Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Image Transforms”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Infrared Optical Anisotropy in Quasi‐1D Hexagonal Chalcogenide BaTiSe 3

Polarimetric infrared (IR) detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy is discovered in quasi-1D narrow-bandgap hexagonal perovskite sulfides, A 1+x TiS 3 , specifically BaTiS 3 and Sr 9/8 TiS 3 . In these materials, the critical role of atomic-scale structure modulations in the unconventional electrical, optical, and thermal properties raises the broader question of the nature of other materials that belong to this family. To address this issue, for the first time, high-quality single crystals of a largely unexplored member of the A 1+x TiX 3 (X = S, Se) family, BaTiSe 3 are synthesized. Single-crystal X-ray diffraction determined the room-temperature structure with the P31c space group, which is a superstructure of the earlier reported P6 3 /mmc structure. The crystal structure of BaTiSe 3 features antiparallel c-axis displacements similar to but of lower symmetry than BaTiS 3 , verified by the polarization dependent Raman spectroscopy. Fourier transform infrared (FTIR) spectroscopy is used to characterize the optical anisotropy of BaTiSe 3 , whose refractive index along the ordinary (E ⊥ c) and extraordinary (E ‖ c) optical axes is quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence Δn ∼ 0.9, BaTiSe 3 emerges as a new candidate for miniaturized birefringent optics for mid-wave infrared to long-wave infrared imaging.

optical anisotropy↗
DOE OSTI2025

Catalyst-Vision (PEM Catalyst Layer Image Analysis Tool) [SWR-25-100]

Catalyst-Vision (PEM Catalyst Layer Image Analysis Tool) provides an advanced Python-based tool, primarily designed for use in a Jupyter/Colab notebook, for the quantitative morphological analysis of pre-segmented shapes. While developed for analyzing PEM catalyst layers from microscopy, its methodology is suitable for characterizing any grayscale object provided on a uniform white background. The tool uses a robust computer vision pipeline based on the Euclidean Distance Transform and skeletonization to accurately measure local thickness and tortuosity, providing a comprehensive characterization of an object's geometry and internal texture. If you find this code useful, please cite our preprint as: Chan, Ai-Lin and Hayden, Steven and Harvey, Steven P. and Smeaton, Michelle and Okrucky, Caleb and Watt, John and Ulična, Soňa and Spurgeon, Steven and Jungjohann, Katherine and Alia, Shaun, Mechanism-informed breakdown: understanding degradation by controlling voltage hold patterns in PEM water electrolyzers. Preprint (2025).

Spurgeon, Steven [National Laboratory of the Rocki↗
DOE OSTI2022

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Topological Hall Effect in Antiferromagnetic Co-Doped Fe 3 GaTe 2

Fe 3 GaTe 2 is van der Waals (vdW) ferromagnet with a Curie temperature T C ranging from 350 K to 380 K, followed upon cooling by a ferrimagnetic transition near room temperature. Substituting Fe with Co was previously reported to induce antiferromagnetism (AFM) at a Co fraction dependent Néel temperature TN. In this work, we confirm the overall phase diagram of the Fe 3-x Co x GaTe 2 series as a function of x and temperature via magnetization and electrical transport measurements. For x ≥ 0.6 the Hall effect is observed to mimic the magnetization as the AF ground state is suppressed by the external magnetic field via a metamagnetic transition, thus displaying an anomalous Hall response. At low temperatures, we also observe a pronounced topological Hall signal peaking at μ 0 H = 4 T, or within the metamagnetic transition region of fields. This observation points to the presence of magnetic field-induced chiral spin textures, such as skyrmions upon approaching magnetization saturation. Remarkably, magnetic force microscopy (MFM) reveals the emergence of nearly circular magnetic domains, with diameters on the order of 100–200 nm, within the antiferromagnetic phase. Here, a detailed analysis of the MFM images indicates that the topological Hall effect is closely linked to the field-induced stabilization of magnetic domain structures, likely exhibiting chiral textures. This observation suggests the possible formation of skyrmions already in the AFM phase, i.e., AFM skyrmions, that evolve into ferromagnetic (FM) ones upon increasing the magnetic field. Consequently, Co-doped Fe 3 GaTe 2 might provide a platform to investigate the transformation of skyrmions, initially coupled antiferromagnetically into ferromagnetic skyrmions, and to explore its impact on the topological and skyrmion Hall effects.

