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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 451 records · Page 25

Integrated lipidomic and proteomic profiling reveals metabolic network disruption by SARS-CoV-2 variants

The rapid evolution of SARS-CoV-2 has produced myriad viral strains with increasing transmissibility and capacity for immune evasion. While effective vaccination campaigns have reduced the fatalities associated with SARS-CoV-2, infections continue, and a detailed understanding of how this virus manipulates host biochemical pathways remains elusive. We asked both whether the patterns of host lipid rewiring remained consistent across variants and whether the changes in the abundance of lipid classes are related to changes in the expression of the enzymes involved in their biosynthesis. We compared global nontargeted lipidomics on A549-ACE2 cells infected with the delta variant (B.1.617.2), or the omicron (B.1.1.529) variant to our previous results of global nontargeted lipidomics on A549-ACE2 cells infected with the original WA1 strain and further performed quantitative proteomics to assess changes in the host proteome. We found that metabolic rewiring, both on the lipid and the enzymatic level, is remarkably consistent across all three variants. We further mapped changes in the expression of host metabolic enzymes, linking enzyme expression to alterations in the abundance of specific lipids during infection. This analysis identified key proteins related to virus-mediated changes in lipid abundance, including fatty acid synthase (FASN), lysosomal acid lipase (LIPA), and ORMDL, a regulator of sphingolipid biosynthesis. These integrated lipidomic and proteomic experiments shed light on the importance of the complex network of host metabolism networks that support SARS-CoV-2 infection and suggest that lipid metabolism may be a promising avenue for uncovering conserved therapeutic targets.

SARS-CoV-2↗

Bulk synthesis of radiation resistant W – Ti – Cr – V compositionally complex alloys

Refractory compositionally complex alloys are candidate material systems for next generation advanced nuclear reactors. This work showcases the first successful bulk synthesis of low activation W–Ti based refractory compositionally complex alloys using arc melting and provides insights on using additive manufacturing for these compositions using directed energy deposition. Both techniques produce equiaxed grains composed of a tungsten matrix with Ti–V–Cr dendritic boundaries. The arc melted specimen possesses a multi-modal grain size distribution, while the directed energy deposition specimen possesses a more gaussian distribution of grain size. Both arc melted and directed energy deposition specimens demonstrate high thermal stability up to 900 °C, as well as promising radiation resistance with low loop formation and the presence of homogeneously distributed helium cavities maintaining small diameters at ≥10 dpa under simultaneous light (helium) and heavy (krypton) ion irradiation at 900 °C.

Arc melting (AM)↗

Microstructural features and deuterium diffusion in lithium penta-aluminate pellets under He + and D + ion irradiation

Lithium (Li) penta-aluminate (LiAl 5 O 8 ) is investigated as a potential tritium (T) breeding material, with a focus on microstructural response to ion irradiation and deuterium (D) diffusion behavior. Under high-fluence ion irradiation (2 x 10 17 (He + +D + )/cm 2 ) at 773 K, LiAl 5 O 8 exhibits significant disorder on the Li sublattice, as revealed by atomic-resolution scanning transmission electron microscopy, while the Al and O sublattices remain stable, demonstrating strong resistance to structural amorphization. Irradiation induces the formation of platelet-shaped antiphase boundaries (APBs), which may serve as effective D trapping sites. Atom probe tomography suggests the presence of 6 LiD clusters in the mass spectra, though definite conclusions regarding APB composition are hindered by signal overlap and limited data statistics. Time-of-flight secondary ion mass spectrometry reveals that D retention approaches to saturation at 3 x 10 17 (He + +D + )/cm 2 . Isothermal and isochronal annealing studies determine an average diffusivity of 1.6 x 10 -13 at 773 K and an effective activation energy of 0.8 ± 0.1 eV for D migration. Compared to γ-LiAlO 2 , LiAl 5 O 8 demonstrates superior irradiation resistance, minimal Li loss, and enhanced D retention, underscoring its potential as a durable breeder material for T production. In conclusion, these findings provide key insights into the microstructural evolution, defect dynamics, and D retention mechanisms in LiAl 5 O 8 under reactor-relevant conditions.

