Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “multimodal imaging”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
DOE OSTI2022

First Multimodal, Three-Dimensional, Image-Guided Total Marrow Irradiation Model for Preclinical Bone Marrow Transplantation Studies

Total marrow irradiation (TMI) has significantly advanced radiation conditioning for hematopoietic cell transplantation in hematologic malignancies by reducing conditioning-induced toxicities and improving survival outcomes in relapsed/refractory patients. However, the relapse rate remains high, and the lack of a preclinical TMI model has hindered scientific advancements. To accelerate TMI translation to the clinic, we developed a TMI delivery system in preclinical models.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Characterization of Electronic Stress-Induced Changes in Multilayer MoS 2

Transition metal dichalcogenides like molybdenum disulfide (MoS 2 ) are compelling for next-generation electronic devices. In this work, we investigate the impact of electronic stress on MoS 2 to illustrate that observational and phenomenological information on multiple devices can be useful to describe changes in the device, and caution against the rationalization of paltry results as representative or correlative to device behavior. Here, we stress MoS 2 by applying a sustained 20 V DC bias to study the material’s response. Post-stress electronic characterization revealed nonuniform shifts in current–voltage (I–V) behavior alongside microscale changes. Complementary mechanical, spectroscopic, and scanning microwave impedance measurements showed that stress-induced features locally modulate stiffness, surface potential, Raman intensity, and charge carrier density. We correlated I–V behavior with morphological features (wrinkles, tears, folds, height) and device-level geometry (MoS 2 overlap with electrodes, channel area, contact length) on 50 test structures across five chips to move beyond anecdotal conclusions. We found no universal correlations before DC stress. However, device-level geometry was correlated with I–V behavior after DC stress, suggesting that electrode contacts play a more dominant role than morphology in determining performance. Delamination and thinning induced by DC stress led to localized reductions in charge carrier density within the affected regions. Further, delamination and thinning appear to map to I–V device performance in a few samples, but the correlation is lost when a larger sample size is considered. This suggests significant sample-to-sample variability in surface electronic states of the test structures. We also discuss how environmental factors introduced during fabrication may contribute to the observed heterogeneous device response. Progress will require high-resolution, multimodal analysis across many samples constructed under controlled, clean conditions. By building data sets that capture variability, we can better identify the true drivers of performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical Insights into the Molecular Composition of Organic Aerosols in the Urban Region of Houston, Texas

Molecular functional groups, such as organosulfates (CHOS) and organonitrates (CHNO) are important tracers for field observations of secondary organic aerosols (SOA). While CHOS and CHNO are prevalent in the atmosphere, there is a lack of knowledge regarding daily and day- and night-time variations in these species in the urban atmosphere. Meteorological factors such as wind speed/direction, relative humidity (RH), and temperature can influence the formation of CHOS/CHNO. To investigate these trends, we utilized multimodal chemical imaging and advanced high resolution mass spectrometry techniques to acquire particle speciation and molecular formulas (MFs) associated with day and night sampling periods. Back trajectory analyses revealed the oceanic influence of southern wind airmasses in later June sampling periods with organic fractions <10%. Conversely, northern winds in early June sampling periods contributed to the episodic emergence of extremely low volatile organics (ELVOCs) and organic factions up to 41%. The observed unique MFs to June 3 (223 MFs) and to June 4 (144 MFs) were largely found to be of biogenic rather than anthropogenic origin. Finally, our findings reveal episodic prevalence and temporal distribution of SOA constituents across the urban region of Houston, Texas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Rapid assessment of structural and compositional changes during early stages of zirconium alloy oxidation

A multimodal chemical imaging approach has been developed and applied to detail the dynamic, atomic-scale changes associated with oxidation of a zirconium alloy (Zircaloy-4). Scanning transmission electron microscopy, a gas-phase reactor chamber attached to an atom probe tomography instrument, and synchrotron-based X-ray absorption near-edge spectroscopy were employed to reveal morphology, composition, crystal, and electronic structure changes that occur during initial stages of oxidation at 300 °C. Oxidation was carried out in 10 mbar O 2 gas for short exposure times of 1 and 5 min. A multilayered oxide film with a cubic ZrO adjacent to the oxide/metal interface, a nanoscopic transition region with a graded composition of ZrO 2-x (where 0 < x < 1), and tetragonal ZrO 2 in the outermost oxide were formed. Partitioning of the major alloying element (tin) to the oxide/metal interface and heterogeneously within the oxide accompanied the development of the layered oxide. Our work provides a rapid, high-throughput approach for detailed characterisation of initial stages of zirconium alloy oxidation at an accelerated time scale, with implications for several other alloy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electric field stimulates production of highly conductive microbial OmcZ nanowires

