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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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The objectives of this paper are to give a brief insight into the launch vehicle implications for a manned Mars expedition and follow-on exploration; and secondly, to show the effects of such a mission requirement on the launch vehicle system. Due to the complexity and intricacy of the overall mission, vehicle system, and their interrelationships, only the very broad and salient points will be discussed.
The application of quantum algorithms to the study of many-particle quantum systems requires the ability to prepare wave functions that are relevant in the behavior of the system under study. Hamiltonian symmetries are important instruments used to classify relevant many-particle wave functions and to improve the efficiency of numerical simulations. In this work, quantum circuits for the exact and approximate preparation of total spin eigenfunctions on quantum computers are presented. Two different strategies are discussed and compared: exact recursive construction of total spin eigenfunctions based on the addition theorem of angular momentum, and heuristic approximation of total spin eigenfunctions based on the variational optimization of a suitable cost function. The construction of these quantum circuits is illustrated in detail, and the preparation of total spin eigenfunctions is demonstrated on IBM quantum devices, focusing on three- and five-spin systems on graphs with triangle connectivity.
The report describes the development of scalable manufacturing methods for high-performance, stable perovskite solar modules using flexographic printing. The project developed self-leveling perovskite inks that exploit Marangoni flows to reduce coating defects and improve large-area film uniformity. Bayesian optimization was integrated with high-throughput photoluminescence mapping and photovoltaic measurements to efficiently optimize ink formulations and printing conditions. The resulting printed perovskite solar cells achieved champion power conversion efficiencies above 21.6%, with median efficiencies exceeding 20% across large device batches. At the module scale, printed devices achieved active-area efficiencies up to approximately 17.3% on 25 cm² substrates. The project also demonstrated improved performance and stability using additively patterned interconnections compared with laser-scribed controls. Overall, the work establishes a data-driven, roll-compatible pathway toward high-throughput, low-capital-cost manufacturing of uniform and stable perovskite photovoltaics.
Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening ofSchistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection ofS. haematobiumthat combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containingS. haematobiumeggs, during two different field studies in Côte d’Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF and DF models alone to combinations of BF and DF models, using annotations from trained microscopists as the gold standard. We found that models trained on DF images, and almost all BF and DF combinations, performed significantly better than models trained on BF images only. When models were trained on images from the first field study (n = 349 patients, 748 images of each contrast), patient-level classification performance on patient images from the second study (n = 375 patients, 752 images of each contrast) met the WHO Diagnostic Target Product Profile (TPP) sensitivity and specificity for the monitoring and evaluation use case (sensitivity for all models and combinations was >75% when evaluated at a confidence score threshold that resulted in specificity >96.5%). When we used images from both field studies for the training set, performance of the models was improved. Overall, this work shows that the use of DF and BF increases the performance of ML models on images from devices with low-cost optics, while retaining the portability, power, and time-to-results of the WHO’s diagnostic TPP. DF requires no additional sample preparation and does not increase the complexity of the imaging system. It thus offers a practical means to improve performance of automated diagnostics forS. haematobiumas well as other microscopy-based diagnostics.
Bosch technology has the potential to close the atmospheric revitalization loop by providing theoretically 100% regeneration of metabolic carbon dioxide into oxygen. The main obstacle to using Bosch technology is over-pressurization issues in the carbon formation reactor. Prior work on Bosch catalyst regeneration established that agitating carbon coated beads in water broke up pressure causing clogs and regenerated the catalyst surface. This process proved to be time consuming with experiments taking at least one hour to regenerate ten grams of carbon coated beads. In an attempt to shorten the processing time, thermally shocking the carbon coated beads prior to mechanical agitation was tested. Agitating the carbon coated beads after exposure to a saturated steam environment was also tested to ensure that the steam generated by thermally shocking the carbon coated beads would not cause adverse effects. The rationale behind thermally shocking the carbon coated beads is that the bead would pull away from the carbon shell due to a difference in the thermal expansion coefficients. The results of this project show that the processing time can be cut in half when compared to previous work, and that saturated steam has little to no effect on regeneration.
