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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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We present near-IR array images of the shocked molecular hydrogen in Herbig-Haro objects and jets from YSOs. These observations, in combination with optical and EEL observations of atomic emission, serve as probes of the shock conditions within the objects.
Molten salts have beneficial thermophysical and electrolytic properties, but the aggressive nature of molten salts requires corrosion mitigation strategies, such as structural material surface treatment. In this study, a laser-induced breakdown spectroscopy (LIBS) system was used to simultaneously ablate and monitor de-excitation spectra to form and identify dense alumina passivation layers. Additionally, high-frequency (kHz) LIBS imaging allowed rapid elemental mapping and depth profiling for surface O/Al ratios via minimally destructive analysis. The paired t-test rejected the null hypothesis that air and Ar cover gases were equivalent (p = 0.00012), and the analysis of variance (ANOVA) showed that overlap between shots (p = 0.03112) and cover gas flow rate (p = 0.02729) were statistically significant. At 75% overlap, the O/Al ratio increased to 10% ± 2%, and 90% overlap rose further to 31% ± 2%. Regardless of overlap, tuning the gas flow from 0.5 to 2.5 L min −1 enhanced the O/Al ratio by 24% ± 4%. LIBS depth profiling showed a 3× increase in layer thickness from 75% to 90% overlap. Molecular band peaks of AlO, a precursor to Al 2 O 3 found during treatment, suggest that band intensity could be used for real-time Al 2 O 3 optimization. Ultimately, by adjusting laser spot overlap and cover gas flow rate, a 266 nm laser was shown to form Al 2 O 3 layers on an Al6061 sample, with cover gas flow rate primarily affecting the O/Al ratio and layer thickness correlating with laser spot overlap. This combination of molecular spectra collection, imaging, and depth profiling demonstrated the robust layer analysis capabilities of LIBS.
To achieve tunable controls on the interactions of siliceous materials in subsurface environments bearing water and hydrocarbons, it is essential to determine the influence of hydrocarbon–water interfaces on the self-assembly of the silica particles. The hydrophilic silica particle have a tendency to aggregate on the water front of the interface. Self-assembly of silica nanoparticles proceeds via the migration of these nanoparticles to the water–hydrocarbon interface followed by aggregation at the interface. Fractal-like morphologies of assembled silica nanoparticles at water–hydrocarbon interfaces are observed. Rapid assembly of the hydrophilic silica nanoparticles at water–hydrocarbon interfaces corresponds to an overall reduction in the surface tension of water–toluene and water–heptane systems. These studies demonstrate the silica aggregation that is the precursor of silica polymerization, or nucleation and growth is influenced by the presence of hydrocarbons in subsurface geologic environments. These insights were derived from in operando ultrasmall and small-angle X-ray scattering (USAXS/SAXS) measurements, cryo-scanning electron microscopy (Cryo-SEM) imaging, and classical molecular dynamics simulations. As a result, these studies are intended to inform current and future efforts aimed at tuning silica reactivity in subsurface geologic environments to enhance permeability, the design and use of silica-based proppants to enhance fractures, and the development of effective strategies to control silica-based scaling behavior in subsurface reservoirs.
Cryo-electron microscopy is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments, which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.
Under the terms of our contract with NASA Ames Research Center, the University of Rochester (UR) offers the following final technical report on grant NAG 2-958, Molecular shocks associated with massive young stars: CO line images with a new far-infrared spectroscopic camera, given for implementation of the UR Far-Infrared Spectroscopic Camera (FISC) on the Kuiper Airborne Observatory (KAO), and use of this camera for observations of star-formation regions 1. Two KAO flights in FY 1995, the final year of KAO operations, were awarded to this program, conditional upon a technical readiness confirmation which was given in January 1995. The funding period covered in this report is 1 October 1994 - 30 September 1996. The project was supported with $30,000, and no funds remained at the conclusion of the project.
