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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Combined effects of emitter–emitter and emitter–plasmonic surface separations dictate photoluminescence enhancement in a plasmonic field

The brightness of an emitter can be enhanced by metal-enhanced fluorescence, wherein the excitonic dipole couples with the electromagnetic field of the surface plasmon. In this report, we experimentally map the landscape of photoluminescence enhancement (EF exp ) of emitters in a plasmonic field as a function of the emitter–emitter separation, s, and the emitter–plasmon distance, t. We use Au nanoparticles overcoated with inert spacers as plasmonic systems and CdSe/ZnS quantum dots (QDs) as an emitter bearing opposite surface charges. The t and s are varied by changing the spacer thickness and number density of QDs on the plasmonic surface, respectively. The electrostatic binding of emitters on the plasmonic surface and their number density are established by following the variation of zeta-potential. EF exp is high, when t is short and s is large; nevertheless, it decreases when the emitter–emitter interaction dominates due to plasmon assisted nonradiative processes. In the absence of a plasmonic field, the enhancement observed is attributed to environmental effects and is independent of s, confirming the role of the electric field. Indeed, the distance dependence of EF exp closely follows the decay of the plasmonic field upon dilution of the emitter concentration on nanoparticles’ surface (s = 18 nm). The QD–plasmon system is visualized in the framework of the Thomson problem, and classical electrodynamics calculations give the trends in t and s dependence of the photoluminescence. Being the first report on the simultaneous dependence of t and s on plasmon-enhanced photoluminescence, the results presented herein will open newer opportunities in the design of hybrid systems with a high brightness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Smartphone application for visualizing building air leakage

Annually, unwanted air leakage through building envelopes accounts for 4 quads of energy consumption in the United States, which translates to about 10% of total building energy consumption. Locating and sealing leakage sites is crucial fordecreasing building energy consumption. Smartphones are ubiquitous and contain sophisticated cameras and highperformance processors that could be employed to visualize air leakage using the background-oriented schlieren imaging technique, making leak detection cheaper and easier. This technique requires a textured and high-contrast background such as a brick or concrete masonry unit wall, a building air leak that has a temperature difference compared with the ambient air, and an imaging system. This work focuses on using smartphones as the imaging system to visualize air leakages. The paper discusses application development and the results of testing to determine leakage visualization performance as a function of leak temperature. The results show that leaks with a temperature difference greater than 16°C compared with the ambient air temperature were visualized using existing smartphones.

Boudreaux, Philip [ORNL] (ORCID:0000000229564665)↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Ultrabroadband Spacetime Nanoscopy of Terahertz Polaritons in a van der Waals Cavity

Guiding, storing, and processing light at the nanoscale hinges on understanding how polaritons — hybrid quasiparticles of light and matter — propagate and interfere in both space and time. This work introduces a synchrotron-based technique, SYnchrotron SpaceTimE Mapping (SYSTEM), which captures real-time evolution of polariton wave packets with ∼10 nm spatial and sub-100 fs temporal resolution across an ultrabroadband 5–50 THz range. Here, SYSTEM directly visualizes the creation, interference, and decay of multiple high-quality Fabry-Pérot phonon polariton cavity modes in an α-MoO 3 microcavity. These real-space, real-time observations reveal wave-packet dynamics and cavity resonances with record-high quality factors (Q ≈ 100) in the single-digit terahertz regime near 9 THz. SYSTEM thus offers a powerful and broadly applicable platform for probing and engineering ultraslow, deeply subwavelength polaritons, opening new avenues for tailoring light–matter interactions and advancing next-generation THz nanophotonic technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Exploratory Visual Analysis of Anomalous Runtime Behavior in Streaming High Performance Computing Applications