Electrochemical methods for the direct reduction of alkyl esters have been understudied but provide a potential advantage for addressing specific waste streams given that such strategies often require only a minimal chemical profile. One such waste stream that could benefit from electrochemical remediation is poly(vinyl chloride) (PVC) plastics. PVC recycling often faces challenges due to the complexity of such waste. Solvent-based recycling methods pose several advantages for addressing post-use PVC; however, such methods are complicated by the high amounts of toxic legacy plasticizers (ortho-phthalates) present in PVC. A potential solution to addressing such phthalates is to address them electrochemically, coupled with recovery of the resulting valuable products. Presented herein is an optimized electrochemical method for ester activation that is leveraged to separate and recover the aromatic and alkyl components of ortho-phthalate plasticizers. The resulting aliphatic alcohols may be reused to prepare other non-toxic plasticizers. This electro-degradation method is demonstrated on six phthalates that have been identified to pose health concerns in addition to plasticizers recovered from commercial samples of PVC.
Here, this work develops a predictive density tool in Python, named Plutonium Nitrate Solutions (PuNS), to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer method and an empirical method were implemented into the PuNS tool to generate atom densities for use in MCNP6 material cards. These material cards are directly prepared into an MCNP6 input text file and are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, and plutonium isotope weight percentages. The PuNS tool is validated and verified against the International Criticality Safety Benchmark Evaluation Project Handbook experiments and is observed to predict densities within a root mean square error of 0.89% for the Pitzer method and 1.82% for the empirical method. These errors in density lead to up to 1569 pcm difference in MCNP6 calculated k eff for the Pitzer method and up to a 1751 pcm difference for the empirical method when compared to experimental benchmarks. Simultaneous work is also being performed at Los Alamos National Laboratory and the University of New Mexico to create a similar tool for plutonium chloride solutions, named Plutonium Chloride Solution, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also facilitate an improved understanding of solution systems and a potential relaxation in the conservatism of current aqueous plutonium processing criticality safety limits.
Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.
Nature builds hierarchically ordered materials, such as seashells, wood, and bones, through spatially and temporally regulated growth. Mimicking such a level of control in synthetic systems remains challenging, particularly in achieving multiscale organizations with prescribed nanoscale arrangements and desired material morphologies. In this study, we introduce a DNA-based self-assembly strategy for constructing diverse multi-shell mesoscale morphologies from nanoscale lattices, enabling prescribed structural, and compositional 3D material patterns. Using DNA origami frames as modular monomers, we direct anisotropic epitaxial growth through addressable DNA frame binding motifs and encapsulate nanoparticles (NPs) in desired 3D patterns. Sequential monomer addition under thermodynamically favorable conditions enables shell growth through heterogeneous nucleation while minimizing unwanted homogeneous nucleation. Here, we demonstrate that DNA-encoded addressability enables epitaxial shell growth along specific lattice directions, yielding crystals with multilayered mesoscale organization, including tube-like (sushi roll) and plate-like (macaron) morphologies. Shell-specific NP configurations and compositions are achieved through addressable and differentiated placement of NPs within each shell, as validated by small-angle x-ray scattering and cross-sectional scanning transmission electron microscopy. We further demonstrate addressable NP release and reveal that shells modulate release kinetics. Together, these findings establish a platform for fabricating DNA origami crystals with programmable mesoscale morphologies, nanoscale structure, composition, and transport properties.