anomalous Hall effect↗
DOE OSTI2025

A tweezer array with 6,100 highly coherent atomic qubits

Optical tweezer arrays have transformed atomic and molecular physics, now forming the backbone for a range of leading experiments in quantum computing, simulation and metrology. Typical experiments trap tens to hundreds of atomic qubits and, recently, systems with around 1,000 atoms were realized without defining qubits or demonstrating coherent control. However, scaling to thousands of atomic qubits with long coherence times and low-loss and high-fidelity imaging is an outstanding challenge and critical for progress in quantum science, particularly towards quantum error correction (QEC). Here we experimentally realize an array of optical tweezers trapping more than 6,100 neutral atoms in around 12,000 sites, simultaneously surpassing state-of-the-art performance for several metrics that underpin the success of the platform. Specifically, while scaling to such a large number of atoms, we demonstrate a coherence time of 12.6(1) s, a record for hyperfine qubits in an optical tweezer array. We show room-temperature trapping lifetimes of about 23 min, enabling record-high imaging survival of 99.98952(1)% with an imaging fidelity of more than 99.99%. We present a plan for zone-based quantum computing and demonstrate necessary coherence-preserving qubit transport and pick-up/drop-off operations on large spatial scales, characterized through interleaved randomized benchmarking. Our results, along with recent developments, indicate that universal quantum computing and QEC with thousands to tens of thousands of physical qubits could be a near-term prospect.

atomic and molecular physics↗
DOE OSTI2021

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantum Vision Transformers for Quark–Gluon Classification

We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters.

Comajoan Cara, Marçal (ORCID:0009000126263752)↗
DOE OSTI2025

Strain-driven oxygen vacancy ordering in LaNiO 3 thin films revealed by integrated differential phase contrast imaging in scanning transmission electron microscopy

Rare-earth nickelates, such as LaNiO 3 (LNO), exhibit complex electronic properties, with ordered oxygen vacancies (OOV) influencing conductivity and magnetic behavior. We investigate the structural stability of strain-induced OOV phases in LNO thin films grown on SrTiO 3 substrates and the impact of Ruddlesden–Popper (RP) faults. Using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and integrated differential phase contrast (iDPC) STEM imaging, we conducted atomic-scale structural and compositional analyses of OOV. Geometric phase analysis (GPA) was employed to measure the strain in fault-free and RP fault regions, while density functional theory (DFT) calculations explored different OOV arrangements in the LNO phase. Simulated iDPC-STEM imaging of energy-stabilized structures was performed to correlate with experimental results. Here, our findings reveal superstructure modulation in the chemical composition and atomic-scale lattice structure in LNO, primarily due to the formation of the OOV in Ni–O layers of the LaNiO 2.5 phase. The out-of-plane compressive strain of about 2% stabilizes this phase, reducing the strain, diminishing OOV, and transforming them into LNO.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Conformer-specific photochemistry imaged in real space and time

Conformation-dependent dynamics play an important role in organic chemistry syntheses such as electrocyclic reactions, as well as in biological processes such as protein folding. However, current time-resolved experimental methods struggle to distinguish conformers from each other, and conformational isomerism is usually analyzed through reactant and product distributions. Using a combination of mega–electron volt ultrafast electron diffraction and quantum wave packet simulations, Champenois et al. directly followed the photochemical electrocyclic ring opening of the molecule α-phellandrene with femtosecond time resolution and confirmed that the transformation of a specific molecular conformer follows the famous Woodward-Hoffmann rules. The proposed method is potentially a powerful tool to follow conformer specificity in various organic and biological systems in real time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Flow structures of the cross-flow over a five-layer helically coiled steam generator geometry