Super-resolved cryogenic correlative light and electron tomography is an emerging method that provides both the single-molecule sensitivity and specificity of fluorescence imaging, and the molecular scale resolution and detailed cellular context of tomography, all in vitrified cells preserved in their native hydrated state. Technical hurdles that limit these correlative experiments need to be overcome for the full potential of this approach to be realized. Chief among these is sample heating due to optical excitation which leads to devitrification, a phase transition from amorphous to crystalline ice. Here, in this work, we show that much of this heating is due to the material properties of the support film of the electron microscopy grid, specifically the absorptivity and thermal conductivity. We demonstrate through experiment and simulation that the properties of the standard holey carbon electron microscopy grid lead to substantial heating under optical excitation. In order to avoid devitrification, optical excitation intensities must be kept orders of magnitude lower than the intensities commonly employed in room temperature super-resolution experiments. We further show that the use of metallic films, either holey gold grids, or custom made holey silver grids, alleviate much of this heating. For example, the holey silver grids permit 20× the optical intensities used on the standard holey carbon grids. Super-resolution correlative experiments conducted on holey silver grids under these increased optical excitation intensities have a corresponding increase in the rate of single-molecule fluorescence localizations. This results in an increased density of localizations and improved correlative imaging without deleterious effects from sample heating.
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.
The placenta is a complex and heterogeneous organ that links the mother and fetus, playing a crucial role in nourishing and protecting the fetus throughout pregnancy. Integrative spatial multi-omics approaches can provide a systems-level understanding of molecular changes underlying the mechanisms leading to the histological variations of the placenta during healthy pregnancy and pregnancy complications. Herein, we advance our metabolome-informed proteome imaging (MIPI) workflow to include lipidomic imaging, while also expanding the molecular coverage of metabolomic imaging by incorporating on-tissue chemical derivatization (OTCD). The improved MIPI workflow advances biomedical investigations by leveraging state-of-the-art molecular imaging technologies. Lipidome imaging identifies molecular differences between two morphologically distinct compartments of a placental villous functional unit, syncytiotrophoblast (STB) and villous core. Next, our advanced metabolome imaging maps villous functional units with enriched metabolomic activities related to steroid and lipid metabolism, outlining distinct molecular distributions across morphologically different villous compartments. Complementary proteome imaging on these villous functional units reveals a plethora of fatty acid- and steroid-related enzymes uniquely distributed in STB and villous core compartments. Integration across our advanced MIPI imaging modalities enables the reconstruction of active biological pathways of molecular synthesis and maternal-fetal signaling across morphologically distinct placental villous compartments with micrometer-scale resolution.
Abstract The electrical double layer is known to spontaneously form at the electrode‐electrolyte interface, impacting many important chemical and physical processes as well as applications including electrocatalysis, electroorganic synthesis, nanomaterial preparation, energy storage, and even emulsion stabilization. However, it has been challenging to study this fundamental phenomenon at the molecular level because the electrical double layer is deeply “buried” by the bulk electrolyte solution. Here, we report a quantitative probing of the electrical double layer of ionic liquids from the solid side of a photoelectron‐transparent graphene‐carbon nanotube hybrid membrane electrode using X‐ray photoelectron spectroscopy. The membrane window is ultrathin (1‐1.5 nm), large (~1 cm 2 ), and robust, enabling a tight seal of the electrolyte and quantitative measurement with excellent photoelectron signals. By operando monitoring the population changes of cations and anions in response to the applied electrical potentials, we experimentally resolve the chemical structure and dynamics of the electrical double layer, which corroborate results from molecular dynamics simulations. image
Hot spots are spatial regions of intense energy localization that govern initiation of secondary high explosives. Studies that characterize or compare simulated hot spots are frequently either qualitatively descriptive or resort to quantitative distribution functions that neglect stochastic variations and spatial correlations—effects that are also neglected in common comparison tests like the Kolmogorov–Smirnov test. To this end, we develop an image distinguishability analysis (IDA) test based on principal component (PC) analysis that makes pixel-by-pixel comparisons between small, for example, O(<10), image data sets. The IDA test makes comparisons through a generalized distance metric in the PC space and a test statistic that is derived to calculate mathematical equation-values. Here, we derive a statistical distribution and criticality criterion to determine whether images are distinguishable from established baselines. We apply the IDA test on images generated from molecular dynamics simulations of hot spots from pore collapse in TATB to assess scale invariance in the complex patterns of hot spots that form in a representative high explosive crystal. The IDA test shows that TATB hot spot spatial temperature fields and their derived temperature histograms exhibit scale-invariant features over specific intervals of shock orientation, strength, and initial pore diameter. However, the IDA test also shows that qualitatively different conclusions regarding invariance can be reached depending on whether the hot spot is treated as a spatially correlated field as opposed to a distribution function that lacks spatial information.