Multifunctional living materials are attractive due to their powerful ability to self-repair and replicate. However, most natural materials lack electronic functionality. Here we show that an electric field, applied to electricity-producing Geobacter sulfurreducens biofilms, stimulates production of cytochrome OmcZ nanowires with 1,000-fold higher conductivity (30 S cm -1 ) and threefold higher stiffness (1.5 GPa) than the cytochrome OmcS nanowires that are important in natural environments. Using chemical imaging-based multimodal nanospectroscopy, we correlate protein structure with function and observe pH-induced conformational switching to β-sheets in individual nanowires, which increases their stiffness and conductivity by 100-fold due to enhanced π-stacking of heme groups; this was further confirmed by computational modeling and bulk spectroscopic studies. These nanowires can transduce mechanical and chemical stimuli into electrical signals to perform sensing, synthesis and energy production. Overall, these findings of biologically produced, highly conductive protein nanowires may help to guide the development of seamless, bidirectional interfaces between biological and electronic systems.

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

Multimodal characterization of Te inclusions in Cd 1−x Zn x Te and Cd 1−x Zn x Te 1−y Se y for gamma and X-ray detectors

While CdZnTe (CZT) and CdZnTeSe (CZTS) semiconductors have emerged as compounds for room-temperature gamma and X-ray detection materials, they continue to be constrained by the formation of Te-inclusion defects generated during the growth and post-growth phases of the material, which adversely affect the detector performance. We demonstrate the utility of multimodal microscopic imaging and analysis for the characterization of the optical and electronic properties of Te inclusions in CZT and CZTS crystals at both micron and nanometer length scales. Having first identified regions with micron-scale Te inclusions using confocal Raman microscopy techniques, optically coupled infrared scattering near-field optical microscopic mapping was performed to map the distribution of these inclusions with nanometer spatial resolution and correlate the presence of Te inclusions in the matrix with other properties. Kelvin probe force microscopy was then utilized to characterize the variations of the work function associated with the presence of Te inclusions. Here, we observe an increase of ~ 240 mV in the work function associated with Te inclusions compared to the bulk CZT/CZTS crystals. Additionally, we observe that individual bulk grains in CZT can exhibit slight potential variations. Our findings develop a portrait of the charge trapping mechanisms in CZT and CZTS that act to degrade detector performance, while the demonstration of these combined microscopy techniques provides a new analytical tool that can be utilized for further optimization of the detector performance for these semiconducting compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning

In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the additional in-formation could improve the model’s ability to distinguish malicious inputs, or simply provide new attack avenues and vulnerabilities. For natural perturbations, the additional information could better inform model decisions and weaken perturbation effects or have no significant influence at all. In this work, we seek to characterize the performance and robustness of a multispectral (RGB and near infrared) image segmentation model subjected to adversarial attacks and natural perturbations. While existing adversarial and natural robustness research has focused primarily on digital perturbations, we prioritize on creating realistic perturbations designed with physical world conditions in mind. For adversarial robustness, we focus on data poisoning attacks whereas for natural robustness, we focus on extending ImageNet-C common corruptions for fog and snow that coherently and self-consistently perturbs the input data. Overall, we find both RGB and multispectral models are vulnerable to data poisoning attacks regardless of input or fusion architectures and that while physically-realizable natural perturbations still degrade model performance, the impact differs based on fusion architecture and input data.

Deep learning, multispectral images, multimodal fu↗
DOE OSTI2026

The scientific case for concurrent neutron and X-ray scattering and spectroscopy

The interrogation of materials with X-rays or neutrons to determine structure, energetics, and dynamics is fundamental to advancing physical and chemical materials science and enabling innovative material technologies. A persistent challenge in materials development is that progress depends on understanding structure and dynamics across multiple length and time scales in increasingly complex, multicomponent systems featuring interfaces, heterogeneity, and hierarchical organization. Despite rapidly growing demands on materials characterization, current experimental approaches are almost exclusively based on isolated X-ray or neutron scattering and spectroscopy, reflecting a paradigm largely unchanged for decades. To assess the scientific need for a new experimental paradigm, a 3-day workshop sponsored by the U.S. National Science Foundation (NSF) was held at the SpringHill Suites, San Jose, California, from June 2 to 4, 2022. The workshop brought together 70 national and international experts who critically evaluated opportunities enabled by concurrent neutron and X-ray (NeX) scattering, spectroscopy, and imaging experiments. The participants reached a clear consensus that establishing NeX capabilities is crucial for advancing the science of complex materials in the United States. This report illustrates the scientific drivers for NeX experiments through representative examples spanning biomaterials, energy materials, soft matter, nanomaterials, quantum materials, geoscience, and applied materials research. The complementarity of neutrons and X-rays is essential for robust model development and refinement, particularly in multiphase and multicomponent systems. While joint refinement of data from separate experiments is valuable, concurrent measurements uniquely eliminate uncertainties arising from sample evolution, environmental drift, and irreproducibility associated with experiments performed at different locations and times. Realizing NeX capabilities will require the development of new instrumentation, data analysis frameworks, and robust sample environments compatible with both neutron and X-ray probes. Addressing these challenges will enable unambiguous interpretation of complex materials behavior and open new frontiers in materials research.

