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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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Liquid/liquid extraction (LLE) is one of the most industrially relevant separations methods. Adsorbed surfactant is demonstrated to enhance interfacial heterogeneity and lead to water protrusions that form the basis for transport into the organic phase.
Abstract The ability to redirect electron transport to new reactions in living systems opens possibilities to store energy, generate new products, or probe physiological processes. Recent work by Huang et al. showed that 3D crystals of small tetraheme cytochromes (STC) can transport electrons over nanoscopic to mesoscopic distances by an electron hopping mechanism, making them promising materials for nanowires. However, fluctuations at room temperature may distort the nanostructure, hindering efficient electron transport. Classical molecular dynamics simulations of these fluctuations at the nano‐ and mesoscopic scales allowed us to develop a graph network representation to estimate maximum electron flow that can be driven through STC wires. In longer nanowires, transient structural fluctuations at protein‐protein interfaces tended to obstruct efficient electron transfer, but these blockages are ameliorated in thicker crystals where alternative electron transfer pathways become more efficient. The model implies that more flexible proteinprotein interfaces limit the required minimum diameter to carry currents commensurate with conventional electronics.
Freestanding and lightweight thin-films were rationally designed to serve as robust electrodes for renewable energy applications. A facile and scalable nanomanufacturing process was developed to fabricate these transformative thin-film electrodes (iron group mixed oxides) that exhibit a nanoporous structure and controllable composition. More specifically, electrodeposition and anodic treatments were employed to produce freestanding and lightweight metal oxides nanoporous layers (NPL). These NPL can be directly used as flexible and additive-free electrodes for renewable energy generation (water splitting) and storage (supercapacitor) applications without requiring binders and current collector and other additives. Significantly enhanced electrochemical performance was achieved due to the unique merits of the NPL: i) highly porous structure considerably increases the electrode/electrolyte interface, which facilitate electrochemical reactions; ii) NPL substantially increase the number of active sites that are favorable in electrochemical reactions; iii) residual metal network within the NPL forms a conductive framework, drastically improving electrode strength, flexibility and conductivity.
We report a Many Body Energy (MBE) analysis of aqueous ionic clusters containing anions and cations at the two opposite ends of the Hofmeister series, viz. the kosmotropes Ca2+, SO42- and chaotropes NH4+ and ClO4- with 9 water molecules to quantify the how these ions in altering the interaction between the water molecules in their immediate surrounding. The current results are contrasted to the ones reported earlier for water clusters as well as for alkali metal and halide ion aqueous clusters of the same size, which lie in the middle of the Hofmeister series. Through this analysis, noteworthy differences between the MBE of kosmotropes and chaotropes were identified. The MBE of kosmotropes is dominated by ion-water interactions that extends beyond the 4-body term, the point at which the MBE of pure water converges. The percentage contribution of the 2- B to the total cluster binding energy is noticeably larger. The disruption due to the dominant ion results in weak, unfavorable water-water interactions. The MBE for chaotropes, on the other hand, was found to converge more quickly as it more closely resembles that of pure water clusters. Chaotropes exhibit weaker overall binding energies and ion-water interactions with more favorable water-water interactions, somewhat recovering the pattern of the 2-4 body terms exemplified by pure water clusters. More importantly, both kosmotropic and chaotropic ions exhibit an anticorrelation between the 2-B ion-water (I-W) and water-water (W-W) interactions as well as between the 3-B (I-W-W) and (I-W) interactions. The consideration of two different structural arrangements (ion inside and outside of a water cluster) suggests that fully solvated (ion inside) chaotropes disrupt the hydrogen bonding network in a similar manner as partially solvated (ion outside) kosmotropes and offer useful insights into the modeling requirements of bulk vs. an interface. Finally, the 2-B contribution to the total Basis Set Superposition Error (BSSE) correction for the kosmotropic and chaotropic ions follows the previously reported erf profile vs. intermolecular distance. When scaled for the corresponding dimer energies and distances, a single profile fits the current results together with all previously reported ones for the pure water and halide water clusters.
