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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Further, models trained through this workflow achieved an error (mean absolute error of 0.43 J mol –1 K –1 ) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Solvent-free synthesis of lanthanide oxide and mixed lanthanide oxide nanoparticles

Lanthanide oxides and mixed lanthanide oxides can be produced using furnace or microwave assisted solid-state synthesis. The use of Ln-tri(methylsilyl)amide-based precursors yields spherical nanoparticles. The formation of spherical shaped nanoparticles is likely due to the preferential single-step decomposition of the Ln-TMS as well as the low activation energy to overcome decomposition. Reaction temperature, initial metal ion ratio, and reaction dwell time can be used to control the final nanoparticle size. The method enables solvent-free, high-yield synthesis of morphology-controlled lanthanide oxides.

Treadwell, LaRico Juan↗
DOE OSTI2020

Characteristics of Uranium-Plutonium Mixed Oxide Nondestructive Assay Standards

A set of six well-characterized, homogeneous, mixed HEU-WGPu oxide standards has been manufactured for and are proposed for qualification as NDA working reference standards with SAFE-NMCA. These standards, if approved, will provide the basis for qualifying the FRAM isotopic systems for use in accountability measurements of 235U in the presence of Pu for unknown mixed U-Pu items by nondestructive assay. The U to Pu elemental mass ratios of the six analytical standards are nominally in the proportions 0.1, 0.25, 1.0, 2.5, 10, and 20; these values were chosen to span the U/Pu mass ratios of inventory items that ultimately will be measured by nondestructive assay and which currently reside in long term storage. The measured characteristics, including plutonium and uranium elemental masses and isotopic distribution, of each standard and relevant source documentation are reported here.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗
DOE OSTI2023

Single-step conversion of ethanol into n-butene-rich olefins over metal catalysts supported on ZrO 2 -SiO 2 mixed oxides

With airlines committed to drastically reduce their carbon footprint by 2050, producing jet fuel from renewable ethanol is of particular interest. Here we reported on an Ag/ZrO 2 /SBA-16 catalyst that is very effective for directly converting ethanol into to n-butene-rich olefins jet fuel precursors (i.e., 88% at full conversion). Here, we report on a Cu/ZrO 2 /SBA-16 catalyst that presents remarkable olefins selectivity (i.e., 89% at 96% conversion) and enhanced stability as compared to Ag/ZrO2/SBA-16 catalyst. Under severe operating conditions a conversion loss < 10% was observed with the Cu/ZrO 2 /SBA-16 catalyst as compared to a 50% loss of conversion with the Ag/ZrO 2 /SBA-16 catalyst. Combined experimental and computational tools revealed that replacing Ag with Cu shifts the reaction pathway of crotonaldehyde hydrogenation from 1,3-butadiene (i.e., coke precursor) production to butyraldehyde formation. Experiments conducted with 4%Cu/4%ZrO 2 supported on SBA-16, dealuminated zeolite Beta, and aluminum silicate revealed the performance and stability advantage of the SBA-16-supported catalyst.

09 BIOMASS FUELS↗
DOE OSTI2022

Preparation and Characterization of Model Homotopic Catalysts: Rh Adatoms, Nanoparticles, and Mixed Oxide Surfaces on Fe 3 O 4 (001)

The atomic-level characterization of active sites is essential to understanding the mechanisms behind catalytic reactions. In this study, using scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS), we follow the morphological changes of a model Rh catalysts supported on Fe 3 O 4 (001) as a function of temperature and Rh coverage. Here, we identify the preparation conditions leading to model catalysts containing homotopic or nearly homotopic Rh species bound as adatoms, small clusters, substitutional within the Fe 3 O 4 (001), and nanoparticles. Adsorbates such as CO and CO 2 are further subsequently used to characterize the properties of different Rh sites. Using temperature programmed desorption (TPD), we demonstrate that adatoms, clusters, and nanoparticles exhibit high-temperature CO desorption (250-600 K). Strong binding on such sites further allows for CO oxidation to CO 2 via the Mars-van-Krevelen mechanism. In contrast, CO 2 was found to interact weakly with all Rh sites. Differences in desorption temperature enable the use of CO and CO 2 as titration methods for nanoparticles and Fe 3 O 4 (001), respectively. A small quantity of CO 2 was found to be reduced to CO on Rh adatoms and clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Reactive Capture and Conversion of Carbon Dioxide to Methanol with ZnZrO 2 and Alkali-Promoted Mg 3 AlO x Mixed Oxide Catalytic Sorbents

Reactive capture and conversion (RCC) explores the use of a single-unit process to capture CO 2 and produce a product, in this case, methanol (MeOH). In this study, different configurations of a catalytic sorbent (CS) composed of ZnZrO 2 catalyst and Mg 3 AlO x sorbent with and without alkali modification are evaluated for CO 2 adsorption, steady-state catalysis with cofed CO 2 and H 2 , and transient RCC performance. A catalyst composed of a physical mixture of Mg 3 AlO x with ZnZrO 2 resulted in a slight increase in CO 2 uptake, with a low impact on the catalytic activity and RCC of the materials compared to ZnZrO 2 alone. In contrast, Na impregnation significantly increased the level of CO 2 uptake from 0.28 mmol/g (ZnZrO 2 alone) to 0.6 and 1.1 mmol/g for the CS with Na on the catalyst or Mg 3 AlO x , respectively. However, Na impregnation reduced the CO 2 conversion rate and MeOH selectivity during steady-state cofeed experiments at 300 °C and 6 bar. In contrast to steady-state catalysis conditions, RCC, which is a cyclic capture and conversion process, creates dynamic CO 2 and H 2 surface coverages, favoring CH 4 in the early stages of the conversion step and then CO and MeOH as the catalyst CO 2 coverage reduces. The highest MeOH productivity during RCC was achieved with CS that balanced the CO 2 uptake with only moderate catalyst rate reductions caused by Na addition. The optimal material, ZnZrO 2 +10%Na/Mg 3 AlO x , achieved a CO 2 uptake of 0.8 mmol/g and a MeOH productivity of 0.5 mmol/g with 100% selectivity at 260 °C and 6 bar during RCC. This marks the highest RCC MeOH productivity reported to date, although the process needs further optimization and even with optimization, may remain impractical. The results further demonstrate that optimization of catalytic sorbents under steady-state flow conditions does not easily correlate to transient capture and conversion cycles for methanol synthesis from CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