Online analysis of runtime behavior is essential for performance tuning in streaming scientific workflows. Integration of anomaly detection and visualization is necessary to support human-centered analysis, such as verification of candidate anomalies utilizing domain knowledge. In this work, we propose an efficient and scalable visual analytics system for online performance analysis of scientific workflows toward the exascale scenario. Here, our approach uses a call stack tree representation to encode the structural and temporal information of the function executions. Based on the call stack tree features (e.g., execution time of the root function or vector representation of the tree structure), we employ online anomaly detection approaches to identify candidate anomalous function executions. We also present a set of visualization tools for verification and exploration in a level-of-detailed manner. General information, such as distribution of execution times, are provided in an overview visualization. The detailed structure (e.g., function invocation relations) and the temporal information (e.g., message communication) of the execution call stack of interest are also visualized. The usability and efficiency of our methods are verified in a real-world HPC application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Mechanochromic Palettes Based on Optical Rotation in Cholesteric Liquid Crystal Elastomers for Visual Signalling

Mechanochromic photonic materials present promising prospects for enhancing diverse facets of human life. Cholesteric liquid crystal elastomers (CLCEs) present immediate mechanochromic properties, allowing their utility in various fields, including camouflages, textiles, and anticounterfeiting. Nonetheless, it is essential to provide differentiated color information corresponding to diverse types and degrees of deformation for practical use in real life. Herein, the mechanochromic responses of CLCEs are investigated using their optical rotation under compression and uniaxial stretching. The CLCEs exhibit the same reflection colors indistinguishable for compression and uniaxial stretching due to their contracted pitches. In contrast, the linearly polarized light passing through deformed CLCEs can be modulated by optical rotation, resulting in distinct transmission colors that can be discerned through mechanical deformations. A visual signaling system with color information derived from optical rotation is demonstrated. By comprehending and manipulating the mechanoresponsive behavior of CLCEs, these materials can be advanced into innovative solutions with enhanced functionality and performance, thereby broadening their applications for human life enhancement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Mechanochromic Palettes of Cholesteric Liquid Crystal Elastomers for Visual Signaling

Mechanochromic photonic materials present promising prospects for enhancing diverse facets of human life. Cholesteric liquid crystal elastomers (CLCEs) present immediate mechanochromic properties, allowing their utility in various fields, including camouflages, textiles, and anticounterfeiting. Nonetheless, it is essential to provide differentiated color information corresponding to diverse types and degrees of deformation for practical use in real life. Herein, we investigate the mechanochromic responses of CLCEs using their optical rotation under compression and uniaxial stretching. The CLCEs exhibit the same reflection colors indistinguishable for compression and uniaxial stretching due to their contracted pitches. In contrast, the linearly polarized light passing through deformed CLCEs can be modulated by optical rotation, resulting in distinct transmission colors that can be discerned through mechanical deformations. We demonstrate a visual signaling system with color information derived from optical rotation. By comprehending and manipulating the mechanoresponsive behavior of CLCEs, we can advance these materials into innovative solutions with enhanced functionality and performance, thereby broadening their applications for human life enhancement.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Optimization of Energy Flow Through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Diatoms are important oceanic photosynthetic microorganisms, responsible for approximately 20% of all carbon fixation on Earth. Additionally, diatoms are attractive metabolic engineering candidates. This investigation leveraged Systems Biology techniques to advance our understanding of and potential to engineer diatoms for bioproducts of societal interest, such as biofuels. Systems Biology combines computational and experimental techniques to understand an organism at the whole-cell level as well as characterize the interface between the organism and its environment. Our research resulted in major contributions to the understanding of systems-level light-driven metabolism. Diatoms have a unique evolutionary history. As a result, existing understanding of photosynthetic metabolism may not be applicable to diatom physiology. We generated, validated, and published a high-quality metabolic model of diatom metabolism facilitating novel insights into these unique organisms. Additionally, we overcame a consistent challenge in the modeling of photosynthetic organisms by integrating light into metabolic models. This result allows realistic assessments of how the light environment affects cell physiology and metabolism, a necessary advancement for bioengineering. Next, we generated several methodologies for integrating large-scale datasets with models of metabolism. These “big data” approaches begin to address a constant challenge in biology, which is translating data into information and understanding. Finally, we built web-based databases for housing, distributing, and visualizing systems-level biology data. These projects created an online ecosystem for sharing, analyzing, and characterizing metabolic information and is currently being used by public and private institutions. Photosynthetic bioengineering has the promise to create high-energy products with minimal inputs. Systems Biology has a long history of facilitating bioengineering of cellular metabolism. However, photosynthetic organisms are only sparsely present in this history. This project and its outputs have accelerated the application of proven Systems Biology techniques to light-driven metabolism. The resulting methodologies will accelerate public and private endeavors leveraging photosynthetic systems in the bioengineering of bioproducts such as biofuels or carbon sequestration.