Here, the magnetotransport properties of thin manganite films (La 0.7 Ca 0.3 MnO 3 ) coupled with arrays of permalloy (Py) nanodots deposited on the surface of the film are studied as a function of temperature, magnetic field, and the size of the dots. In the presence of the magnetic dots, a reduction of the electrical resistivity is observed, especially at the insulator-to-metal transition, as well as a shift of the transition peak towards higher temperatures. This indicates that, due to local interface exchange coupling, highly conductive ferromagnetic domains are nucleated in the manganite film underneath the Py nanodots. The use of a simplified resistor network model allows us to estimate the size of the metallic regions induced by exchange coupling. At low temperatures, these regions extend ~70 nm beyond the edge of the nanodots, a length scale comparable to the correlation length of the ferromagnetic clusters in the phase-separated state of La 0.7 Ca 0.3 MnO 3 .
The Infrastructure Expression: Discovery Tools is a toolset that allows for the automated collection of various network, hardware, and software vulnerabilities and details to be collected from systems on a given network. These details are then converted into a STIX 2.1 format, allowing for easy viewing in existing applications as well as sharing and interfacing with tools that already use the STIX 2.1 format.
Optically linked solid-state quantum memories such as color centers in diamond are a promising platform for distributed quantum information processing and networking. Photonic integrated circuits (PICs) have emerged as a crucial enabling technology for these systems, integrating quantum memories with efficient electrical and optical interfaces in a compact and scalable platform. Packaging these hybrid chips into deployable modules while maintaining low optical loss and resiliency to temperature cycling is a central challenge to their practical use. We demonstrate a packaging method for PICs using surface grating couplers and angle-polished fiber arrays that is robust to temperature cycling, offers scalable channel count, applies to a wide variety of PIC platforms and wavelengths, and offers pathways to automated high-throughput packaging. Using this method, we show optically and electrically packaged quantum memory modules integrating all required qubit controls on chip, operating at millikelvin temperatures with <3 dB losses achievable from fiber to quantum memory for the TE 0 mode at a wavelength of 737 nm.
A method for emulating threats in virtual network computing environment is provided. The method comprises creating a number of virtual machines in the virtual network computing environment. A number of threat actors are emulated, wherein each threat actor comprises a number of threat artifacts that form a sequence of attack steps against the virtual network computing environment. The threat actors are then deployed against the virtual network computing environment. Behavioral data about actions of the threat actors in the virtual network computing environment is collected, as is performance data about the virtual network computing environment in response to the threat actors. The collected behavioral and performance data is then presented to a user via an interface.
The fields of coordination polymers (CPs) and supramolecular metallopolymers (SMPs) have been extensively studied for decades. Spectacular recent advances in both fields have created new compounds that lie at the interface between these two classes of organic-inorganic hybrid materials. At this interface, materials based on molecular weaving, supramolecular clusters, and metal-organic framework-polymer hybrids have emerged. This minireview provides a perspective on the intellectual emergence and connection between different supramolecular constructs and the state of the art with respect to this new materials interface.
Monitoring plays an important role in supporting remedial investigations and selection of remedies for groundwater contamination and for assessing performance during implementation of remedies. When a groundwater plume is near the interface between groundwater and surface water, the dynamics of groundwater flow at this interface needs to be considered in the design and implementation of a remedy. For this and other aquifer situations, groundwater flow dynamics may be difficult to evaluate through groundwater head measurements due to a sparse monitoring-well network, a relatively flat hydraulic gradient, or the presence of induced gradients from pumping operations or groundwater mounds. Geophysical approaches, coupled with other targeted measurements or the use of an ionic tracer, provide temporal data that can quantify groundwater flow dynamics. Recent developments include a flux-monitoring tool that measures fluid conductivity, bulk conductivity, and temperature at the interface between groundwater and surface water. Joint interpretation of these local data with temporal two- or three-dimensional geophysical data improves quantification of groundwater and mass flux at a targeted location. In addition, geophysical design improvements that enable the use of numerical simulations to evaluate implementation configurations and expected data resolution for geophysical methods identify threshold conditions for successful monitoring and identify the most cost-effective configurations to meet monitoring objectives.