42 ENGINEERING↗

Modeling the microplastic distribution along the Delaware River Estuary: Accumulation patterns and hydrodynamic influences

Microplastic pollution is an escalating environmental concern, particularly in densely populated estuary regions, where it poses significant threats to aquatic life and human health. The dispersion patterns of microplastic particles along estuaries are influenced and complicated by multiple environmental factors such as river flow, tidal mixing, salt intrusion, and estuarine circulation. This study examines the accumulation and dispersion patterns by modeling three typical classes of microplastics in the Delaware River Estuary: synthetic fibers, sinking plastic films, and rising plastic pellets. Our findings reveal specific areas with high microplastic accumulation for each type. Notably, the upper estuary regions exhibit significant retention of rising microplastics, associated with a region with reduced along-thalweg velocities downstream of Trenton, NJ and upstream of Philadelphia, PA. Conversely, synthetic fibers and sinking plastic films accumulate in the flow convergence zone near the bottom salinity front, typically downstream of Philadelphia. All of the microplastic accumulation hot spot locations are controlled by the balance of river discharge and salinity intrusions. During the dry season, microplastic accumulation hot spots shift upstream in the estuary, whereas in the wet season, the strong river discharge pushes them downstream. Furthermore, on the other hand, tidal mixing, settling, and resuspension processes strongly impact the spreading of microplastics along the river.

Delaware River Estuary↗

Improving adhesive bonding of short carbon fiber thermoplastic composites to aluminum alloys with a hybrid laser-plasma surface modification strategy

This study investigates hybrid laser–plasma surface modification strategies for metal–CFRTP (carbon-fiber-reinforced thermoplastic polymer) dissimilar joints to improve their bonding performance, in contrast to existing literature that mostly focuses on either plasma or laser treatment alone. By conducting double cantilever beam (DCB) tests on adhesively-bonded AA5052 and CFRPA66 (carbon-fiber-reinforced polyamide 66) joints, as an example of metal–CFRTP joints, it was found that laser engraving on the metal surface combined with plasma treatment on the CFRTP surface significantly improved the specific fracture energy of the joint by 187% and 31% compared to as-received and plasma-treated-only joints, respectively. However, the hybrid treatment of laser engraving and plasma on the investigated CFRTP surface did not improve the bonding performance of the joints. The underlying mechanisms related to hybrid laser-plasma surface modification strategies were further investigated by examining the surface and cross-sectional morphologies after DCB testing using microscopy. Computational modeling was performed to elucidate the interaction between grooves on the metal substrate and the CFRTP–adhesive interfacial bonding in metal–CFRTP joints. This study provides new insights into developing surface modification methods for achieving strong metal–CFRTP adhesive joints, aimed at lightweighting structural components in automotive, aerospace, and other applications.

Adhesive bonding↗

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗

The real-time data processing and acquisition system for Project 8 Phase II

In Phase II of the Project 8 neutrino mass experiment, electrons from the decays of tritium or 83m Kr are detected via their ≈26 GHz cyclotron radiation while contained within a circular waveguide. The signal from a given electron is characterized as a brief chirp, lasting ≲10 ms and changing in frequency by ≲1 MHz/ms. To detect these signals, the Project 8 collaboration developed a Data Acquisition (DAQ) system tailored to the signal properties. The DAQ is responsible for simultaneously selecting up to three 100 MHz-wide frequency windows to study, detect, and trigger on likely signals from different electron kinetic energies, and for writing the relevant data to disk. Finally, we describe the Phase II DAQ system in detail and address how the system was used for data-taking operations.

Data acquisition↗

Direct comparison of gamma, electron beam and X-ray radiation effects on the polymers of a pulsed lavage device

Gamma radiation used for sterilization of medical devices is challenged by cobalt-60 supply and commercial capacity. To maintain a robust radiation sterilization marketplace for the rapidly growing single-use medical device industry, investigation of potential alternatives to gamma technology, such as electron beam (e-beam) and X-ray technology, is critical. In this work, we directly compare the effects of radiation source and absorbed dose level on the polymeric materials and function of a commercial pulsed lavage device used for wound care. Product functionality, polymer mechanical, and polymer optical properties were evaluated using standard methods and input from the device manufacturer. Test results show that functionality of the product was not inhibited by radiation although the battery in the device exposed to X-ray exhibited greater voltage loss compared to batteries in products exposed to gamma or e-beam. Statistically significant differences between gamma and e-beam exposure and between gamma and X-ray exposure were also observed for product appearance in terms of yellowness index of several of the polymers considered. Overall, the results of this study support the viability of e-beam and X-ray radiation technologies as alternatives to cobalt-60 gamma technology for sterilization of the single-use pulsed lavage medical device investigated.

