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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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We present a generalizable database architecture ESAMP that captures the complete provenance associated with a material. We demonstrate this architecture and provenance based machine learning on one of the largest experimental materials databases.
Particle-to-supercritical carbon dioxide (sCO 2 ) heat exchanger is a critical component in next-generation concentrating solar power (CSP) plants. The inherently low heat transfer between falling particles and sCO 2 imposes a challenge toward economic justification of levelized cost of electricity produced through solar energy. Introduction of integrated porous media with the walls bounding particle flow has the potential to enhance the overall particle-to-sCO 2 heat exchanger performance. Here, this paper presents an experimental study on heat transfer characterization of additively manufactured lattice frame material based on Octet-shaped unit cell with particles and air as working fluids. The lattice structures were additively manufactured in stainless steel (SS) 316L and SS420 (with 40% bronze infiltration) via Binder jetting process, where the lattice porosities were varied between 0.75 and 0.9. The mean particle diameters were varied from 266 μm to 966 μm. The effective thermal conductivity and averaged heat transfer coefficient were determined through steady-state experiments. It was found that the presence of lattice enhances the effective thermal conductivity by 2–4 times when compared to packed bed of particles alone. Furthermore, for gravity-assisted particle flow through lattice panel, significantly high convective heat transfer coefficients ranging from 200 W/m2K to 400 W/m2K were obtained for the range of particle diameters tested. The superior thermal transport properties of Octet-shape-based lattice frame for particle flow makes it a very promising candidate for particle-to-sCO 2 heat exchanger for CSP application.
Accurate electron attenuation lengths are of critical importance in using electron spectroscopic methods to quantitatively characterize complex materials. Here we show that analysis of core-level and valence-band x-ray photoelectron spectra excited with monochromatic AlK? x-rays from the substrate and measured as a function of film thickness can be used to determine electron attenuation lengths in epitaxial SrTiO3 films on Ge(001). Closely lattice-matched epitaxial heterojunctions are ideal systems for determining attenuation lengths provided the films grow in a layer-by-layer fashion, leading to atomically flat surfaces, and the buried interfaces are atomically abrupt. In principle, either the rate of attenuation of substrate peak intensities or the rate of increase of film peak intensities can be used for this purpose. However, we find that structural nonuniformities in the films reduce the accuracy of electron attenuation lengths determined from photoelectrons that originate within the films. A more reliable source of information is found in photoelectrons from the substrate which traverse the film. By using the energy dependence of calculated electron attenuation lengths from the NIST database in combination with Ge 3d core and Ge-derived valence band intensities, we determine electron attenuation length as a function of kinetic energy for SrTiO3.
To combat the global problem of carbon dioxide emissions, hydrogen is the desired energy vector for the transition to environmentally benign fuel cell power. Water electrolysis (WE) is the major technology for sustainable hydrogen production. Despite the use of renewable solar and wind power as sources of electricity, one of the main barriers for the widespread implementation of WE is the scarcity and high cost of platinum group metals (pgms) that are used to catalyse the cathodic hydrogen evolution reaction (HER) and the anodic oxygen evolution reaction (OER). Hence, the critical pgm-based catalysts must be replaced with more sustainable alternatives for WE technologies to become commercially viable. This critical review describes the state-of-the-art pgm-free materials used in the WE application, with a major focus on phosphides and borides. Several emerging classes of HER and OER catalysts are reviewed and detailed structure-property correlations are comprehensively summarised. The influence of the crystallographic and electronic structures, morphology and bulk and surface chemistry of the catalysts on the activity towards OER and HER is discussed.
The Activated Materials Laboratory (AML), located in the Long Beamline Building (LBB) of the Advanced Photon Source (APS) of Argonne National Laboratory (ANL), serves as a centralized radiological facility for preparing radioactive samples for APS beamline experiments. The AML is equipped to receive shipments, handle open-form radioactive materials, encapsulate samples, and transport samples to-and-from beamline end-stations. The AML works closely with users and the APS radiological safety committee to make sure the safe conduct of experiments.
The National Laboratory of the Rockies (NLR) and AltiSora, LLC., will develop new lightweight cementitious composite material technologies that will utilize wind turbine blade waste as a raw material to; (1) allow a high value addition (2) at low cost, enabling (3) a significant waste consumption volume; while (4) consuming the entire wind turbine blade, without (5) creating any waste or emissions and to also (6) offer specific benefits to communities involved.
The invention provides a cathode active material having a discrete change in concentrations of a first base region and a second pulse region. Also provided is a method for preparing a cathode active material, the method having the steps: supplying chelating agent, aqueous basic solution and a first aqueous metal salt solution to a reactor to create a base region; supplying a second aqueous metal-salt solution to a reactor to form a pulse region, wherein the second aqueous metal-salt solution is intermittently or continuously added during or after the creation of the base region; thermally treating the base region and the pulse region to create active metal precursors; mixing the precursors with lithium salt to produce a mixture; and thermally treating the mixture.
A method of turning a catalytic material by altering the charge state of a catalyst support. The catalyst support is intercalated with a metal ion, altering the charge state to alter and/or augment the catalytic activity of the catalyst material.
Many chemical processes depend on having an environment that is low in oxygen partial pressure (P O2 < 100 Pa); sorption pumps are a promising route to establishing that environment by either oxygen pumping or oxygen separation from an inert gas. Near ambient sorption-based processes rely on either pressure or thermal swings, requiring no moving parts, no electricity, and neither very high nor very low temperatures. In this work, we use ab initio calculations to explore zeolites as a class of materials for sorption based oxygen pumping/separation. Our calculations indicate that while the neat ALPO-5 zeolite does not adsorb O 2 , O 2 does adsorb on zeolites selectively substituted with transition metals and metalloids and hence, can enable separation and pumping. ALPO-5 substituted with Si, Ge, Sn, Pd, Pt, Ti, V, Cr, Mn, Zr, Mo, Hf, W, Ce, and Pr provides adsorption energies ranging from -0.19 to -3.92 eV, (-) indicates exothermic process. Additionally, we provide a comprehensive understanding of what controls the adsorption energy: 1) the substi-tutions must be able to adopt an oxidation state that is more positive than the cation it replaces, and 2) the size of the pore into which the O 2 adsorbs to the wall. Additionally, we conduct a ther-modynamic analysis of a thermal swing cycle to approximate the optimal O 2 binding energy for 2 low energy O 2 pumping/separation. We find that the minimum energy cost likely occurs when the adsorption energy is in the range of 0.75 – 1.00 eV (72 – 97 kJ·mol -1 ), which corresponds to Ge, V, Pt, or Ce substituted ALPO-5.
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Municipal solid waste infrastructure in the United States contains complex supply chains to transport, sort, and sequester waste. This project developed models to estimate the environmental and economic costs of waste infrastructure in the U.S. using data from several municipalities. Life cycle costing (LCC) models were developed for waste management relevant supply chain steps, including waste collection, sorting, landfilling, waste-to-energy, ash processing, and recycling. A waste-to-materials and energy (WTM&E) life cycle analysis (LCA) and LCC dashboard was developed in collaboration with Argonne National Laboratory (ANL) and an LCC tool was developed for additional analysis.
The presentation is to give an overview of the Nuclear Science User Facilities (NSUF) and its materials acquisition routes, sample library, and concrete related awards, at the NRC-NEA Hybrid Workshop on International Concrete Harvesting.
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The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.
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