Monitoring plays an important role in supporting remedial investigations and selection of remedies for groundwater contamination and for assessing performance during implementation of remedies. When a groundwater plume is near the interface between groundwater and surface water, the dynamics of groundwater flow at this interface needs to be considered in the design and implementation of a remedy. For this and other aquifer situations, groundwater flow dynamics may be difficult to evaluate through groundwater head measurements due to a sparse monitoring-well network, a relatively flat hydraulic gradient, or the presence of induced gradients from pumping operations or groundwater mounds. Geophysical approaches, coupled with other targeted measurements or the use of an ionic tracer, provide temporal data that can quantify groundwater flow dynamics. Recent developments include a flux-monitoring tool that measures fluid conductivity, bulk conductivity, and temperature at the interface between groundwater and surface water. Joint interpretation of these local data with temporal two- or three-dimensional geophysical data improves quantification of groundwater and mass flux at a targeted location. In addition, geophysical design improvements that enable the use of numerical simulations to evaluate implementation configurations and expected data resolution for geophysical methods identify threshold conditions for successful monitoring and identify the most cost-effective configurations to meet monitoring objectives. (authors)
Ducted fuel injection (DFI) is a proposed fuel injection concept for achieving substantial reductions in emissions. In this concept, the fuel is injected through a coannular duct, resulting in increased fuel-air mixing and minimized formation of soot and other unwanted combustion products. Apart from comprehensive experimental investigations on DFI, so far computational studies have been limited to single-point Reynolds-averaged Navier Stokes simulations. Therefore, the objective of this work is to complement these studies by performing large-eddy simulations using a diffuse-interface method to examine the physical mechanisms and combustion processes of DFI, specifically focusing on the mixing process and the effect of fuel-ducting on combustion and pollutant emissions. To this end, finite-rate chemistry simulations are performed of the DFI configuration corresponding to the Engine Combustion Network Spray A injector at transcritical conditions (n-dodecane fuel, 60 bar pressure and 1000 K temperature chamber conditions). A two-equation soot model is employed for the qualitative analysis of soot emissions. Direct comparisons of averaged and instantaneous flow field results with the Spray A configuration are performed to assess the effect of DFI on the first- and second-stage ignition and soot formation. Compared to the free-spray condition, the results show that the DFI case exhibits a combination of (i) increased mass flow rate and entrained air, (ii) larger pressure drop magnitude and flow velocity, and (iii) a closer-to-stoichiometric mixture composition (both globally and locally), each of which is conjectured to contribute toward reduced soot production.
SF-22-156 Machine learning (ML) models and applications in materials design and discovery typically involve the use of feature representations or descriptors followed by a learning algorithm that maps them to user desired properties of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments and suffer from transferability issues across different application domains and/or material classes. The CEGANN code provides a unified interface to facilitate material characterization across materials across multiple scales (from atomic to mesoscale) and diverse classes of materials ranging from metals oxides, non-metals, and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. CEGANN implements a Graph Attention Network (GAT) type convolution architecture. The details of network architecture can be found in the paper https://doi.org/10.48550/arXiv.2207.10168. The software comes with pretrained examples and dataset for the classification of the following representative systems: (1) Structure-level representation such as space group (2) Structural dimensionality (e.g., bulk, 2D, clusters etc.) (3) Grain boundary identification (4) Nucleation and growth of a zeolite polymorph (5) Characterization of binary mesophases and their phase transitions (6) Growth of ice. The code is written in python programming language.
We examine synthesized standards of structurally related ß-caryophyllene ozonolysis products at the air/water and air/(NH4)2SO4 (aq) interface using polarization-resolved standard- and high-resolution vibrational sum frequency generation (SFG) spectroscopy in the C–H and O–H stretching regions. ß-Caryophyllene aldehyde (1), the most surface-active molecule in the suite, appears to undergo a change in its molecular orientation distribution when transitioning from low to high relative surface coverage. We also report that the water molecules present at the outermost layer of the aqueous interface are displaced by ß-caryophyllene aldehyde (1) on both the (NH4)2SO4 (aq) and water subphases. Additionally, the more weakly hydrogen-bonded water network observed around ~3400 cm-1 seems to be perturbed to a greater extent by ß-caryophyllene aldehyde (1) than the more strongly hydrogen-bonded population observed at ~3200 cm-1 on the (NH4)2SO4 (aq) subphase, while this effect is not observed on the water subphase. We provide surface spectroscopic evidence for the “salting out” effect of (NH4)2SO4 in aqueous environments and discuss the implications for understanding the organization of surface-localized molecules present in aqueous cloud-forming droplets containing dissolved aerosol cloud condensation nuclei (CCN) in the atmosphere. Despite the importance of elucidating the surface activity and interfacial organization of individual molecules in aqueous media, future work will focus on how the combined effects of several surface-active constituents dictate overall cloud activation potentials of aerosol particles.
