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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Porosity evolution under increasing tension in wire-arc additively manufactured aluminum using in-situ micro-computed tomography and convolutional neural network

Internal defects such as porosities are often formed in additively manufactured metal components. The pores nucleate, grow, and coalesce to form cracks under loads, leading to eventual catastrophic failure. Here, in this paper, the full-field porosity evolution, including pore growth and coalescence in a wire-arc additively manufactured (WAAM) aluminum alloy cylinder under tension is observed with in-situ X-ray micro-computed tomography (μCT). The pore size distribution, density, and tensile stress are calculated from the volumetric images analyzed by a convolutional neural network (CNN) algorithm, which provides rapid analysis of 12,950 slice images from μCT volumetric images at the reference state and 13 tensile strains. The results show the quantitative evolution of the growth and coalescence of macropores under tension. A strong correlation is found between the local pore volume fraction and the true tensile stress when the tensile strain is larger than 5%.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Challenges in spatial metabolomics and proteomics for functional tissue unit and single-cell resolution

While transcriptomics is the most broadly applied technology for global spatial and single cell measurements in healthy and diseased tissues. Transcripts are often used as a proxy for protein and even metabolite measurements, but it has become commonly accepted that extrapolating this kind of information is a poor proxy and not a substitute for direct measurement. Within the last decade advanced developments of mass spectrometry-based assays have made these direct measurements not only possible, but routine. Where mass spectrometry has become an enabling technology, and various methods can now detect hundreds of metabolites and thousands of proteins from samples. Not only can this be performed within bulk measurements, but much effort has been directed into translating these measurements to single cells and tissues at cellular resolution. The information obtained from mass spectrometry is now able to trace metabolic events and decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein, we will broadly overview developments in the field over the past decade, leading into several case studies which highlight the direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspective on paths forward and the challenges that persist with adoption and application of spatial omics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Extracting the Frequency-Dependent Dynamic Stokes Shift from Two-Dimensional Electronic Spectra with Prominent Vibrational Coherences

The dynamic Stokes shift is a common means for characterizing ultrafast solvation dynamics of electronically excited states. Here we extract the excitation frequency-dependent dynamic Stokes shift from two-dimensional electronic spectra (2DES) of cresyl violet, a molecule with a well-defined vibronic progression. The extracted dynamic Stokes shift function, S(t), exhibits oscillatory behavior, and the oscillatory components are assigned to intramolecular vibrational modes through DFT and TD-DFT calculations. Here, the well-characterized oscillations are incorporated into the fitting procedure of S(t). The excitation frequency dependence of the ultrafast response is examined through the analysis of S(t) obtained from slices taken at different excitation frequencies of the 2DES spectra. The extracted ultrafast timescales range from 36 to 98 fs, and we interpret the frequency dependence of the timescales in the context of other dynamic processes that also lead to lineshape changes in the 2DES spectrum, such as vibrational energy relaxation and spectral diffusion. Through comparison of the extracted timescales, we find that the fastest timescales are extracted over a range of excitation frequencies, where contributions from vibrational relaxation and spectral diffusion can be minimized.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Proximate Quantum Spin Liquid on Designer Lattice

Complementary to bulk synthesis, here we propose a designer lattice with extremely high magnetic frustration and demonstrate the possible realization of a quantum spin liquid state from both experiments and theoretical calculations. In an ultrathin (111) CoCr 2 O 4 slice composed of three triangular and one kagome cation planes, the absence of a spin ordering or freezing transition is demonstrated down to 0.03 K, in the presence of strong antiferromagnetic correlations in the energy scale of 30 K between Co and Cr sublattices, leading to the frustration factor of similar to 1000. Persisting spin fluctuations are observed at low temperatures via low-energy muon spin relaxation. Our calculations further demonstrate the emergence of highly degenerate magnetic ground states at the 0 K limit, due to the competition among multiply altered exchange interactions. Furthermore, these results collectively indicate the realization of a proximate quantum spin liquid state on the synthetic lattice.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Antibodies Raised Against an Aβ Oligomer Mimic Recognize Pathological Features in Alzheimer’s Disease and Associated Amyloid-Disease Brain Tissue