In this work, the application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.
Achieving reversible and tunable assembly of silica nanoparticles at liquid–liquid interfaces is vital for a wide range of scientific and technological applications including sustainable subsurface energy applications, catalysis, drug delivery and material synthesis. In this study, we report the mechanisms controlling the assembly of silica nanoparticles (dia. 50 nm and 100 nm) at water–heptane and water–toluene interfaces using sodium dodecyl sulfate (SDS) surfactant with concentrations ranging from 0.001–0.1 wt% using operando ultrasmall/small-angle X-ray scattering, cryogenic scanning electron microscopy imaging and classical molecular dynamics simulations. The results show that the assembly of silica nanoparticles at water–hydrocarbon interfaces can be tuned by controlling the concentrations of SDS. Silica nanoparticles are found to: (a) dominate the interfaces in the absence of interfacial SDS molecules, (b) coexist with SDS at the interfaces at low surfactant concentration of 0.001 wt% and (c) migrate toward the aqueous phase at a high SDS concentration of 0.1 wt%. Energetic analyses suggest that the van der Waals and electrostatic interactions between silica nanoparticles and SDS surfactants increase with SDS concentration. However, the favorable van der Waals and electrostatic interactions between the silica nanoparticles and toluene or heptane decrease with increasing SDS concentration. As a result, the silica nanoparticles migrate away from the water–hydrocarbon interface and towards bulk water at higher SDS concentrations. These calibrated investigations reveal the mechanistic basis for tuning silica nanoparticle assembly at complex interfaces.
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.
Nature routinely exploits liquid–liquid phase separation (LLPS) of proteins to control the assembly and mineralization of hybrid materials. Here, we show that fusion of the Car9 silica-binding peptide to an elastin-like polypeptide (ELP) yields temperature- and sequence-programmable soft matter templates for the synthesis of silicified architectures ranging in size from nanometers to micrometers. Specifically, we demonstrate unprecedented control over the diameter of silica nanoparticles (SiNP) in the 30–60 nm range with 4 nm precision, show that a single arginine residue (R4) in the Car9 sequence underpins the transition from micelles to proteinosomes, and find that substitutions in other basic residues modulate electrostatic repulsion and solvation to enable access to kinetically trapped species. These structures, which include interconnected micelles, small (∼200 nm) and large (>5 µm) vesicles, are readily visualized by SEM imaging following silicification. Molecular dynamics (MD) simulations and AlphaFold predictions reveal that mutations in positively charged residues alter interfacial packing, hydration, and conformational freedom of the silica-binding segments. Overall, our results establish sequence and thermal energy as synergistic levers for morphological control across length scales using solid-binding ELPs and establish mineralization as a powerful tool to visualize the structure of dynamic soft matter assemblies.
An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development, and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, re-analysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). Finally, as this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP and external research efforts.
