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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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This review introduces area selective deposition and highlights recent contributions toward understanding and developing robust ASD processes.
Abstract The organic ligand environment surrounding enzymatic and homogeneous catalytic active sites often determines catalytic activity. Ruthenium nanoparticles, ≤1 nm in diameter, are synthesized using monodentate thiol, monodentate phosphine, and bidentate bisphosphine ligands. Even though some of the ruthenium surface is blocked by the ligands, catalytic activity is still observed for CO oxidation and H 2 O 2 decomposition. All three ligand‐stabilized ruthenium nanoparticles have similar CO oxidation rates; however, the bisphosphine‐stabilized Ru nanoparticles are approximately 2.5 times less active than the monothiol‐stabilized and monophosphine‐stabilized ruthenium nanoparticles for H 2 O 2 decomposition. It is observed that the organic ligand environment is modulated in situ during nanoparticle synthesis via partial oxidation of the bisphosphine as confirmed by 31 P NMR measurements. We hypothesize that bisphosphine‐bound Ru nanoparticles consist of a Ru core with some of the ligands bound in a monodentate manner where the other P atom is oxidized and not bound to the Ru surface leading to a thicker hydrophobic layer around the Ru nanoparticles. The increase in hydrophobicity is confirmed via contact angle and zeta potential measurements. H 2 O 2 decomposition rates are known to decrease with increasing hydrophobicity, and this work illustrates a pathway for increasing hydrophobicity in situ using ligand‐bound metallic nanoparticles.
CO and/or H 2 O on mesoporous oxide supported metal catalysts are reviewed from our research and literature in 2020–2025, for WGS, CO oxidation, and F–T synthesis, with focuses on advanced spectroscopic techniques and metal–support interactions.
An advanced engineering computational model has been developed to aid in the analysis of chemical rocket engines. The complete multispecies, chemically reacting and diffusing Navier-Stokes equations are modelled, including the Soret thermal diffusion and Dufour energy transfer terms. Demonstration cases are presented for a 1030:1 area ratio nozzle, a 25 lbf film-cooled nozzle, and a transpiration-cooled plug-and-spool rocket engine. The results indicate that the thrust coefficient predictions of the 1030:1 nozzle and the film-cooled nozzle are within 0.2 to 0.5 percent, respectively, of experimental measurements. Further, the model's predictions agree very well with the heat transfer measurements made in all of the nozzle test cases. It is demonstrated that thermal diffusion has a significant effect on the predicted mass fraction of hydrogen along the wall of the nozzle and was shown to represent a significant fraction of the diffusion fluxes occurring in the transpiration-cooled rocket engine.
Abstract The increasing popularity of time‐resolved X‐ray absorption measurements for understanding dynamics in molecular and material systems has led to many advances in table‐top sources for pulsed X‐rays. We report on a table‐top laser‐produced plasma (LPP) source that can perform soft X‐ray (SXR), near‐edge X‐ray absorption fine structure (NEXAFS) measurements using a laser source with 23 ps pulse duration. The spectrometer's key specifications, such as brilliance, resolution, and stability, are characterized against the more commonly used longer‐pulse‐duration LPP sources. The 23 ps laser produced approximately an order of magnitude weaker SXR flux than the 8 ns laser for a higher power density due to the smaller total energy absorbed by the plasma. The increased repetition rate, as well as the use of a high line‐density X‐grating, and a self‐referencing scheme still allowed for NEXAFS measurements of Si 3 N 4 and TiO 2 thin films with 2.5 minute acquisition times, a resolving power of E/ΔE=424, and a signal‐to‐noise ratio of 100. It was observed that degradation of the gas jet nozzle led to long‐term instability of the source, which can be remediated using alternative nozzle designs. This work demonstrates the feasibility of achieving higher temporal resolution in future time‐resolved X‐ray absorption measurements using table‐top LPP sources.
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Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.
Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.
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Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.
The role of thiocyanates in minimising organohalide diffusion into PEDOT:PSS whilst accelerating device degradation is identified and a route towards improving both device efficiency and stability is demonstrated.
Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.
