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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mesoporous Fe-doped MgO nanoparticles as a heterogeneous photo-Fenton-like catalyst for degradation of salicylic acid in wastewater

Mesoporous Fe-doped MgO nanoparticles were synthesized using a facile sol-gel method and utilized for photo-Fenton-like degradation of salicylic acid (SA). The MgO surface dissolution facilitated an increase in the pH under the reaction conditions that allowed the Fe-MgO catalyst to be active without detectable iron leaching. Under simulated solar radiation, SA was completely degraded with an initial rate constant of 0.048 min -1 at the optimal reaction conditions of 500 ppm loading of 5% Fe-MgO, 20 mM H 2 O 2 concentration, and 50 ppm SA concentration. The catalyst was stable over 5 reaction cycles. The Fe-MgO catalyst was shown to have a surface area up to 171 m 2 /g and contain hematite (Fe 2 O 3 ) nanoparticles with octahedrally coordinated iron catalytic centers, as inferred from diffuse reflectance UV–vis measurements. Post-reaction catalyst characterization showed that some Fe 2+ was present in the catalyst due to the redox cycle during the chain initiation and propagation steps of the reaction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Containerless Techniques for in-situ X-Ray Measurements on Materials in Extreme Conditions

This article reviews containerless methods in the context of measuring materials in-situ using x-ray techniques. Non-contact processing enables high chemical purity to be achieved even at temperatures above 2000 °C. The absence of extrinsic heterogenous nucleation allows supercooling and supersaturation of liquids and solutions. In many cases new types of glass can be made, particularly from fragile liquids. Current methods for containerless processing are briefly reviewed. The application of scattering, spectroscopic and emerging x-ray techniques is described. Examples including measurements on molten metal oxides, molten metallic alloys and organic molecules are presented. The work is discussed in the broader context of combining structure and property data to predict how to make new applied materials.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

The Nucleosynthetic Heritage of Bennu

The OSIRIS-REx mission sampled the B-type asteroid Bennu and delivered 121.6 g of material to Earth in September 2023. Nucleosynthetic isotope variations are powerful tracers that can be used to establish relationships between different meteorite groups and the returned samples. They can also constrain the samples’ nucleosynthetic heritage, as well as mixing and reservoir formation in the protoplanetary disk. Nucleosynthetic variations reflect the heterogeneous distribution of isotopically distinct dust in the disk, and as such, they provide evidence for the formation of meteorite parent bodies from isotopically distinct dust reservoirs. They are thus also a means to identify exogenous clasts in Bennu materials. Here, we report Ti and Fe isotope data obtained from Bennu samples and carbonaceous chondrites. We also present Ti isotope data for hand-picked matrix from CV and CR chondrites. An isotopic dataset was obtained on aggregate sample OREX-803015-100 consisting of particles typically <500 μm; therefore, its composition represents an average for Bennu materials. A 20.66 mg aliquot was dissolved at Washington University, St. Louis. From this, 5 mg was analysed for Ti and Fe isotopes on a NeptunePlus MC-ICPMS at ETH Zurich; data from a separate aliquot, analysed at Lawrence Livermore National Laboratory, are reported in a companion abstract. Our results demonstrate a close relationship of Bennu to CI and Ryugu samples and therefore a formation outside the snowline, as hypothesized by the mission. Furthermore, the 50 Ti data from the CV and CR matrix fall consistently below the CI and Bennu values, indicating the presence of a uniform matrix mixed with ⁓0.1–0.7% of refractory inclusions in CIs and Bennu.