The Energy Systems Integration Facility (ESIF), located at the National Renewable Energy Laboratory (NREL) South Table Mountain campus, is a world-renowned user facility for research and development of modern, advanced, and clean energy technologies. ESIF is distinguished by its continuously evolving, highly integrated systems that span throughout the building, connecting research capabilities across multiple laboratories and test areas. The primary ESIF research systems include: [1] data, cyber, and control networks, [2] research electrical distribution buses (REDB), [3] thermal integration infrastructure, and [4] hydrogen systems. The data, cyber, and control networks provide monitoring, control, communication, automation, visualization, and time series data storage and tagging capabilities for research projects and ESIF systems, including facility safety functions. The REDB system consists of four dedicated AC and DC electrical power networks that can connect devices located across the facility through versatile, automatic circuit configuration to support complex power electronics experiments up to the megawatt-scale. The thermal integration infrastructure consists of three temperature-conditioned water loops that provide heating and cooling interfaces and capabilities for thermal energy research. The hydrogen systems provide megawatt-scale hydrogen production, drying, compression, high-pressure storage, and delivery to laboratory end uses, including hydrogen fuel cell vehicle fueling. The ESIF research systems interconnect and extend throughout the various lab areas of the facility to create elaborate networks composed of diverse technologies for cutting-edge research. The ESIF capabilities are operated and stewarded by the ESIF Research Operations group, who also actively upgrade and advance the systems to ensure they remain ahead of anticipated research - enabling the success of many pioneering energy integration projects. The poster, created by members of the ESIF Research Operations team, highlights and summarizes the four core integrated systems at ESIF. The poster was first presented at the internal NREL Energize Forum on May 13th, 2024, and received the "Best Poster" award.
To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.
The XRCC1–DNA ligase IIIα complex (XL) is critical for DNA single-strand break repair, a key target for PARP inhibitors in cancer cells deficient in homologous recombination. Here, we combined biophysical approaches to gain insights into the shape and conformational flexibility of the XL as well as XRCC1 and DNA ligase IIIα (LigIIIα) alone. Structurally-guided mutational analyses based on the crystal structure of the human BRCT–BRCT heterodimer identified the network of salt bridges that together with the N-terminal extension of the XRCC1 C-terminal BRCT domain constitute the XL molecular interface. Coupling size exclusion chromatography with small angle X-ray scattering and multiangle light scattering (SEC-SAXS–MALS), we determined that the XL is more compact than either XRCC1 or LigIIIα, both of which form transient homodimers and are highly disordered. The reduced disorder and flexibility allowed us to build models of XL particles visualized by negative stain electron microscopy that predict close spatial organization between the LigIIIα catalytic core and both BRCT domains of XRCC1. Together our results identify an atypical BRCT–BRCT interaction as the stable nucleating core of the XL that links the flexible nick sensing and catalytic domains of LigIIIα to other protein partners of the flexible XRCC1 scaffold.
Transport Layer Security (TLS) is a cryptographic protocol that encrypts communication data, providing end-to-end communication encryption and authentication. Currently, TLS is widely adopted for securing communication between servers and end devices, including solar inverter systems. Therefore, users/operators can securely access the solar inverters through a web user interface (WebUI) application programmable interface (API) on a PC or server over TLS-enabled Wi-Fi or Ethernet. However, the security of the TLS-based network becomes compromised if it is breached by a TLS proxy man-in-the-middle (MITM) exploit. This report explores potential vulnerabilities in a commercial solar inverter system that leverages a TLS proxy MITM and discusses the impacts through assume-breached penetration testing. Furthermore, the paper explores recommended mitigation methods against the TLS proxy MITM exploit in solar inverters.