provides instant, interactive full‑grid load‑flow visualizations, and Quanta‑Grid, a quantum‑inspired maintenance‑scheduling engine to support logistics optimization for power utilities. Together, these advances demonstrate how Qubit’s partnership with ORNL and TVA is delivering practical, physics‑grounded analytics for next‑generation grid management.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification

Contour trees offer an abstract representation of the level set topology in scalar fields and are widely used in topological data analysis and visualization. However, applying contour trees to large-scale scientific datasets remains challenging due to scalability limitations. Recent developments in distributed hierarchical contour trees have addressed these challenges by enabling scalable computation across distributed systems. Building on these structures, advanced analytical tasks—such as volumetric branch decomposition and contour extraction—have been introduced to facilitate large-scale scientific analysis. Despite these advancements, such analytical tasks substantially increase memory usage, which hampers scalability. In this paper, we propose a pre-simplification strategy to significantly reduce the memory overhead associated with analytical tasks on distributed hierarchical contour trees. We demonstrate enhanced scalability through strong scaling experiments, constructing the largest known contour tree—comprising over half a trillion nodes with complex topology—in under 15 minutes on a dataset containing 550 billion elements.

Li, Mingzhe [University of Utah]↗
DOE OSTI2025

Uniform secondary current distribution at disk electrodes under Tafel kinetics enabled by concentric current-shielding rings

Non-uniform secondary current distribution at rotating disk electrodes (RDE) is a common problem when using resistive electrolyte media or large applied currents. In a recent publication, we have shown that auxiliary electrodes such as the ring of a rotating ring disk electrode (RRDE) can help suppress current non-uniformities at the disk enabling reliable electroanalytical measurements. However, this previous work considered linear kinetics where current distribution non-uniformities were moderate. In the present contribution, we consider current distribution non-uniformities encountered under Tafel kinetics. We show, for the case of 2,5-dihydroxy-1,4-benzoquinone (DHBQ) reduction, that optimally chosen ring conditions serve to provide effective shielding at the disk edge rendering the overall disk current distribution to be uniform. Numerical modeling and scaling analysis (using the Wagner number) are presented to aid a user in determining the optimal ring current density for achieving uniform disk current distribution under Tafel kinetics. This approach is especially useful when studying soluble-soluble redox transitions for which, unlike deposit distribution in electrodeposition, the current distribution non-uniformity is not visually apparent.

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

Teaching Nonradiative Transitions with MATLAB and Python

Nonradiative transitions are changes in energy states of atoms, ions, or molecules that do not involve the emission or absorption of photons. Despite their importance in understanding luminescent properties and photochemical reaction mechanisms, nonradiative transitions are rarely given more than a qualitative overview in undergraduate and even graduate physical chemistry curricula. To supplement the coverage of nonradiative transition topics, we provide here a set of active learning exercises to help students develop an intuitive understanding of the factors that determine the rate of nonradiative transitions. Here, we start by outlining the theoretical background through the formulations of the Franck–Condon factor and its relation to the rate of nonradiative transition. We then introduce three teaching modules, with associated MATLAB and Python codes, to explore how (1) the excited state nuclear displacement, (2) the electronic energy gap between excited and ground state and (3) the excited/ground state vibrational mode frequencies affect the magnitude of the Franck–Condon factor and thereby the rate of nonradiative transitions. The wave function overlap plots that accompany all teaching modules provide direct visualization of the effect of input parameters on the magnitude of Franck–Condon overlap integral.

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

Microscopy X-ray imaging enriched with small angle X-ray scattering for few nanometer resolution reveals shock waves and compression in intense short pulse laser irradiation of solids

Understanding how laser pulses compress solids into high-energy-density states requires diagnostics that simultaneously resolve macroscopic geometry and nanometer-scale structure. Here we present a combined X-ray imaging (XRM) and small-angle X-ray scattering (SAXS) approach that bridges this diagnostic gap. Using the Matter in Extreme Conditions end station at LCLS, we irradiated 25 μm copper wires with 45 fs, 0.9 J, 800 nm pulses at 3.5 × 10 19 W/cm 2 while probing with 8.2 keV XFEL pulses. XRM visualizes the evolution of ablation, compression, and inward-propagating fronts with ∼ 200 nm resolution, while SAXS quantifies their nanometer-scale sharpness via the time-resolved evolution of scattering streaks. The joint analysis reveals that an initially smooth compression steepens into a nanometer-sharp shock front after t sh ≈ (18 ± 3) ps, consistent with an analytical steepening model and hydrodynamic simulations. The front reaches a velocity of c sh ≈ 25 k m / s and a lateral width of several tens of microns, demonstrating direct observation of shock formation and decay at solid density for the first time with few-nanometer precision. This integrated XRM–SAXS method establishes a quantitative, multi-scale diagnostic of laser-driven shocks in dense plasmas relevant to inertial confinement fusion, warm dense matter, and planetary physics.

Kluge, Thomas [Helmholtz-Zentrum Dresden-Rossendor↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