Lewis is a multidisciplinary Center with strong research and development programs in aeronautical and space propulsion, power, space communications, space experiments and materials. Computational fluid dynamics (CFD) is playing an important and growing role in most of these areas. Described here is how CFD is integrated into these programs and highlights elements of the CFD activities. Examples are presented of codes developed to predict flow fields in advanced propulsion systems and several of the code validation experiments are described. The CFD effort at Lewis ranges from basic research on new and improved algorithms through code development to the application of these codes to specific engineering problems. Because of the substantial improvement in CFD's predictive capability, its use at Lewis is on a steep growth path, spreading rapidly into new areas which had not traditionally taken advantage of the techniques of numerical simulation. Multidisciplinary codes and the future direction of CFD at Lewis are discussed.
The Turbulent Heat Flux (THX) experiments were conducted at NASA Glenn Research Center (GRC) in order to collect measurements of velocities and temperatures for computational fluid dynamics (CFD) validation of heated flows, with a focus on propulsion system components. The experiments spanned 5 phases; four of which were conducted in the GRC AeroAcoustic Propulsion Laboratory (AAPL) using the Small Hot Jet Flow Rig (SHJAR). In addition to making velocity measurements with Particle Image Velocimetry (PIV), the THX experiments introduced a new Raman-scattering based capability to measure temperatures. Computational studies were also conducted for each of the experimental configurations, in order to provide a baseline of expected CFD results and conduct an assessment of the capability of various CFD approaches for calculating flows where the turbulent transport of heat was important. Two of the collected sets of data were used for American Institute of Aeronautics and Astronautics (AIAA) Propulsion Aerodynamic Workshops (PAWs). The data set from the 5th phase, collected for heated supersonic jets, was also used to construct new validation cases for the NASA Turbulence Model Resource (TMR). This paper provides an overview of the experiments and associated computations for each of the 5 test phases. Key experimental findings are presented. Lessons learned are provided concerning the effect of computational modeling choice on accuracy of predicting turbulent flows where thermal transport is important. Emphasis is placed on comparing Reynolds-averaged Navier-Stokes approaches with large-eddy simulation approaches. The benefits of utilizing a conjugate heat transfer method in conjunction with CFD solver for film cooling is demonstrated.
Vanadium alloys are being considered as a candidate structural material for the breeder blanket component of future fusion reactors due to their good thermal conductivity, low activation, and chemical compatibility with FLiBe. However, vanadium has a high hydrogen solubility, resulting in hydrogen diffusion and trapping in the vanadium structural alloy and lower than expected tritium breeding ratios. To mitigate the permeation of hydrogen into the structural alloy, a tritium permeation barrier will be coated onto the vanadium alloy. Multiple materials have been proposed as candidate tritium permeation barriers, but the extent to which each of these materials impacts hydrogen diffusion and trapping is still unknown. In this project, we perform atomic-scale calculations to narrow down the list of potential tritium permeation barrier candidates and to understand the mechanisms through which hydrogen diffusion is affected in these materials.
Here, we report systematic torque magnetometry measurements to investigate the electronic properties of the newly discovered kagome compound CaTi 3 Bi 4 . Electrical transport, magnetic susceptibility, and thermal measurements reveal no evidence of a magnetic ground state in this material. Torque data obtained in magnetic fields up to 41.5 T exhibit clear de Haas–van Alphen (dHvA) oscillations, with nine distinct frequencies ranging from 13 to 6164 T. Angular-dependent dHvA measurements show that, with the exception of the lowest frequency (13 T), all observed frequencies nearly follow a 1/cos 𝜃 dependence, where 𝜃 is the angle between the crystallographic 𝑐 axis and the magnetic field direction. This behavior is characteristic of quasi-two-dimensional Fermi-surface sheets with nearly circular cross sections. To further elucidate the electronic structure, we performed density functional theory (DFT) calculations of the band structure and Fermi surface. The calculated bands reveal the presence of multiple Dirac points (DP), flat bands (FB), and van Hove singularities (VHS) near the Fermi level. The resulting Fermi surface consists of several quasi-two-dimensional cylindrical sheets, consistent with the experimentally observed 1/cos 𝜃 dependence. Notably, the theoretical dHvA frequencies, derived from extremal Fermi-surface cross-sectional areas, agree well with the experimental values and reproduce their angular dependence. Remarkably, all experimentally observed frequencies are captured by the DFT predictions. Pressure-dependent calculations up to 10 GPa show that the electronic features—DP, FB, and VHS—evolve systematically with pressure. In particular, the VHS shifts closer to the Fermi level, demonstrating that pressure acts as an effective tuning parameter in this material. These combined experimental and theoretical results provide a comprehensive understanding of the electronic structure of CaTi 3 Bi 4 and demonstrate how pressure can be used to tune its key electronic features, offering valuable guidance for exploring related kagome materials.