X-ray↗
DOE OSTI2023

In planta single-molecule imaging and holographic force spectroscopy to study real-time, multimodal turnover dynamics of polysaccharides and associated carbohydrate metabolites

Cellular production of polysaccharides from simple sugar molecules serves critical roles in a variety of metabolic processes essential to the survival of every living organism. In the context of photosynthetically fixed carbon, cell wall polysaccharides synthesis is particularly of interest for increased biomass accumulation for human food, animal feed, and bioenergy related applications. Despite a long history of research in plant cell biology, our understanding of in planta cell wall biosynthesis and its regulation is far from complete due to lack of a comprehensive microscopy toolkit that encompass the multiple spatial and temporal scales in which cell wall polysaccharides fibrils are synthesized and assembled into intricate cell wall networks. In this project, a multidisciplinary team of scientists from Rutgers University, Vanderbilt University, and Oak Ridge National Laboratory carried out innovative multimodal, multiscale microscopy studies of cell wall synthesis using single-molecule force-spectroscopy, super-resolution fluorescence microscopy, and in vivo live cell imaging. Our novel multimodal and holistic imaging approach revealed plant cell wall polysaccharides synthesis processes in unprecedented detail across multiple spatiotemporal scales, from a single-molecule to a single-cell. Besides, the team has made technical and scientific innovations across multiple research fields—microscopy, bioengineering, single-molecule biophysics, and plant biology, etc.—to accomplish the goals. The results from this project will greatly advance the mechanistic and holistic understanding of in planta cell wall synthesis, which will accelerate the development of better transgenic crops for bioenergy applications. Moreover, the new toolbox, combining powerful advanced microscopy assays with cell/protein engineering, will have broader impacts on molecular and cellular biology fields by paving the way for studying cellular processes occurring across multiple physical scales with multimodal microscopy methodologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multimodal, Multidimensional, and Multiscale X-ray Imaging at the National Synchrotron Light Source II

Over the last couple of decades, the synchrotron radiation research community has witnessed tremendous advancement in the field of x-ray imaging and microscopy. Continuing enhancement of the light sources’ brightness, advances in x-ray focusing optics, incorporation of precision instruments, and development of innovative imaging techniques are some of the leading contributors for the rapid progress. New imaging and microscopy beamlines, such as the ones at the National Synchrotron Light Source II (NSLS-II), a Department of Energy (DOE) Office of Science user facility located at DOE’s Brookhaven National Laboratory, are capable of performing more sophisticated and complicated measurements than ever before, either on their own or used together as a suite of tools. These sophisticated and complex measurements exhibit attributes for multimodal, multidimensional, and multiscale imaging. Recent popularity of these methods is strongly driven by the current trends in materials characterizations, where researchers desire to map out hierarchical materials structure over a large range of length scales, to understand structure-property correlation, to quantify materials structures in 3D, and/or to perform operando or in situ experiments. It is important to emphasize that “materials” under investigation are not only synthesized materials but also the natural materials. In this article, we describe multimodal, multidimensional and multiscale x-ray imaging capabilities of the NSLS-II beamlines and how these methods are used to tackle complex scientific problems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗
DOE OSTI2023

Experimental Assessment of Mammalian Lipidome Complexity Using Multimodal 21 T FTICR Mass Spectrometry Imaging

Herein, we assess the complementarity and complexity of data that can be detected within mammalian lipidome mass spectrometry imaging (MSI) via matrix-assisted laser desorption ionization (MALDI) and nanospray desorption electrospray ionization (nano-DESI). We do so by employing 21 T Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) with absorption mode FT processing in both cases, allowing unmatched mass resolving power per unit time (≥613k at m/z 760, 1.536 s transients). While our results demonstrated that molecular coverage and dynamic range capabilities were greater in MALDI analysis, nano-DESI provided superior mass error, and all annotations for both modes had sub-ppm error. Taken together, these experiments highlight the coverage of 1676 lipids and serve as a functional guide for expected lipidome complexity within nano-DESI-MSI and MALDI-MSI. To further assess the lipidome complexity, mass splits (i.e., the difference in mass between neighboring peaks) within single pixels were collated across all pixels from each respective MSI experiment. The spatial localization of these mass splits was powerful in informing whether the observed mass splits were biological or artificial (e.g., matrix related). Mass splits down to 2.4 mDa were observed (i.e., sodium adduct ambiguity) in each experiment, and both modalities highlighted comparable degrees of lipidome complexity. Further, we highlight the persistence of certain mass splits (e.g., 8.9 mDa; double bond ambiguity) independent of ionization biases. Here, we also evaluate the need for ultrahigh mass resolving power for mass splits ≤4.6 mDa (potassium adduct ambiguity) at m/z > 1000, which may only be resolved by advanced FTICR-MS instrumentation.