X-ray free electron laser (XFEL) microcrystallography and synchrotron single-crystal crystallography are used to evaluate the role of organic substituent position on the optoelectronic properties of metal–organic chalcogenolates (MOChas). MOChas are crystalline 1D and 2D semiconducting hybrid materials that have varying optoelectronic properties depending on composition, topology, and structure. While MOChas have attracted much interest, small crystal sizes impede routine crystal structure determination. A series of constitutional isomers where the aryl thiol is functionalized by either methoxy or methyl ester are solved by small molecule serial femtosecond X-ray crystallography (smSFX) and single crystal rotational crystallography. While all the methoxy examples have a low quantum yield (0-1%), the methyl ester in the ortho position yields a high quantum yield of 22%. Here, the proximity of the oxygen atoms to the silver inorganic core correlates to a considerable enhancement of quantum yield. Four crystal structures are solved at a resolution range of 0.8–1.0 Å revealing a collapse of the 2D topology for functional groups in the 2- and 3- positions, resulting in needle-like crystals. Further analysis using density functional theory (DFT) and many-body perturbation theory (MBPT) enables the exploration of complex excitonic phenomena within easily prepared material systems.
This study reveals the reaction pathways of 2Al–Fe earth-abundant photocatalytic systems for CO 2 reduction through femto-nanosecond optical transient absorption and microsecond X-ray spectroscopies.
Correction for ‘Ion pair extractant selective for LiCl and LiBr’ by Nam Jung Heo et al. , Chem. Sci. , 2024, 15 , 13958–13965, https://doi.org/10.1039/D4SC03760J.
Cultivating microorganisms in native-like conditions is vital for bioprospecting and accessing now unculturable species. However, there remains a gap in scalable tools that can both mimic native microenvironments and enable targeted recovery of microbes from complex settings. Such approaches are essential to advance our understanding of microbial ecology, predict community functions, and discover previously unidentified biotherapeutics. We present magnetic nanocultures—a high-throughput microsystem for isolating and growing environmental microbes under near-native conditions. These nanoliter-scale bioreactors are encapsulated in semipermeable membranes that form magnetic polymeric microcapsules using iron oxide nanoparticles within polydimethylsiloxane-based shells. This design offers mechanical stability and magnetic actuation, enabling efficient retrieval from soil-like environments. The nanocultures are optimized for optical and biological properties to support microbial encapsulation, growth, and sorting. Our study demonstrates the feasibility of using magnetically responsive microenvironments to cultivate elusive microbes, offering a promising platform for bioprospecting previously uncultured or unknown microbial species.
Structurally ordered PtCo intermetallics are one of the most promising oxygen-reduction catalysts in proton exchange membrane fuel cells (PEMFCs) due to their intrinsically improved catalytic activity and stability relative to PtCo solid-solution alloys. However, increasing the heating temperature to achieve a desirable high degree of ordering results in severe particle agglomeration and low mass activity and stability. Herein, a two-step synthesis approach is developed to create an L1 2 -Pt 3 Co intermetallic structure with an increased ordering degree and well-dispersed ultrafine particles. The first step of the synthesis yields ultrafine Pt nanoparticles that are well-dispersed on the ZIF-8-derived carbon support. The second adsorption step enables us to fine-tune the Pt and Co interfaces, assisted by optimal amino acids, to establish a favorable Co-rich environment around fine Pt nanoparticles, facilitating Co diffusion into the Pt crystalline under mild thermal conditions (<800 °C). In conclusion, this two-step ordered L1 2 -Pt 3 Co catalyst is systematically evaluated using membrane electrode assemblies under heavy-duty vehicle (HDV) conditions and demonstrated exceptional performance and durability, retaining 1.35 A cm -2 only a 7% loss in current density at 0.7 V after an extensive accelerated stress test of 150,000 voltage cycles.
Solar electric propulsion (SEP) technology is currently being used for geostationary satellite station keeping to increase payload mass. Analyses show that advanced electric propulsion technologies can be used to obtain additional increases in payload mass by using these same technologies to perform part of the orbit transfer. In this work three electric propulsion technologies are examined at two power levels for an Atlas 2AS class spacecraft. The on-board chemical propulsion apogee engine fuel is reduced to allow the use of electric propulsion. A numerical optimizer is used to determine the chemical burns which will minimize the electric propulsion transfer time. Results show that for a 1550 kg Atlas 2AS class payload, increases in net mass (geostationary satellite mass less wet propulsion system mass) of 150 to 800 kg are possible using electric propulsion for station keeping, advanced chemical engines for part of the transfer, and electric propulsion for the remainder of the transfer. Trip times are between one and four months.