09 BIOMASS FUELS↗
DOE OSTI2022

Voronoi Meshing to Accurately Capture Geological Structure in Subsurface Simulations

Mesh generation lies at the interface of geological modeling and reservoir simulation. Highly skewed or very small grid cells may be necessary to accurately capture the geometry of geological features, but the resulting poorly scaled or small grid cells can have a substantial negative impact on simulator accuracy and speed. One way to minimize numerical errors caused by gridding complex structures is to simulate on high-quality Voronoi meshes, which reduce grid orientation effects in fluid flow. This work presents a complete methodology to create Voronoi simulation grids, model fluid flow in complex geological systems, and visualize the results. A recently developed Voronoi meshing method that can automatically generate provably good unstructured meshes that conform to input surfaces creating closed volumes is used. Initially an analytical benchmark simulation is presented to validate the quality of the meshes and simulation results and demonstrate the superiority of simulation results using Voronoi meshes over flexed-hexahedral meshes on a domain with internal features. Next, meshes are created for test structures representing four of the most common geological features in the subsurface: layering, pinch-out, an interior lens that tapers to zero thickness on all sides and a fault with offset. Two benchmark flow simulations are run for each test structure. Finally, a realistic geological example for CO 2 injection into an anticline is simulated. Three realizations of the Voronoi mesh at the same resolution are generated for the simulations. Each mesh is highly refined near the injection wells and coarse in areas of less interest. These three meshes are used to model the CO 2 plume in the subsurface as it migrates to the top of the structure and then fills downward. Simulations on the meshes with randomly generated elements inside the input volumes each give slightly different fingering patterns for the viscous-unstable buoyant gas flow. The results presented in this work show a promising step towards utilizing fully automated Voronoi meshing for subsurface flow simulations in complex geology.

58 GEOSCIENCES↗
DOE OSTI2021

In vivo volumetric imaging of calcium and glutamate activity at synapses with high spatiotemporal resolution

Studying neuronal activity at synapses requires high spatiotemporal resolution. For high spatial resolution in vivo imaging at depth, adaptive optics (AO) is required to correct sample-induced aberrations. To improve temporal resolution, Bessel focus has been combined with two-photon fluorescence microscopy (2PFM) for fast volumetric imaging at subcellular lateral resolution. To achieve both high-spatial and high-temporal resolution at depth, we develop an efficient AO method that corrects the distorted wavefront of Bessel focus at the objective focal plane and recovers diffraction-limited imaging performance. Applying AO Bessel focus scanning 2PFM to volumetric imaging of zebrafish larval and mouse brains down to 500 µm depth, we demonstrate substantial improvements in the sensitivity and resolution of structural and functional measurements of synapses in vivo. This enables volumetric measurements of synaptic calcium and glutamate activity at high accuracy, including the simultaneous recording of glutamate activity of apical and basal dendritic spines in the mouse cortex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Smart soft contact lenses for continuous 24-hour monitoring of intraocular pressure in glaucoma care