M. Schönbächler↗
DOE OSTI2023

Intermittent Defect Fluctuations in Oxide Heterostructures

Abstract The heterogeneous nature, local presence, and dynamic evolution of defects typically govern the ionic and electronic properties of a wide variety of functional materials. While the last 50 years have seen considerable efforts into development of new methods to identify the nature of defects in complex materials, such as the perovskite oxides, very little is known about defect dynamics and their influence on the functionality of a material. Here, the discovery of the intermittent behavior of point defects (oxygen vacancies) in oxide heterostructures employing X‐ray photon correlation spectroscopy is reported. Local fluctuations between two ordered phases in strained SrCoO x with different degrees of stability of the oxygen vacancies are observed. Ab‐initio‐informed phase‐field modeling reveals that fluctuations between the competing ordered phases are modulated by the oxygen ion/vacancy interaction energy and epitaxial strain. The results demonstrate how defect dynamics, evidenced by measurement and modeling of their temporal fluctuations, give rise to stochastic properties that now can be fully characterized using coherent X‐rays, coupled for the first time to multiscale modeling in functional complex oxide heterostructures. The study and its findings open new avenues for engineering the dynamical response of functional materials used in neuromorphic and electrochemical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tuning metal-support interactions in nickel–zeolite catalysts leads to enhanced stability during dry reforming of methane

Ni-based catalysts are highly reactive for dry reforming of methane (DRM) but they are prone to rapid deactivation due to sintering and/or coking. In this study, we present a straightforward approach for anchoring dispersed Ni sites with strengthened metal-support interactions, which leads to Ni active sites embedded in dealuminated Beta zeolite with superior stability and rates for DRM. The process involves solid-state grinding of dealuminated Beta zeolites and nickel nitrate, followed by calcination under finely controlled gas flow conditions. By combining in situ X-ray absorption spectroscopy and ab initio simulations, it is elucidated that the efficient removal of byproducts during catalyst synthesis is conducted to strengthen Ni–Si interactions that suppress coking and sintering after 100 h of time-on-stream. Transient isotopic kinetic experiments shed light on the differences in intrinsic turnover frequency of Ni species and explain performance trends. This work constructs a fundamental understanding regarding the implication of facile synthesis protocols on metal-support interaction in zeolite-supported Ni sites, and it lays the needed foundations on how these interactions can be tuned for outstanding DRM performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗
DOE OSTI2025

Terahertz near-field imaging of sidewall-induced losses in superconducting qubits

Correlating superconducting qubit performance with advanced materials analysis is a key strategy for improving coherence. Existing diagnostics for key properties, such as dielectric loss, structural discontinuity, and interface heterogeneity, often rely on destructive electron microscopy or low-throughput millikelvin measurements. Here, in this study, we demonstrate noninvasive terahertz (THz) nano-imaging/spectroscopy of encapsulated niobium transmon qubits as a high-throughput proxy for performance evaluation. We identify large variations in sidewall near-field signals, implicating sidewall loss and discontinuity as major coherence limiters, and also use THz hyperspectral line scans to probe dielectric responses and field participation at Al junction interfaces.

Kim, Richard H. J. [Ames Laboratory (AMES), Ames, ↗
DOE OSTI2023

ZrO 2 /MWCNT as a Support Platform for Acid and Metal Catalysis in Water: The Investigation of Pd-WO x -ZrO 2 /MWCNT Catalysts (Final Technical Report)

This final technical report covers the final renewal period, 08/01/2019 to 07/31/2020, and the no cost extension 08/01/2020 to 07/31/2021, that was required because of the pandemic interruption. The proposed research was in the area of sustainable recovery of fuels and/or chemicals from biomass; it was by design intended to address fundamental science of the catalysts and that required using a model reaction. The model reaction molecule was phenol because it is a reasonably simply molecule that can be derived from lignin, and requires both hydrogenation and dehydration catalytic reactions that can be performed in water. The needed bifunctional catalyst can be provided by a metal for hydrogenation and by an acid (preferably Brønsted acid) for the dehydration function. Therefore, phenol conversion to an alkane in liquid water at a practical temperature, e.g., 200°C, that will not require extreme pressures, captures many of the complications of biomass derived fuels so it may reveal fundamental science that is relevant to development and implementation of biomass refining.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enabling Efficient Water Splitting with Advanced Materials Designed for High pH Membrane Interface