The helically-coiled heat exchanger (HCSG) offers advantages over straight tubes such as compactness in geometry, increased heat transfer coefficients, and capability to absorb thermal expansion. In this study, the time-resolved velocity field data of shell-side crossflow over a five-layer helically coiled steam generator geometry was obtained using Particle Image Velocimetry (PIV) between adjacent rods in four regions with up to 5000 frames per second (5 kHz) at approximately Re u = 3600. The slant rod bundle created different flow patterns in different regions. Proper Orthogonal Decomposition (POD) revealed high-energy-mode flow structures. Due to the confined flow channel geometry, the large flow structures were not advected resulting in no strong POD mode paring. Continuous Wavelet Transform (CWT) visualized the characteristics of flow fluctuation in time and frequency domain simultaneously, which ranged in Strouhal number, S u , between 0.03 and 1.17. In the wake regions of the rods, flows changed in both pattern and magnitude over time. Once a pattern formed, it continued for relatively shorter than straight tube arrays, before changing the pattern again. The histograms of the POD time coefficients presented this multimodal flow characteristics. The current study focuses more on whole flow field analyses in the selected regions rather than local point-wise analyses. Finally, this study revealed that various Strouhal numbers can exist in the helically coiled heat exchanger geometry, which was not observed in straight tube arrays. In particular, Strouhal numbers smaller than 0.1 do not seem to be caused by vortex shedding but seem to be the result of the combination of complex geometry and possible multimodal trends. Based on the current observation, further works would involve intensive instantaneous point-wise analyses on the interactions between important locations such as separation points, stagnation points, shear layers, etc.

42 ENGINEERING↗
DOE OSTI2023

Automated piezoresponse force microscopy domain tracking during fast thermally stimulated phase transition in CuInP 2 S 6

Real-time tracking of dynamic nanoscale processes such as phase transitions by scanning probe microscopy is a challenging task, typically requiring extensive and laborious human supervision. Smart strategies to track specific regions of interest (ROI) in the system during such transformations in a fast and automated manner are necessary to study the evolution of the microscopic changes in such dynamic systems. In this work, we realize automated ROI tracking in piezoresponse force microscopy during a fast (≈0.8 °C s –1 ) thermally stimulated ferroelectric-to-paraelectric phase transition in CuInP 2 S 6 . We use a combination of fast (1 frame per second) sparse scanning with compressed sensing image reconstruction and real-time offset correction via phase cross correlation. The applied methodology enables in situ fast and automated functional nanoscale characterization of a certain ROI during external stimulation that generates sample drift and changes local functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Thermal cycling-driven microstructural changes of eutectic Al–Si phase change materials in SS304 containers revealed by multi-modal imaging

Aluminum-based Al–Si alloys are widely used as phase change materials (PCMs) in thermal energy storage (TES) systems owing to their high volumetric latent heat and superior thermal conductivity. However, their long-term reliability is limited by degradation processes that remain insufficiently understood. In this work, we employ a multimodal, correlative characterization framework to systematically resolve the degradation behavior of eutectic Al–Si PCMs in contact with SS304 containers under repeated thermal cycling. By integrating high-resolution electron microscopy, three-dimensional X-ray fluorescence (3D XRF) imaging, and differential scanning calorimetry (DSC), we directly link spatially resolved compositional and microstructural evolution to changes in thermophysical properties. The correlative analysis reveals that elemental leaching of Fe, Cr, and Ni from the stainless-steel container into the PCM drives the formation of intermetallic compounds (IMCs) both at the interface and within the bulk PCM, leading to pronounced compositional heterogeneity. These interfacial reactions and diffusion-induced transformations progressively destabilize the Al–Si eutectic, reducing the effective phase-transforming fraction. Consistent with these observations, DSC measurements show a decrease in melting temperature and latent heat of fusion with thermal cycling. These results underscore the critical influence of interfacial reactions and material compatibility on the stability, durability, and overall performance of Al–Si-based TES systems.

