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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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All sourcesNASA NTRSDOE OSTI
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↗
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↗
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↗
DOE OSTI2021

Azo(xy) vs Aniline Selectivity in Catalytic Nitroarene Reduction by Intermetallics: Experiments and Simulations

Intermetallic nanoparticles are promising catalysts in hydrogenation and fuel cell technologies. Much is known about the ability of intermetallic nanoparticles to selectively reduce nitro vs alkene, alcohol, or halide functional groups; less is known about their selectivity toward aniline vs azo or azoxy condensation products that result from the reduction of a nitro group alone. Because azo(xy)arenes bear promise as dyes, chemical stabilizers, and building blocks to functional materials but can be difficult to isolate, developing high surface area nanoparticle catalysts that display azo(xy) selectivity is desirable. To address this question, we studied a family of nanocrystalline group 10 metal (Pd, Pt)- and group 14 metal (Ge, Sn, Pb)-containing intermetallics-Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , Pd 3 Pb, and PtSn-in the catalytic reduction of nitroarenes. In contrast to monometallic Au, Pt, and Pd nanoparticles and "random" PdxSn1 – x nanoalloys, which are selective for aniline, nanoparticles of atomically precise intermetallic Pd 2 Ge, Pd 2 Sn, Pd 3 Sn 2 , and PtSn prefer an indirect condensation pathway and have a high selectivity for the azo(xy) products. The only exception is Pd 3 Pb, the most active among the intermetallic nanoparticles studied here, which is instead selective for aniline. Employing a novel application of molecular dynamics-based on machine learned potentials within a DeePMD framework-to heterogeneous catalysis, we are able to identify key reaction species on the different types of catalysts employed, furthering our understanding of the unique selectivity of these materials. By demonstrating how intermetallic nanoparticles can be as active yet more selective than other more traditional catalysts, this work provides new physical insights and opens new opportunities in the use of these materials in other important chemical transformations and applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2023

Probing Nanocrystal Optical Properties in the Limit of Single Nanocrystals

The Bawendi lab has been at the forefront of the synthesis and characterization of the fundamental photo-physics of quantum confined systems for the past three decades. During this DoE project, our progress has been enabled by both our expertise in single nanocrystal (NC) photoluminescence (PL) spectroscopy as well as our extensive knowledge of the synthetic optimization of various NC semiconductors - all of this forming an efficient feedback loop between characterization and synthesis. Our research in this project has focused on the development and application of novel single emitter optical tools for the characterization of colloidal quantum dots (CQDs) which are semiconducting NCs with size-tunable quantum confined electronic structure. CQDs have had a significant commercial impact as display technologies. They are also commercially available as fluorescent biological markers. CQDs are being investigated as a potential material for photovoltaic or optical sensing applications and as emerging optical quantum materials. We have focused on the fundamental understanding of the optical properties of NCs through the development of novel spectroscopic methods at the single emitter level, including understanding how synthetic and environmental heterogeneities influence NC properties. A major advance in our research has been the development of novel photon resolved correlation spectroscopies that allow us to extract the physics of photo-excited single NCs with unprecedented time and spectral resolution from the photon stream of individual NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

From acetone fractionation to lignin-based phenolic and polyurethane resins

In this paper, acetone fractionation was used to increase the homogeneity of softwood kraft lignin for product development. The acetone soluble and insoluble fractions were analyzed in terms of elemental composition, chemical structure, and thermal properties. The phenolic hydroxyl content of the acetone soluble fraction increased by 24%. The acetone soluble fraction of kraft lignin was used to fully substitute phenol in phenolic resin formulation. The resulting formulated lignin-formaldehyde adhesive showed higher adhesion strength (4.3 MPa) compared to a lab formulated phenol-formaldehyde resin (3.4 MPa) with 100% wood failure after lap shear test. On the other hand, the acetone insoluble lignin fraction, which had 23% higher aliphatic hydroxyl content compared to the starting lignin, was used to entirely replace petroleum-based polyol in polyurethane resin formulation. Then a bio-derived solvent (dihydrolevoglucosenone, Cyrene) was used to dissolve the acetone insoluble lignin fraction to synthesize lignin-based polyurethane resin. Tailoring the structure of kraft lignin through solvent fractionation resulted in two fractions, which were used to replace 100% of two major petrochemicals (phenol and polyol) in the formulations of phenolic and PU resins, that would otherwise be challenging due to the heterogeneity of softwood kraft lignin.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Direct Laser Writing of Multimetal Bifunctional Catalysts for Overall Water Splitting