Antibodies that target the β-amyloid peptide (Aβ) and its associated assemblies are important tools in Alzheimer’s disease research and have emerged as promising Alzheimer’s disease therapies. This paper reports the creation and characterization of a triangular Aβ trimer mimic composed of Aβ 17–36 β-hairpins and the generation and study of polyclonal antibodies raised against the Aβ trimer mimic. The Aβ trimer mimic is covalently stabilized by three disulfide bonds at the corners of the triangular trimer to create a homogeneous oligomer. Structural, biophysical, and cell-based studies demonstrate that the Aβ trimer mimic shares characteristics with oligomers of full-length Aβ. X-ray crystallography elucidates the structure of the trimer and reveals that four copies of the trimer assemble to form a dodecamer. SDS-PAGE, size exclusion chromatography, and dynamic light scattering reveal that the trimer also forms higher-order assemblies in solution. Cell-based toxicity assays show that the trimer elicits LDH release, decreases ATP levels, and activates caspase-3/7 mediated apoptosis. Immunostaining studies on brain slices from people who lived with Alzheimer’s disease and people who lived with Down syndrome reveal that the polyclonal antibodies raised against the Aβ trimer mimic recognize pathological features including different types of Aβ plaques and cerebral amyloid angiopathy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Nicotinamide-Loaded Peptoid Nanotubes for Energy Regeneration in Acute Brain Injury

Acute brain injuries such as perinatal asphyxia, stroke, and traumatic brain injury result in ischemia, oxidative stress, excitotoxicity, and inflammation, leading to a depletion of ATP. Nicotinamide adenine dinucleotide (NAD+) is crucial for ATP regeneration and DNA repair during postinjury recovery. However, the therapeutic benefits of NAD+ and its precursors, such as nicotinamide (NAM), are limited by challenges in achieving effective cell-specific intracellular delivery. In this study, we use a nanopeptoid delivery strategy to replenish the cellular redox state and increase energy production in the acutely injured brain. By self-assembling peptoids into tubular structures, we created biocompatible NAM-conjugated peptoid nanotubes (NAM-PNTs) that vary in tubular length. NAM-PNTs demonstrated significant therapeutic benefits by enhancing cell viability and replenishing intracellular ATP levels within 24 h of treatment in oxygen–glucose-deprived (OGD) BV-2 cells. In organotypic brain slices, NAM-PNT treatment promoted glial proliferation, reduced proinflammatory cytokines, and increased anti-inflammatory cytokines after OGD, an ex vivo model of hypoxia-ischemia. The effect of NAM-PNTs is associated with their uptake into microglia via fluid-phase phagocytosis and caveolae-mediated endocytosis. A single systemic dose of NAM-PNTs localized in microglia in the injured hemisphere and reduced brain tissue loss and improved neuropathology after hypoxia-ischemia in term-equivalent rats. These findings highlight the therapeutic potential of NAM-PNTs for cell-specific targeted delivery and energy restoration in the acutely injured neonatal brain. In the neonatal brain injury field, this work demonstrates the development of an innovative nanoparticle platform from first-principles design and synthesis to in vitro screening and then demonstration of efficacy in vivo .

ATP↗
DOE OSTI2022

MPCVD-Grown Nanodiamond Microelectrodes with Oxygen Plasma Activation for Neurochemical Applications

Nanodiamonds (NDs) are a carbon nanomaterial that has a diamond core with heteroatoms and defects at the surface. The large surface area, defect sites, and functional groups on NDs make them a promising material for electrochemical sensing. Previously, we dip-coated ND onto carbon-fiber microelectrodes (CFMEs) and found increases in sensitivity, but the coating was sparse. Here, for this work, we directly grew thin films of ND on niobium wires using microwave plasma chemical vapor deposition (MP-CVD) to provide full surface coverage. ND microelectrodes show a reliable performance in neurotransmitter detection with good antifouling properties. To improve sensitivity, we oxygen plasma etched ND films to activate the surface and intentionally add defects and oxygen surface functional groups. For fast-scan cyclic voltammetry detection of dopamine, oxygen plasma-etching increases the sensitivity from 21 nA/μM to 90 nA/μM after treatment. Fouling was tested by repeated injections of serotonin or tyramine, and both ND and plasma oxidized nanodiamond (NDO) microelectrodes maintain their currents better compared to CFMEs and therefore are more antifouling. Furthermore, a biofouling test in brain slices shows that ND microelectrodes barely have any current drop, while the more hydrophilic NDO microelectrodes decrease more, but still not as much as CFMEs. Overall, grown ND microelectrodes are promising in neurotransmitter detection with excellent fouling resistance, whereas oxygen plasma etching slightly lowers the fouling resistance but dramatically increases sensitivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Schizophrenia-related microdeletion causes defective ciliary motility and brain ventricle enlargement via microRNA-dependent mechanisms in mice

