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Results for “multimodal characterization”

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 OSTI2023

A multimodal flow reactor for photocatalysis under atmospheric conditions

Photocatalysis is a promising concept for the direct conversion of solar energy into fuels and chemicals. The design, experimental protocol, and performance of a multimodal and versatile flow reactor for the characterization of powdered and immobilized photocatalysts are presented in this report. Ultimately, this instrument enables rigorous evaluation of photocatalysis performance metrics. The apparatus quantifies transient gas-phase reaction products via online real-time gas analyzer mass spectrometry (RTGA-MS). For H 2 , the most challenging gas, the photocatalytic system’s RTGA-MS gas detection sensitivity spans over three orders of magnitude and can detect down to tens of parts per million under atmospheric conditions. Using Pt nanoparticles supported on anatase TiO 2 photocatalyst via wet impregnation, the instrument’s capability for the characterization of photocatalytic H 2 evolution is demonstrated, resulting in an apparent quantum yield (AQY) of 48.1% ± 0.9% at 320 nm, 45.7% ± 0.3% at 340 nm and 31% ± 1% at 360 nm. The photodeposition of Pt on anatase TiO 2 was employed to demonstrate the instrument’s capability to track the transient behavior of photocatalysts, resulting in an improved 55% ± 2% AQY for H 2 evolution at 340 nm from aqueous methanol. This photocatalytic instrument enables systematic study of a wide variety of photocatalytic reactions such as water splitting and CO 2 reduction to valuable C2+ fuels and chemicals.

14 SOLAR ENERGY↗
DOE OSTI2021

Computed Tomography Scans of Naturally Mineralized Fractures in Upper Wolfcamp Shale

Computed tomography scans of fractures in small samples of Wolfcamp Shale from the Permian Basin and a depth of approximately 10,400 feet that have been cemented through natural processes. These samples were CT scanned at NETL. The data sets compliment the manuscript by Hajirezaie et al entitled "Mineral characterization of a sealed fracture: A multiscale multimodal imaging study of a syntaxial vein"

Computed Tomography↗
DOE OSTI2024

Fast Oxygen Redox Kinetics Induced by CoO 6 Octahedron With π –Interaction in P2–Type Sodium Oxides

Enhancing the kinetics of lattice oxygen redox (LOR) in P2-type layered sodium oxide cathodes is crucial for the advancement of sodium-ion batteries (SIBs) with superior energy and power densities. Electronic structure regulation stands out as a highly effective approach to address the inherent limitations of P2-type layered oxides with LOR, including sluggish kinetics, phase transitions, voltage hysteresis, and local structural distortion. In this work, a strategy involving the introduction of CoO 6 octahedra with π-interaction into Na 0.6 Li 0.1 Fe 0.3–x Co x Mn 0.6 O 2 (x = 0, 0.15, 0.3) cathodes to facilitate Na-ion transport is proposed. Furthermore, the impact of FeO 6 octahedra with σ-interaction in P2-type cathodes on electrochemical performance is comprehensively investigated. Through multimodal in-situ and ex-situ characterization techniques, it is revealed that Co–O with π-interaction effectively mitigates P2-OP4 phase transitions by strengthening Na–O, reduces voltage hysteresis, and stabilizes the local structure. Consequently, Na 0.6 Li 0.1 Co 0.3 Mn 0.6 O 2 demonstrates enhanced Na-ion diffusion kinetics, leading to improved rate performance and a reversible capacity of 55 mAh g –1 at 10 C, significantly outperforming cathodes with Fe–O σ-interaction. Moreover, when coupled with hard carbon, the full cell achieves a remarkable energy density of 395 Wh kg –1 (on cathode) at 0.1 C, with a capacity retention of 75% over 100 cycles at 1 C.

25 ENERGY STORAGE↗
DOE OSTI2020

Folding at the Microscale: Enabling Multifunctional 3D Origami‐Architected Metamaterials

Abstract Mechanical metamaterials inspired by the Japanese art of paper folding have gained considerable attention because of their potential to yield deployable and highly tunable assemblies. The inherent foldability of origami structures enlarges the material design space with remarkable properties such as auxeticity and high deformation recoverability and deployability, the latter being key in applications where spatial constraints are pivotal. This work integrates the results of the design, 3D direct laser writing fabrication, and in situ scanning electron microscopic mechanical characterization of microscale origami metamaterials, based on the multimodal assembly of Miura‐Ori tubes. The origami‐architected metamaterials, achieved by means of microfabrication, display remarkable mechanical properties: stiffness and Poisson’s ratio tunable anisotropy, large degree of shape recoverability, multistability, and even reversible auxeticity whereby the metamaterial switches Poisson’s ratio sign during deformation. The findings here reported underscore the scalable and multifunctional nature of origami designs, and pave the way toward harnessing the power of origami engineering at small scales.