Electron beam↗

Wave energy converter buoy for Arctic observations

Ocean observation buoys are currently powered-constrained by battery storage capacity or available solar power. Power constraints limit the number of measurements that can be made and the lifetime of the buoys. Ocean surface wave energy could be used for power production, but wave energy converters are not yet commercially available for ocean observation buoys. Here we present the design and testing of a drifting wave energy converter buoy using a pendulum transmission system (PTS). The wave energy converter buoy was designed to operate in Arctic temperatures and wave conditions, but it could be used in areas with warmer temperatures and larger waves. Field tests measured a maximum power production of 5 watts in waves with a significant wave height of 0.42 meters and a ten minute average power production of 37 milliwatts. Power production increased with the energy period and significant wave height. The energy harvesting capabilities of the PTS showed the utility of adding a wave energy conversion device to a drifting ocean observation buoy.

Arctic observations↗

Insight into industrial hemp ( Cannabis sativa L.) root exudation composition in a simulated soil environment: a rhizosphere-on-a-chip study

Microfluidic technologies provide a reduced complexity and soil-free environment to study plant-soil interactions at the microscale. Traditionally used for model plants such as Arabidopsis thaliana, this study represents the first application of a rhizosphere-on-a-chip (RhizoChip) to investigate root exudation in industrial hemp (Cannabis sativa L.), an agronomic crop with growing economic importance. By incorporating soil-like minerals (kaolinite, potassium feldspar, and biotite), the RhizoChip addresses limitations of previous research. Hemp seedlings grown in mineral-containing chips exhibited significant root growth, emphasizing the critical role of minerals in root development. Using untargeted metabolomics, 170 compounds were identified, including organic acids, amino acids, and secondary metabolites, with distinct profiles across conditions. Metabolic pathway analysis revealed activity in amino acid metabolism, the citric acid cycle, and secondary metabolite biosynthesis. In conclusion, this study highlights the RhizoChip's potential for long-term studies of root exudates in non-model crops and offers insights into rhizosphere processes with implications for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗

Velocity- and pressure-based metrics for estimating strike injuries during fish passage through hydro turbines

Hydropower is a crucial source of clean and reliable energy worldwide, and its importance will continue to grow. To enhance the environmental sustainability of hydropower development and operations, it is essential to predict the strike injury rates of fish passing through turbines accurately and cost-effectively. However, conventional experiments involving a large number of live fish are still commonly conducted in practice, and previous attempts mainly focused on the dose-response relationships at an individual level or relative comparisons of biological characterization between different conditions. Thus, this study proposes two novel strike metrics based on velocity and pressure (M V and M P ) measured by the cutting-edge Sensor Fish (SF) technology, designed to quantify the biological effects of strikes and collisions between fish and rigid hydraulic structures. SF measurements and 48-hour survival rates of live fish from a study conducted at the Ice Harbor Dam on the Snake River were used to establish thresholds for the two proposed metrics. These metrics and thresholds were then applied to three additional SF studies conducted at hydropower facilities featuring Kaplan turbines within the Columbia River basin. Results from the application of the strike metrics indicate that the estimated survival rates derived from both metrics agree well with previously reported live fish survival rates. Furthermore, the study identified specific passage regions with elevated risks of fish mortality due to strikes and collisions. Overall, the proposed strike metrics present a reliable and cost-effective approach to reducing reliance on live fish and lowering costs in fish passage studies.

Biological characterization↗

Root exudate lipids: Uncovering chemodiversity and carbon stability potential

Root-derived carbon has been shown to contribute more to soil carbon stocks than aboveground litter. Yet the molecular chemodiversity of root exudates remains poorly understood due to limited characterization and annotation. In this study, we characterized the molecular chemodiversity and production of metabolites and lipids in root exudates from field grown mature tall wheatgrass (Thinopyrum ponticum). We discovered a diversity of lipids, including substantial levels of triacylglycerols (∼19 μg/g fresh root per min), fatty acyls, sphingolipids, sterol lipids, and glycerophospholipids, some of which have not been previously documented in root exudates. By integrating tandem mass spectral library searching and deep learning-based chemical class assignment, our metabo-lipidomics approach significantly expanded the known molecular diversity of root exudates. Rates of lipid derived carbon production were approximately double that of polar metabolites (lipids: 81.52 ± 13.81 vs polar metabolites: 38.41 ± 5.93 μg C g −1 fresh root mass min −1 ) with an order of magnitude higher carbon to nitrogen ratios (lipids: 459 ± 90 vs polar metabolites: 14.40 ± 0.58). Exudate lipids displayed highly negative nominal oxidation state of carbon (−1.182 to −1.909), indicating that these compounds may be less favorable for microbial decomposition. Together our results suggest the potential of root exudate lipids to contribute to stable carbon pools in soil, supporting long-term carbon storage. This work advances understanding of plant-derived lipid inputs to soil and underscores the need for future studies on the functional roles of lipids in shaping root-microbe-soil interactions, microbial activity, soil structure, and nutrient availability – contributing to soil health.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhancing cold spray coatings: Microstructural dynamics and performance attributes of Inconel 625 with chromium carbide incorporation for hydropower applications