Progressive ventricular enlargement, a key feature of several neurologic and psychiatric diseases, is mediated by unknown mechanisms. Here, using murine models of 22q11-deletion syndrome (22q11DS), which is associated with schizophrenia in humans, we found progressive enlargement of lateral and third ventricles and deceleration of ciliary beating on ependymal cells lining the ventricular walls. The cilia-beating deficit observed in brain slices and in vivo is caused by elevated levels of dopamine receptors (Drd1), which are expressed in motile cilia. Haploinsufficiency of the microRNA-processing gene Dgcr8 results in Drd1 elevation, which is brought about by a reduction in Drd1-targeting microRNAs miR-382-3p and miR-674-3p. Replenishing either microRNA in 22q11DS mice normalizes ciliary beating and ventricular size. Knocking down the microRNAs or deleting their seed sites on Drd1 mimicked the cilia-beating and ventricular deficits. These results suggest that the Dgcr8–miR-382-3p/miR-674-3p–Drd1 mechanism contributes to deceleration of ciliary motility and age-dependent ventricular enlargement in 22q11DS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400–1800 cm -1 ) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm -1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Dense, continuous membrane labeling and expansion microscopy visualization of ultrastructure in tissues

Abstract Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines an innovative membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validate the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supports the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrate the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yields ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

Science & Technology - Other Topics↗
DOE OSTI2025

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.

Neural Network↗
DOE OSTI2020

A global ensemble of ocean wave climate projections from CMIP5-driven models

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (H S ), mean wave period (T m ), and mean wave direction (θ m ) computed over time-slices 1979–2004 and 2081–2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Science & Technology - Other Topics↗
DOE OSTI2020

Machine learning for endoleak detection after endovascular aortic repair

Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Experimental demonstration of novel beam characterization using a polarizable X-band transverse deflection structure

The PolariX TDS (Polarizable X-Band Transverse Deflection Structure) is an innovative TDS-design operating in the X-band frequency-range. The design gives full control of the streaking plane, which can be tuned in order to characterize the projections of the beam distribution onto arbitrary transverse axes. This novel feature opens up new opportunities for detailed characterization of the electron beam. In this paper we present first measurements of the Polarix TDS at the FLASHForward beamline at DESY, including three-dimensional reconstruction of the charge-density distribution of the bunch and slice emittance measurements in both transverse directions. The experimental results open the path toward novel and more extensive beam characterization in the direction of multi-dimensional-beam-phase-space reconstruction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by the tolerable radiation dose and/or the geometric constraint on the tilt range, a main challenge in tomography is to achieve the best possible 3D reconstruction from a limited number of projections with noise. Over the years, a number of tomographic reconstruction methods have been developed including direct inversion, real-space, and Fourier-based iterative algorithms. Here, we report the development of a real-space iterative reconstruction (RESIRE) algorithm for accurate tomographic reconstruction. RESIRE iterates between the update of a reconstructed 3D object and the measured projections using a forward and back projection step. The forward projection step is implemented by the Fourier slice theorem or the Radon transform, and the back projection step by a linear transformation. Our numerical and experimental results demonstrate that RESIRE performs more accurate 3D reconstructions than other existing tomographic algorithms, when there are a limited number of projections with noise. Furthermore, RESIRE can be used to reconstruct the 3D structure of extended objects as demonstrated by the determination of the 3D atomic structure of an amorphous Ta thin film. We expect that RESIRE can be widely employed in the tomography applications in different fields. Finally, to make the method accessible to the general user community, the MATLAB source code of RESIRE and all the simulated and experimental data are available at https://zenodo.org/record/7273314.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