Water electrolysis is of interest as a sustainable way to produce clean hydrogen and oxygen fuel and help mitigate the rising problems of climate change while meeting global energy demands. High-efficiency, stable, and earth-abundant bifunctional catalysts are needed to enable more effective electrochemical cells for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Here, we investigate the synthesis, composition, performance, and mechanism of multimetal catalysts serving dual functionality in both OER and HER of water electrolysis. Through a laser synthesis method, we synthesized heterogeneous catalysts of nanocrystalline multimetallic alloy pockets embedded within an amorphous oxide matrix. We evaluated the performance and composition of a range of mixed transition-metal oxide materials for both OER and HER, ultimately synthesizing a Cr 0.01 Fe 0.27 Co 0.34 Ni 0.38 O x /C y catalyst that has a stable, high-rate, and competitive overall water splitting performance of 1.76 V at 100 mA cm –2 in an alkaline medium. Using density functional theory to gain insight as the active site and mechanism, we propose that the inclusion of a minor amount of Cr increases the degeneracy of energetic states that lowers the cost of forming the O 2 p–d bond and H 1 s–d bond due to the hybridization of s, p, and d orbitals from Cr. Using a two-electrode water electrolysis cell with a constant potential of 1.636 V to mimic the setup for fuel production, we found the catalyst to be stable at 14–15 mA cm –2 for 40 h. Furthermore, this laser synthesis method allowing for facile and rapid synthesis of complex multimetal systems demonstrates how doping a Fe, Co, and Ni heterogeneous amorphous/nanocrystalline structure with small amounts of Cr is important for bifunctional catalytic behavior, particularly for increasing HER functionality in advancing our understanding for future electrocatalytic design.

08 HYDROGEN↗
DOE OSTI2020

A complete grain-level assessment of the stress-strain evolution and associated deformation response in polycrystalline alloys

Polycrystalline alloys are used pervasively across structural applications contingent upon extensive experimental testing. A statistically representative number of tests are required to expose the variability in the material's performance, as a result of non-uniform microstructures and associated micromechanical fields. In a more direct means of capturing this pertinent information, multi-modal experimental techniques are presented to measure and track the complete micromechanical state, evolving during loading, of each and every grain within the regions of interest. Specifically, a combination of high-energy X-ray diffraction microscopy and digital image correlation coupled with electron backscatter diffraction are conducted on a specimen for each of the alloys, Haynes 282 and Ti7Al. The results of the multi-modal analysis definitively demonstrate that the degree of heterogeneity increases with deformation level and is used to assess the number of grains necessary for a representative volume element description of the stress state for each of these materials. Moreover, higher resolution imaging is used for identification of the slip system activity and subsequently used to study slip transmission events. An accurate knowledge of the resolved shear stress in adjacent grains (grain interactions) is demonstrated to be a key descriptor of the slip transmission events.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Textured Electrodes: Manipulating Built-In Crystallographic Heterogeneity of Metal Electrodes via Severe Plastic Deformation

Control of crystallography of metal electrodeposit films has recently emerged as a key to achieving long operating lifetimes in next-generation batteries. It is reported that the large crystallographic heterogeneity, e.g., broad orientational distribution, that appears characteristic of commercial metal foils, results in rough morphology upon plating/stripping. On this basis, an accumulative roll bonding (ARB) methodology—a severe plastic deformation process—is developed. Zn metal is used as a first example to interrogate the concept. It is demonstrated that the ARB process is highly effective in achieving uniform crystallographic control on macroscopic materials. After the ARB process, the Zn grains exhibit a strong (002) texture (i.e., [002] Zn //ND). The texture transitions from a classical bipolar pattern to a nonclassical unipolar pattern under large nominal strain eliminate the orientational heterogeneity of the foil. The strongly (002)-textured Zn remarkably improves the plating/stripping performance by nearly two orders of magnitude under practical conditions. The performance improvements are readily scaled to achieve pouch-type full batteries that deliver exceptional reversibility. The ARB process can, in principle, be applied to any metal chemistry to achieve similar crystallographic uniformity, provided the appropriate temperature and accumulated strains are employed. Here this concept is evaluated using commercial Li and Na foils, which, unlike Zn (HCP), are BCC crystals. The simple process for creating strong textures in both hexagonal and cubic metals and illustrating the critical role such built-in crystallography plays underscores opportunities for developing highly reversible thin metal anodes (e.g., hexagonal Zn, Mg, and cubic Li, Na, Ca, Al).

36 MATERIALS SCIENCE↗