It is desired to maintain supersonic flow through the combustor of supersonic airbreathing engines to reduce static temperatures and total pressure losses inherent in reducing flow to subsonic speeds. Due to the supersonic speeds through the combustor, mixing of the fuel and air must by rapid for complete combustion to occur within a reasonable streamwise distance. It was proposed that the addition of swirl to the fuel jet prior to injection might enhance the mixing of the fuel with the air. The effects of swirl on the mixing of a 30 deg wall jet into a Mach 2 flow were experimentally investigated. Swirl was introduced into the fuel stream by tangential injection into a cylindrical swirl chamber. The flow was then accelerated through a convergent-divergent nozzle with an area ratio of two, and supersonically injected into the Mach 2 flow such that the static pressure of the fuel matched the effective back pressure of the main flow. Two different cases with swirl and one without swirl were investigated, with both helium and air simulating the fuel. Rayleigh scattering was used to visualize the flow and seeding the fuel with water allowed it to be traced through the main flow. Using histograms of the pure molecular Rayleigh scattering images, the helium concentration in the jet-mixing region of the flow was monitored and found to decrease slightly with swirl, indicating better mixing. Thresholding the water-seeded images allowed the jet-mixing region to be isolated and showed a slight increase in this area with swirl. Penetration, however, was slightly less with swirl. Rescaling the data for equal mass flow rates allowed comparison for a scramjet application of a combustor with a single injector and the desire to fuel to a specified fuel-to-oxidant ratio. These results showed a substantial increase in the spreading area with swirl, an increase in the mixing occurring in this area, and slightly better penetration.
A compact common path interferometer (CPI) system has been developed to measure the diffusivity of liquid pairs. The CPI is an optical technique that can be used to measure changes in the gradient of the refraction index of transparent materials. It uses a shearing interferometer that shares the same optical path from a laser light source to the final imaging plane. The molecular diffusion coefficient of liquids can be determined from the physical relations between changes in the optical path length and liquid phase properties. When the data obtained by using the CPI have been compared with similar results from other techniques, the instrument has been demonstrated to be far superior to other instruments for measuring the diffusivity of miscible liquids while staying very compact and robust (ref. 1). Because of its compactness and ease of use, the CPI has been adopted for use in studies of interface dynamics as well as other diffusion-controlled process applications (ref. 2). This progress will permit experiments in microgravity that can quantitatively answer basic science questions about mass and thermal diffusion and their effect in transport processes. This instrument is a spinoff of a diagnostic development for microgravity fluid physics experiments at the NASA Glenn Research Center that has used optics and electronics existing in the fluid physics laboratory for feasibility studies. Optical diagnostic techniques have become an integral part of many areas of measurement applications in industrial and research laboratories. Many types of interferometers and their phase-shifted versions have been used as instruments for measuring optical wave fronts for lens testing and combustion and fluid flow diagnostics. One of these, the point diffraction interferometer, is considered to be robust (see, for example, ref. 3) because it has a common-path design. The point diffraction interferometer is difficult to align and has a limited measurement range for liquid-phase applications. Interferometry and schlieren techniques have been widely used for many years for gas-flow-visualization. On the other hand, the CPI, which is basically a Wollaston prism Polarizer in combination with an analyser (crystalline quartz retardation plate) is used instead of a point diffraction interferometer unit. The advantage of using the CPI over other optical techniques is that it can make quantitative measurements in liquids with the large index of refraction variations that often occur in interface dynamics studies. This can be simply accomplished by using different prism polarizers for each particular experimental condition.
Cells treated with RNAlater(TradeMark) have previously been shown to contain antigenic proteins that can be visualized using Western blot analysis. These proteins seem to be stable for several months when stored in RNA stabilizer at 4 C. Antigenic protein can be recovered from cells that have been processed using an Ambion RNAqueous(Registered TradeMark) kit to remove RNA. In this set of experiments, human mixed Mullerian tumor (LNI) cells grown on the International Space Station during Expedition 3 were examined for antigenic stability after removal of RNA. The cells were stored for three months in RNAlater(TradeMark) and RNA was extracted. The RNA filtrate containing the protein was precipitated, washed, and suspended in buffer containing sodium dodecyl sulfate (SDS). Samples containing equal concentrations of protein were loaded onto SDS-polyacrylamide gels. Proteins were separated by electrophoresis and transferred by Western blot to polyvinylidene fluoride (PVDF) membrane. The Western blots were stained with an enhanced chemiluminescent ECL(Registered Trademark) Plus detection kit (Amersham) and scanned using a Storm 840 gel image analyzer (Amersham, Molecular Dynamics). ImageQuant(Registered TradeMark) software was used to quantify the densities of the protein bands. The ground control and flight LN1 cell samples showed a similar staining pattern over time with antibodies to vimentin, glyceraldehyde-3-phosphate dehydrogenase, and epithelial membrane antigens.