Lin, Zhaowen↗
DOE OSTI2021

Unraveling the Formation Mechanism of a Hybrid Zr-Based Chemical Conversion Coating with Organic and Copper Compounds for Corrosion Inhibition

Environmental-friendly chromate-free, zirconium (Zr)-based conversion coating is a promising green technology for corrosion protection. Additives in the surface treatment provides critical functionalities and performance improvements; however, mechanistic understanding as of how the additives influence the coatings remains unclear. In this study, a new organic-inorganic hybrid Zrbased conversion coating which combines copper (Cu) compounds and polyamidoamine (PAMAM), taking advantages of a complementary nature of organic and inorganic additives. Here, a multimodal approach combining electron and X-ray characterization is applied to study the interaction of Cu 2+ and PAMAM and the resulting impacts on the coating formation. Adding PAMAM changed the surface morphology, thickness, distribution of Cu in the cluster and void formation of the coatings. High PAMAM (100-200 pm) leads to little conversion coating formation, and low PAMAM (0-25 ppm) shows voids formation under the coatings. Moreover, PAMAM incorporates in the coating in a form of PAMAM-Cu complex with a higher concentration towards the surface, provides an organic layer at the surface of the coating. X-ray absorption near-edge structure (XANES) spectroscopy shows a difference between the conventional and hybrid coating treatments in an alkaline solution to simulate the E-coat process, suggesting the contribution of PAMAM in the enhanced chemical stability in an alkaline environment. Therefore, an intermediate range of addition of PAMAM (50 ppm) is optimal to 1) avoid excessive voids formation, 2) promote some Cu cluster formation and thus enhancing the Zr-based coating formation and 3) incorporate organic components into the coating to improve the adhesion of the subsequent coatings. Overall, this work furthers our knowledge on the formation mechanism of an effective and environmentally friendly hybrid conversion coating for corrosion inhibition, demonstrating a critical processing-structureproperty relationship. This study will benefit future development of green and effective surface treatment technology.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Scaling Subspace-Driven Approaches Using Information Fusion

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.

Ghanem, Sally↗
DOE OSTI2024

Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Here, this study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Autonomous vehicles↗
DOE OSTI2020

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. In this work, we build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors’ data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Emerging Trends in Radar: Airborne ISR

Airborne radar for intelligence, surveillance, and reconnaissance has become an indispensable tool, found in both crewed and uncrewed platforms. These radars are usually multimode to engage missions for all manner of phenomenology detection and characterization, and have a long history of success for their intended purposes. Nevertheless, the advance of technology is facilitating enhanced exploitation of generally existing capabilities, as well as enhanced utility with new capabilities. In conclusion, the author identifies several areas of promising research and subsequent development that would provide significant and substantial capability enhancements.

Doerry, Armin Walter [Sandia National Laboratories↗
DOE OSTI2024

Exploring New Diamond Surfaces with Precision Chemistry and Quantum Spectroscopy

Point defects in diamond known as color centers are a promising platform for quantum sensing. As atom-like systems, they can exhibit excellent spin coherence and can be manipulated with light. As solid-state defects, they can be produced at high densities and be incorporated into scalable devices. Diamond is a uniquely excellent host; it has a large band gap, can be synthesized with sub-ppb impurity concentrations, and can be isotopically purified to eliminate magnetic noise from nuclear spins. Specifically, the nitrogen vacancy (NV) center has been demonstrated to be a highly sensitive, non-invasive magnetic probe capable of resolving the magnetic field of a single electron spin with nanometer spatial resolution. However, the development of NV centers and other color centers in diamond as a quantum platform crucially relies on developing methods to functionalize and process diamond surfaces. Diamond is the hardest material, making polishing and processing challenging, and diamond surfaces are inert and sterically hindered, making them notoriously difficult to chemically terminate and functionalize. Our approach is to discover new surface chemistries for diamond by combining multimodal surface spectroscopy with NV-based quantum spectroscopy to carefully characterize the diamond surface. We have developed a suite of techniques for characterizing the diamond surface using rapid tools for reaction screening and detailed surface spectroscopy tools for definitive identification of the surface bonding.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unsupervised physics-informed disentanglement of multimodal data

Here, we introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal datasets. Individual modalities are embedded into a shared latent space and fused through a product-of-experts formulation, enabling a Gaussian mixture prior to identify shared features. Sampling from clusters allows cross-modal generative modeling, with a mixture-of-experts decoder that imposes inductive biases from prior scientific knowledge and thereby imparts structured disentanglement of the latent space. This approach enables cross-modal inference and the discovery of features in high-dimensional heterogeneous datasets. Consequently, this approach provides a means to discover fingerprints in multimodal scientific datasets and to avoid traditional bottlenecks related to high-fidelity measurement and characterization of scientific datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Microstructural Characterization of Enhanced Conductivity Aluminum Alloys (Abstract)

In this project, Pacific Northwest National Laboratory (PNNL) will perform multimodal imaging to determine the microstructural features of enhanced conductivity aluminum alloys manufactured by NanoAL LLC. In particular, we will perform imaging to identify the basis of the strengthening mechanisms in the custom AA6X alloys provided by NanoAL LLC. These activities will be performed as part of a voucher service provided by the PNNL for the CABLE Manufacturing Prize stewarded by DOE Advanced Manufacturing Office. This 6-month effort will be executed predominantly at PNNL with a budget of $50,000.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The POINTER Imaging baseline cohort: Associations between multimodal neuroimaging biomarkers, cardiovascular health, and cognition

