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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

How to create an artificial magnetosphere for Mars

If humanity is ever to consider substantial, long-term colonization of Mars, the resources needed are going to be extensive. For a long-term human presence on Mars to be established, serious thought would need to be given to terraforming the planet. One major requirement for such terraforming is having the protection of a planetary magnetic field - which Mars currently does not have. Here, the Earth's magnetosphere helps protect the planet from the potential sterilizing effects of cosmic rays and also helps retain the atmosphere, which would otherwise by stripped by large solar storms as they pass over the planet. Mars does have small patches of remnant surface magnetic field, but these are localized in the southern hemisphere and are not of sufficient size or magnitude to protect the planet or a colony.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

In this work, we developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

42 ENGINEERING↗
DOE OSTI2025

Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence

Continuous measurement and monitoring of surface water coverage in non-perennial streams are essential for understanding the exchange fluxes between surface and subsurface waters under both inundated and non-inundated conditions. In this study, a wildlife camera photo-based framework was developed to monitor small stream water inundation, depth, discharge, and velocity. Two advanced machine learning models, YOLOv8 and Mask2Former, were utilized to efficiently analyze images captured by wildlife cameras. The accuracy of the framework was validated against on-site depth measurements at six sites in the Yakima River Basin, along with the gage height, discharge, and velocity data from four USGS sites. This approach facilitates long-term, continuous monitoring and quantification of river intermittency and water availability with high precision and low cost, thereby advancing river ecosystem research and management.

machine learning↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. Here, the porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ~95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Heterobinuclear Light Absorber Coupled to Molecular Wire for Charge Transport across Ultrathin Silica Membrane for Artificial Photosynthesis

Coupling of robust, all-inorganic heterobinuclear light absorbers to metal oxide catalysts for water oxidation across an ultrathin product-separating silica membrane requires charge transfer through organic molecular wires embedded in the silica. A synthetic approach for assembling the bimetallic units on the silica surface is introduced that is compatible with the presence of encapsulated organic molecules. Accurate selection and fine tuning of the concentration of embedded conducting wires are enabled by a two-step method consisting of surface attachment of a tripodal anchor, trimethoxysilyl aniline, followed by attachment of p-oligo(phenylene vinylene) through amide linkage. Each step of the assembly process was monitored and characterized by a combination of Fourier transform infrared, Fourier transform-Raman, and UV-vis spectroscopy techniques. Hole transfer was observed from transient Co III , formed by Ti IV OCo II → Ti III OCo III charge transfer excitation of the chromophore, to p-oligo(phenylene vinylene) molecule within the 8 ns width of the photolysis laser pulse by transient optical absorption spectroscopy of the wire radical cation. The rectifying property of the light absorber-wire assembly enabled by appropriate selection of redox potentials of metals and embedded wire obviates the need for a molecularly defined linkage between the components. Combined with the previously observed ultrafast hole injection from the embedded wires to Co oxide catalyst, the result implies visible-light-induced hole transfer from visible-light-excited binuclear light absorber to water oxidation catalyst across the silica separation membrane in a few nanoseconds or faster. Finally, demonstration and understanding of this interfacial charge-transfer step is critical for developing nanoscale core-shell architectures for complete photosynthetic cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Fabrication of Core–Shell Nanotube Array for Artificial Photosynthesis Featuring an Ultrathin Composite Separation Membrane

We report macroscale arrays of cobalt oxide-silica core-shell nanotubes with high aspect ratio and ultrathin walls of less than 20 nm have been fabricated. The silica shells feature embedded oligo-para(phenylenevinylene) molecules for charge transport across the insulating silica layer, which is tightly controlled by their electronic properties. The assembly is based on the use of a sacrificial Si nanorod array template combined with atomic layer deposition, covalent anchoring of organic wire molecules, and dry cryo-etching. High-resolution TEM imaging of samples prepared by microtome affords structural details of single core-shell nanotubes. The integrity of silica-embedded organic wire molecules exposed to atomic layer deposition, thermal treatment, and harsh etching procedures is demonstrated by grazing angle ATR FT-IR, FT-Raman, and XPS spectroscopy. The inorganic oxide-based core-shell nanotubes with ultrathin gas-impermeable, proton-conducting silica shells functionalized by molecular wires enable complete nanoscale photosynthetic units for CO 2 reduction by H 2 O under membrane separation. Arrays of massive numbers of such core-shell nanotube units afford a design that extends the separation of the incompatible H 2 O oxidation and CO 2 reduction catalysis environments across the continuum of length scales from nanometers to centimeters.

molecular wires↗