In May 2022, the U.S. Offshore Wind Synthesis of Environmental Effects Research (SEER) project team hosted a stakeholder workshop focused on preconstruction (baseline) research needs for potential floating offshore wind (OSW) energy development on the U.S. Pacific Coast, including California, Oregon, and Washington. Prior to the workshop, the SEER team developed a set of initial synthesized research recommendations that were identified based on a review of relevant, publicly available resources and with advisory group input. The workshop covered three marine life breakout groups on subsequent days to discuss research recommendations related to 1) marine mammals and sea turtles, 2) fish and invertebrates, and 3) birds and bats. As part of the workshop, over a hundred participants from the public and private sectors provided feedback on various aspects of the initial research recommendations, including associated data and knowledge gaps, benefits/limitations of available methods and technologies, and technological advancements or infrastructure needed to address the recommendation. Approximately 1,000 total comments were received on the workshop MURAL boards and were synthesized in this report. Based on workshop feedback, SEER developed a final database of over 500 specific research recommendations based on more than 40 resources. In Fall 2022, the full database and a tool with updated synthesized research recommendations were disseminated on Tethys (https://tethys.pnnl.gov) to assist with informing future funding opportunities and research programming. There is a continued need to improve awareness of the potential environmental effects, monitoring technologies, and management strategies for floating OSW energy development on the U.S. Pacific Coast. Coordination of these activities will require the sustained involvement of multiple stakeholders from across sectors. Beyond the baseline considerations discussed in this workshop, future state-of-the-science activities should be planned to consider research needs across wind energy life cycle phases for all relevant wildlife taxa and associated habitat and ecosystem processes.
Six battery configurations were selected for detailed study and these are described. A computer program was modified for use in estimation of the weights, costs, and reliabilities of each of the configurations, as a function of several important independent variables, such as system voltage, battery voltage ratio (battery voltage/bus voltage), and the number of parallel units into which each of the components of the power subsystem was divided. The computer program was used to develop the relationship between the independent variables alone and in combination, and the dependent variables: weight, cost, and availability. Parametric data, including power loss curves, are given.
Urban watersheds include a gradient of highly managed to unmanaged ecosystems that vary in vegetation cover, built and natural drainage systems, and impervious cover. A distinct difference to undeveloped ecosystems is the dominance of material and energy import and export, the disruption of natural catenae, the covariation of soils, terrain and canopy conditions. In this chapter, we discuss the distinct characteristics of urban ecohydrology compared to non-urban systems. We also investigate emergent principles of unmanaged watersheds that underscore the promotion of ecohydrological resilience, and the need to abstract and adapt these principles to support the goal-oriented restoration of impaired urban ecosystems.
The practical applications of lithium (Li) metal batteries (LMBs) are limited by challenges such as dendrite formation and unstable solid electrolyte interphase (SEI), especially at higher C-rates. Here, this study introduces melamine-coated Li metal anodes (LMAs), forming a Li 3 N-rich SEI layer that improves ionic conductivity and mechanical stability. The optimized melamine-coated LMA demonstrated uniform coverage resulting in denser Li deposition, nearly doubled cycle life (~148 cycles at 0.5 C, 1C = 4.1 mA cm -2 ), compared to Bare-Li. These findings emphasize that coating materials-induced beneficial SEI components could lead to improvement of LMB performance.
Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.