This project was focused on developing the durable, high-performance materials and interfaces for advanced water splitting, enabling a clear pathway for achieving <$2/Kg H2 (on scale) with efficiency of 43 kWh/kg H 2 via anion exchange membrane (AEM)-based electrolysis. We aimed to advance these final goals via an improved fundamental understanding of both hydrogen and oxygen evolution reactions (HER/OER) leading to novel platinum group metal (PGM)-free catalyst materials in conjunction with critical improvements in membrane and ionomers and gas evolution electrodes with corresponding characterization and testing. Northeastern University (NU) lead this effort focusing on catalyst development and characterization (both in situ and ex situ) while project partners lead improvements in ionomer and membrane materials and will aid in the development of specialized electrode and membrane electrode assemblies. In addition, close collaboration occured with the HydroGEN Energy Materials Network (EMN) National Laboratory consortium including efforts related to use of advanced ionomers, durability protocols and validation of electrolyzer materials (e.g. NREL), multiscale modeling and computation (e.g. LBNL), and molecular dynamics (MD) simulations of the membrane catalyst interface (e.g. SNL). The interactions with HydroGEN included exchange of data and materials as needed to facilitate project success.

08 HYDROGEN↗
NASA NTRS1989

Isotopic microanalysis of returned comet nucleus samples

If isotopic measurements of interplanetary dust particles (IDPs) and primitive meteorites can serve as a guide to the isotopic analysis of returned comet nucleus material, an essential requirement will be the capability for microanalysis. The reason is that in both types of extraterrestrial samples large isotopic heterogeneities on a small spatial scale have become apparent once it was possible to measure isotopes in small samples. In the discovery of large isotopic anomalies the ion microprobe has played a significant role because of its high spatial resolution for isotopic ratio measurements. The largest isotopic anomalies in C, N, O, Mg, Si, Ca and Ti found to date were measured by ion microprobe mass spectrometry. The most striking examples are D/H measurements in IDPs and isotopic measurements of C, N and Si in SiC from the CM chondrites Murray and Murchison.

Zinner, Ernst↗
NASA NTRS2012

Formation and Processing of Amorphous Silicates in Primitive Carbonaceous Chondrites and Cometary Dust

Chondritic-porous interplanetary dust particles (CP IDPs) exhibit strongly heterogeneous and unequilibrated mineralogy at sub-micron scales, are enriched in carbon, nitrogen and volatile trace elements, and contain abundant presolar materials [1-4]. These observations suggest that CP IDPs have largely escaped the thermal processing and water-rock interactions that have severely modified or destroyed the original mineralogy of primitive meteorites. CP IDPs are believed to represent direct samples of the building blocks of the Solar System - a complex mixture of nebular and presolar materials largely unperturbed by secondary processing. The chemical and isotopic properties of CP IDPs and their atmospheric entry velocities are also consistent with cometary origins. GEMS (glass with embedded metal and sulfides) grains are a major silicate component of CP IDPs. GEMS grains are < 0.5 microns in diameter objects that consist of numerous 10 to 50 nm-sized Fe-Ni metal and Fe-Ni sulfide grains dispersed in a Mg-Si-Al-Fe amorphous silicate matrix [2, 5]. Based on their chemistry and isotopic compositions, most GEMS appear to be non-equilibrium condensates from the early solar nebula [2]. If GEMS grains are a common nebular product, then they should also be abundant in the matrices of the most physically primitive chondritic meteorites. Although amorphous silicates are common in the most primitive meteorites [6-9], their relationship to GEMS grains and the extent to which their compositions and microstructure have been affected by parent body processing (oxidation and aqueous alteration) is poorly constrained. Here we compare and contrast the chemical, microstructural and isotopic properties of amorphous silicates in primitive carbonaceous chondrites to GEMS grains in IDPs.

Keller, Lindsay P.↗
NASA NTRS2012

Integrated Structural/Acoustic Modeling of Heterogeneous Panels

A model for the dynamic response of heterogeneous media is presented. A given medium is discretized into a number of subvolumes, each of which may contain an elastic anisotropic material, void, or fluid, and time-dependent boundary conditions are applied to simulate impact or incident pressure waves. The full time-dependent displacement and stress response throughout the medium is then determined via an explicit solution procedure. The model is applied to simulate the coupled structural/acoustic response of foam core sandwich panels as well as aluminum panels with foam inserts. Emphasis is placed on the acoustic absorption performance of the panels versus weight and the effects of the arrangement of the materials and incident wave frequency.