The incorporation of chromium carbide (CrC) particles into the cold spray (CS) process is known to mitigate nozzle clogging, although at the expense of deposition efficiency. This study explores the intricate microstructural changes induced by varying amounts of CrC powder (12.5 % and 6 %) in conjunction with Inconel-625 (Inc-625) powder. The deposition was carried out onto A27 cast steel under different CS parameters. Microstructural characterization, including detailed electron microscopy studies, reveals a complex yet structurally stable coating. Noteworthy features include grain fragmentation and a cellular structure enriched with Nb and Mo, with minimal plastic deformation of CrC in the matrix. The cold-sprayed coatings exhibit a significant (~4 times) increase in microhardness compared to the A27 substrate. Mechanical and cavitation erosion properties were systematically investigated. Coatings subjected to higher particle energy conditions with a gas pressure of 600 psi and gas temperature of 650 °C, demonstrated superior resistance to cavitation erosion. This resistance is attributed to a combination of factors, including microstructural characteristics and porosity. Altogether, the study provides valuable insights into the structural dynamics and performance of CS coatings enriched with CrC particles.

A27 cast steel↗

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.

Neural Network↗

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms

Spatially resolved mass spectrometry (MS)-based multi-omics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, in this study, we developed workflows for comprehensive multi-omics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time of flight (timsTOF)-MS. Afterwards, we detected 6,571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6,542 unique proteins across a single poplar root tissue section. These results were comparable to using each assay independently without modifications. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics-levels across biological tissues.

Veličković, Marija [Pacific Northwest National Lab↗

Raman Spectroscopic In Situ Monitoring of Highly Turbid Media

The ability to run chemical processing more efficiently and cost effectively is a need that spans critical materials recovery and legacy nuclear waste cleanup. Sensors integrated to provide online monitoring are essential to addressing this need by providing near-real time feedback on process conditions, which can improve efficiency, aid in decision making, and reduce the need for grab sample measurements. Optical spectroscopy is well-suited for providing online chemical composition information and has been widely applied in varied chemical systems. However, applications in turbid matrices continue to represent substantial challenges to sensor performance, where absorption or scattering of excitation light can cause significant signal interference. Here, in this study, close-focus Raman probes are investigated for use in turbid media as a way to overcome the signal loss from the scattering of the Raman excitation source. This, paired with advanced data science techniques, allowed for the development of chemometric models for the accurate quantification of several analytes of interest (NO 3 – , NO 2 – , and PO 4 3– ) in highly turbid solutions with solids loadings of up to 20 wt %. This work focuses on offline sample measurement and characterization as an initial step toward the development of online monitoring capabilities. Chemical systems of interest were focused on nuclear waste at the Hanford Site, which represents highly complex matrices that could realize significant processing benefits through the integration of online monitoring.

Felmy, Heather M. [Pacific Northwest National Labo↗

Accumulation of Soil Microbial Necromass Controlled by Microbe–Mineral Interactions

Soil organic matter (SOM) is a key reservoir for global carbon (C), supporting soil fertility and influencing greenhouse gas emissions. Microbial residues, composed of dead cells and cellular fragments, are major contributors to SOM formation. Yet, mechanisms by which minerals enhance the accumulation of microbial residues remain poorly understood. Here, we used 13 C-labeled glucose in a year-long incubation to trace microbial residue in sandy and silty soils. Across both soils, approximately 89% of retained microbial 13 C was recovered in the fine (<53 μm) mineral-associated organic matter (MAOM) pool. Within this pool, the light MAOM fraction, enriched in poorly crystalline Fe minerals, held 4.3 times more 13 C than the heavy, phyllosilicate-dominated MAOM fraction, despite accounting for only 17.2% of the total MAOM mass and 12.3% of the total soil mass. Along with 13 C enrichment, the light MAOM fraction showed greater abundance of N-containing groups, e.g., (amides and amino groups), indicative of microbial-derived compounds like proteins and amino sugars. Fe oxides in light MAOM from both soils were spatially dispersed. Microbial residue accumulation was greater in finer-textured silty soil. These findings demonstrate that mineral composition and texture jointly regulate microbial necromass accrual, highlighting light MAOM as a key pool for enhancing soil C storage.

13C isotope labeling↗