21T-FTICR-MS↗
DOE OSTI2023

Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis

Introduction Recent studies in human brain connectomics with multimodal magnetic resonance imaging (MRI) data have widely reported abnormalities in brain structure, function and connectivity associated with schizophrenia (SZ). However, most previous discriminative studies of SZ patients were based on MRI features of brain regions, ignoring the complex relationships within brain networks. Methods We applied a graph convolutional network (GCN) to discriminating SZ patients using the features of brain region and connectivity derived from a combined multimodal MRI and connectomics analysis. Structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 140 SZ patients and 205 normal controls. Eighteen types of brain graphs were constructed for each subject using 3 types of node features, 3 types of edge features, and 2 brain atlases. We investigated the performance of 18 brain graphs and used the TopK pooling layers to highlight salient brain regions (nodes in the graph). Results The GCN model, which used functional connectivity as edge features and multimodal features (sMRI + fMRI) of brain regions as node features, obtained the highest average accuracy of 95.8%, and outperformed other existing classification studies in SZ patients. In the explainability analysis, we reported that the top 10 salient brain regions, predominantly distributed in the prefrontal and occipital cortices, were mainly involved in the systems of emotion and visual processing. Discussion Our findings demonstrated that GCN with a combined multimodal MRI and connectomics analysis can effectively improve the classification of SZ at an individual level, indicating a promising direction for the diagnosis of SZ patients. The code is available at https://github.com/CXY-scut/GCN-SZ.git .

Chen, Xiaoyi↗
DOE OSTI2025

Multimode turbulent flow measurements using magnetic resonance imaging- and laser-based techniques and computational fluid dynamics simulations

We studied the flow field characteristics of a turbulent flow over a regularized cube array with a perpendicular injection flow through the floor between the second and third cubical elements, representing the complex flow interactions of a 3D jet and the wake flows behind cubical obstacles. Four different experimental measurements were performed: two magnetic resonance imaging-based measurements for three-dimensional three-component velocity (MRV) and concentration (MRC) and two laser-based techniques, particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF), for two-dimensional two-component velocity and concentration measurement, respectively. The mainstream Reynolds number is Re = 15 000⁠, based on the primary inlet velocity U m and channel height D H ⁠, whereas the injector Reynolds number is Re j = 3400⁠, based on the injector velocity U j and the injector's exit width D j ⁠. Numerical simulations were performed for the studied flow configuration of turbulent flow over a regularized cube array using Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) approaches. Results obtained from experimental measurements—including MRV, MRC, PIV, and PLIF—as well as RANS and LES simulations are discussed and compared along several horizontal and vertical planes of the studied configuration. In addition, 3D turbulent flow structures, such as leading-edge vortex, horseshoe vortex, and jet shear-layer vortex, and the isosurfaces of scalar concentration successfully revealed by MRV and MRC techniques were found to be in very good agreement with those 3D features extracted from RANS and LES simulations. In conclusion, the high-resolution experimental and numerical database obtained from this study could be useful for validation and verification of numerical codes.

Computational fluid dynamics↗
DOE OSTI2020

Quantum Super-resolution Bioimaging using Massively Entangled Multimode Squeezed Light

This report presents a new method for realizing a super-resolution quantum imaging using massively entangled multimode squeezed light (MEMSL). Each branch of the entangled multimode light interacts with the sample and bears the spatially varying optical phase delay. When imaging optics with finite pupil sizes are used, information is lost. Thanks to the analyticity in the Fourier plane, a noiseless measurement would recover the lost information and accomplish super resolution imaging beating the Rayleigh diffraction limit. I proved rigorously in a fully quantum formalism and presented that (1) such information recovery is possible and (2) the information recovery can be accomplished with much less resources when MEMSL is used than those needed in any non-entangled or non-squeezed classical imaging method. Furthermore, the action of the optical loss in the imaging system that degrades the imaging performance is also rigorously analyzed and presented. Several bioimaging applications that can benefit tremendously from the proposed quantum imaging scheme are also suggested.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