Bednarcyk, Brett, A.↗
NASA NTRS2018

Snow in the Changing Sea-Ice Systems

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow-ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow-ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

Snow↗
DOE OSTI2023

Topography and electrostatic interactions of electret fibres in N95 facemasks

Facemasks have been a critical line of defense in the Covid-19 pandemic and have been used for decades to reduce the inhalation of sub-micron aerosols, including bacteria, viruses and nano-materials. These facemasks leverage electrostatic charge properties of non-woven electret fibre mats to remove sub-micron aerosols. However, the topographical and electrostatic properties of these mats have not been well characterized at the nano-scale. In this study, we use atomic force microscopy (AFM) to examine the surface topography of the electret fibres within a commercial respirator fibre mat, use electrostatic force microscopy to map the electrostatic interactions emanating from these fibres, and use synchrotron X-ray scattering to probe polymer morphology. We find that within these mats the fibres exhibit significant heterogeneity in surface topography, electrostatic interactions and polypropylene phase composition. The surface topographies ranged from nano-smooth surfaces to ordered spherulitic structures. Furthermore, the electrostatic interactions varied across fibre length and circumference and are only weakly correlated with surface topographical features. Finally, we use AFM to characterize fibres after filtering a NaCl aerosol and find that the deposited particles are heterogeneous in size and distributed over the fibre surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Viscoelasticity-induced structure anisotropy in amorphous materials

Viscoelasticity-induced structural change in Zr 55 Cu 30 Ni 5 Al 10 metallic glass (Zr-MG) and amorphous selenium (a-Se) is investigated using synchrotron X-ray diffraction. By analyzing the two-dimensional diffraction pattern, two types of structural anisotropy with the feature of the residual elastic strain or heterogeneous intensity of diffraction rings are revealed. The origin of the structural anisotropy is attributed to the topological rearrangement in the Zr-MG and conformation rearrangement in the a-Se. In conclusion, our findings bring a structural identity to the phenomenological structureless deformation defect widely used in different amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantifying redox heterogeneity in single-crystalline LiCoO 2 cathode particles

Active cathode particles are fundamental architectural units for the composite electrode of Li-ion batteries. The microstructure of the particles has a profound impact on their behavior and, consequently, on the cell-level electrochemical performance. LiCoO 2 (LCO, a dominant cathode material) is often in the form of well-shaped particles, a few micrometres in size, with good crystallinity. In contrast to secondary particles (an agglomeration of many fine primary grains), which are the other common form of battery particles populated with structural and chemical defects, it is often anticipated that good particle crystallinity leads to superior mechanical robustness and suppressed charge heterogeneity. Yet, sub-particle level charge inhomogeneity in LCO particles has been widely reported in the literature, posing a frontier challenge in this field. Herein, this topic is revisited and it is demonstrated that X-ray absorption spectra on single-crystalline particles with highly anisotropic lattice structures are sensitive to the polarization configuration of the incident X-rays, causing some degree of ambiguity in analyzing the local spectroscopic fingerprint. To tackle this issue, a methodology is developed that extracts the white-line peak energy in the X-ray absorption near-edge structure spectra as a key data attribute for representing the local state of charge in the LCO crystal. This method demonstrates significantly improved accuracy and reveals the mesoscale chemical complexity in LCO particles with better fidelity. In addition to the implications on the importance of particle engineering for LCO cathodes, the method developed herein also has significant impact on spectro-microscopic studies of single-crystalline materials at synchrotron facilities, which is broadly applicable to a wide range of scientific disciplines well beyond battery research.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous catalysis. Herein, we investigate different UQ methods applied to a crystal graph convolutional neural network to predict adsorption energies of molecules on alloys from the Open Catalyst 2020 dataset, the largest existing heterogeneous catalyst dataset. We apply three UQ methods to the adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout, and evidential regression. The effectiveness of each UQ method is assessed based on accuracy, sharpness, dispersion, calibration, and tightness. Evidential regression is demonstrated to be a powerful approach for rapidly obtaining tunable, competitively trustworthy UQ estimates for heterogeneous catalysis applications when using neural networks. Recalibration of model uncertainties is shown to be essential in practical screening applications of catalysts using uncertainties.

36 MATERIALS SCIENCE↗