25 ENERGY STORAGE↗
DOE OSTI2025

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2022

Iron acquisition and mineral transformation by cyanobacteria living in extreme environments

Iron is an essential micronutrient for most living organisms, including cyanobacteria. These microorganisms have been found in Earth's driest polar and non-polar deserts, including the Atacama Desert, Chile. Iron-containing minerals were identified in colonized rock substrates from the Atacama Desert, however, the interactions between microorganisms and iron minerals remain unclear. In the current study, we determined that colonized gypsum rocks collected from the Atacama Desert contained both magnetite and hematite phases. A cyanobacteria isolate was cultured on substrates consisting of gypsum with embedded magnetite nanoparticles. Transmission electron microscopy imaging revealed a significant reduction in the size of magnetite nanoparticles due to their dissolution, which occurred around the microbial biofilms. Concurrently, hematite was detected, likely from the oxidation of the magnetite nanoparticles. Higher cell counts and production of siderophores were observed in cultures with magnetite nanoparticles suggesting that cyanobacteria were actively acquiring iron from the magnetite nanoparticles. Magnetite dissolution and iron acquisition by the cyanobacteria was further confirmed using large bulk magnetite crystals, uncovering a survival strategy of cyanobacteria in these extreme environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Signatures of two-dimensional superconductivity emerging within a three-dimensional host superconductor

Significance We report signatures of a new phenomenon wherein a hidden “interdimensional” order emerges from a bulk three-dimensional superconductor tuned to approach a phase transition to an insulator. Prior electrical transport measurements found that the superconductor–insulator transition of BaPb 1− x Bi x O 3 demonstrated critical scaling behavior but inexplicably required parameters consistent only with two dimensions. Here, we directly image the three-dimensional superconducting state as it approaches an insulating phase and discover that signatures of a two-dimensional granular superconducting phase spontaneously emerge at the transition. Moreover, the properties of this phase precisely match the theory of emergent electronic granularity specific to two-dimensional materials. These findings posit that a three-dimensional superconductor electronically reorganizes itself into a two-dimensional granular superconductor before ultimately transforming to an insulator.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Activating Layered Metal Oxide Nanomaterials via Structural Engineering as Biodegradable Nanoagents for Photothermal Cancer Therapy

Layered metal oxides including MoO 3 and WO 3 have been widely explored for biological applications owing to their excellent biocompatibility, low toxicity, and easy preparation. However, they normally exhibit weak or negligible near-infrared (NIR) absorption and thus are inefficient for photo-induced biomedical applications. Herein, the structural engineering of layered MoO 3 and WO 3 nanostructures is first reported to activate their NIR-II absorption for efficient photothermal cancer therapy in the NIR-II window. White-colored micrometre-long MoO 3 nanobelts are transformed into blue-colored short, thin, defective, interlayer gap-expanded MoO 3–x nanobelts with a strong NIR-II absorption via the simple lithium treatment. The blue MoO 3–x nanobelts exhibit a large extinction coefficient of 18.2 L g –1 cm –1 and high photothermal conversion efficiency of 46.9% at 1064 nm. After surface modification, the MoO 3–x nanobelts can be used as a robust nanoagent for photoacoustic imaging-guided photothermal therapy to achieve efficient cancer cell ablation and tumor eradication under irradiation by a 1064 nm laser. Importantly, the biodegradable MoO 3–x nanobelts can be rapidly degraded and excreted from body. We report the study highlights that the structural engineering of layered metal oxides is a powerful strategy to tune their properties and thus boost their performances in given applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Time series methods for the analysis of soundscapes and other cyclical ecological data

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

54 ENVIRONMENTAL SCIENCES↗