Continuous monitoring of intraocular pressure, particularly during sleep, remains a grand challenge in glaucoma care. Here we introduce a class of smart soft contact lenses, enabling the continuous 24-hour monitoring of intraocular pressure, even during sleep. Uniquely, the smart soft contact lenses are built upon various commercial brands of soft contact lenses without altering their intrinsic properties such as lens power, biocompatibility, softness, transparency, wettability, oxygen transmissibility, and overnight wearability. We show that the smart soft contact lenses can seamlessly fit across different corneal curvatures and thicknesses in human eyes and therefore accurately measure absolute intraocular pressure under ambulatory conditions. We perform a comprehensive set of in vivo evaluations in rabbit, dog, and human eyes from normal to hypertension to confirm the superior measurement accuracy, within-subject repeatability, and user comfort of the smart soft contact lenses beyond current wearable ocular tonometers. We envision that the smart soft contact lenses will be effective in glaucoma care.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stimulus edges induce orientation tuning in superior colliculus

Orientation columns exist in the primary visual cortex (V1) of cat and primates but not mouse. Intriguingly, some recent studies reported the presence of orientation and direction columns in the mouse superficial superior colliculus (sSC), while others reported a lack of columnar organization therein. Using in vivo calcium imaging of sSC in the awake mouse brain, we found that the presence of columns is highly stimulus dependent. Specifically, we observed orientation and direction columns formed by sSC neurons retinotopically mapped to the edge of grating stimuli. For both excitatory and inhibitory neurons in sSC, orientation selectivity can be induced by the edge with their preferred orientation perpendicular to the edge orientation. Furthermore, we found that this edge-induced orientation selectivity is associated with saliency encoding. These findings indicate that the tuning properties of sSC neurons are not fixed by circuit architecture but rather dependent on the spatiotemporal properties of the stimulus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Imaging of a van der Waals spin-orbit torque system using spin ensembles in hBN

Recently, optically active spin defects embedded in two-dimensional (2D) van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science. Taking advantage of excellent solid-state integrability, this new class of spin defects can be readily arranged in nanoscale proximity to target materials, showing great promise for realizing in-situ quantum sensing of microscopic spin and charge behaviors in vdW heterostructures. Here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching and electric current distributions in an all-vdW spin-orbit torque (SOT) system. By visualizing variations of nanoscale magnetic stray field profile of room-temperature 2D magnet Fe 3 GaTe 2 under different SOT conditions, we show how the magnetic switching evolves from deterministic to stochastic behavior due to the interplay between spin orientations, anisotropy and Joule heating. Micromagnetic simulations rationalize our results well, revealing the role of field-like SOT in inhibiting thermal fluctuation driven stochastic switching and chaotic multi-domain competition. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights into material design, testing, and performance evaluation of next-generation vdW spintronic devices.

Imaging techniques↗
DOE OSTI2023

Ultrafast structural changes direct the first molecular events of vision

Vision is initiated by the rhodopsin family of light-sensitive G protein-coupled receptors (GPCRs). A photon is absorbed by the 11-cis retinal chromophore of rhodopsin, which isomerizes within 200 femtoseconds to the all-transconformation, thereby initiating the cellular signal transduction processes that ultimately lead to vision. However, the intramolecular mechanism by which the photoactivated retinal induces the activation events inside rhodopsin remains experimentally unclear. Here we use ultrafast time-resolved crystallography at room temperature to determine how an isomerized twisted all-trans retinal stores the photon energy that is required to initiate the protein conformational changes associated with the formation of the G protein-binding signalling state. The distorted retinal at a 1-ps time delay after photoactivation has pulled away from half of its numerous interactions with its binding pocket, and the excess of the photon energy is released through an anisotropic protein breathing motion in the direction of the extracellular space. Notably, the very early structural motions in the protein side chains of rhodopsin appear in regions that are involved in later stages of the conserved class A GPCR activation mechanism. Our study sheds light on the earliest stages of vision in vertebrates and points to fundamental aspects of the molecular mechanisms of agonist-mediated GPCR activation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗
DOE OSTI2019

Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

Chae, Junghoon↗