Abstract INTRODUCTION The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study. METHODS Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta‐amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk. RESULTS A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ−, Aβ+ (29%) participants were older, had higher apolipoprotein E (APOE) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition. DISCUSSION In this heterogenous, at‐risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non‐AD (Alzheimer's disease) pathway to brain injury and cognitive decline. Highlights The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle POINTER Imaging collected multimodal neuroimaging data in this unique, at‐risk cohort Amyloid burden was related to age, apolipoprotein E (APOE) ε4 carriage, and measures of disease progression Associations between amyloid and tau, and tau and cognition, were relatively weak CVD risk and tau pathology were independently related to memory

Neurosciences & Neurology↗
DOE OSTI2022

Damage Tolerance Assessment of Laser Clad Repairs of Coarse Grain Ti-6Al-4V

Background: Laser cladding offers a promising approach to repairing aviation components, with the ability to restore the geometric shape and the static and fatigue strength. Objective: The damage tolerance of a coarse grain Ti-6Al-4 V material is assessed via three sets of samples: (i) baseline, (ii) laser clad repaired, and (iii) laser cladding followed by heat treatment. Method: The specimens were subjected to fatigue loading using a marker band technique, in which cracks initiated from pre-defined laser slots and the small crack growth rates were measured by quantitative fractography. Additional characterization was performed on these samples, including hardness measurements, microstructure analysis, and multimodal residual stress analyses. Results: The region of the specimen undergoing laser cladding exhibited higher hardness values and a compressive residual stress, based on the slitting method. The samples subjected to heat treatment after cladding displayed negligible differences in grain size and minimal residual stress relaxation. Conclusions: The results demonstrated significant variability, with data demonstrating that cracks in the laser clad region (exhibiting the largest compressive residual stress) grew faster than cracks in the substrate region of the sample or in the baseline cases. Finally, these counterintuitive results are rationalized with additional analyses from synchrotron-based energy dispersive X-ray diffraction and high energy X-ray diffraction, demonstrating significant spatial variability in the residual elastic strains in the millimeter-sized prior β grains within the material.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The cardiac molecular setting of metabolic syndrome in pigs reveals disease susceptibility and suggests mechanisms that exacerbate COVID-19 outcomes in patients

Abstract Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients. Myocardium-specific mass spectroscopy-derived metabolomics, proteomics, and transcriptomics enabled the identity and quality of proteins and metabolites to be investigated in the myocardium to greater depth. Myocardium-specific deregulation of pro-inflammatory markers, propensity for arterial thrombosis, and platelet aggregation was revealed by computational analysis of differentially enriched pathways between MetS and control animals. While key components of the complement pathway and the immune response to viruses are under expressed, key N6-methyladenosin RNA methylation enzymes are largely overexpressed in MetS. Blood tests do not capture the entirety of metabolic changes that the myocardium undergoes, making this analysis of greater value than blood component analysis alone. Our findings create data associations to further characterize the MetS myocardium and disease vulnerability, emphasize the need for a multimodal therapeutic approach, and suggests a mechanism for observed worse outcomes in MetS patients with COVID-19 comorbidity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries

The rapid growth of automated and autonomous instrumentation brings forth opportunities for the co-orchestration of multimodal tools that are equipped with multiple sequential detection methods or several characterization techniques to explore identical samples. This is exemplified by combinatorial libraries that can be explored in multiple locations via multiple tools simultaneously or downstream characterization in automated synthesis systems. In co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, an orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Herein, we propose and implement a co-orchestration approach for conducting measurements with complex observables, such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure and integration into an iterative workflow via multi-task Gaussian Processes (GPs). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GPs. We illustrate this method for different modes of piezoresponse force microscopy and micro-Raman spectroscopy on a combinatorial Sm-BiFeO3 library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of the measured signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Nanoscale Mass Spectrometry Multimodal Imaging via Tip-Enhanced Photothermal Desorption

Materials ranging from adhesives, pharmaceuticals, lubricants, and personal care products are traditionally studied using macroscopic characterization techniques. However, their functionality is in reality defined by details of chemical organization on often noncrystalline matter with characteristic length scales on the order of microns to nanometers. Additionally, these materials are traditionally difficult to analyze using standard vacuum-based approaches that provide nanoscale chemical characterization due to their volatile and beam-sensitive nature. Therefore, approaches that operate under ambient conditions need to be developed that allow probing of nanoscale chemical phenomena and correlated functionality. Here, we demonstrate a tool for probing and visualizing local chemical environments and correlating them to material structure and functionality using advanced multimodal chemical imaging on a combined atomic force microscopy (AFM) and mass spectrometry (MS) system using tip-enhanced photothermal desorption with atmospheric pressure chemical ionization (APCI). We demonstrate enhanced performance metrics of the technique for correlated imaging and point sampling and illustrate the applicability for the analysis of trace chemicals on a human hair, additives in adhesives on paper, and pharmaceuticals samples notoriously difficult to analyze in a vacuum environment. Overall, this approach of correlating local chemical environments to structure and functionality is key to advancing research in many fields ranging from biology, to medicine, to material science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