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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Impact of Europium as a Fission Product Surrogate on Chromium Corrosion in Molten Chloride Salt

The rising interest in molten salts, both as thermal energy storage media and as high-temperature solvents for fissile materials in advanced nuclear reactors, has spurred significant growth in research on structural and functional salt-facing materials. Impurities, such as water, play a significant role in the degradation of materials in molten salt environments. Other impurities, such as fission products, are expected to accumulate in molten salts during normal reactor operation and can affect the properties of the salt and salt-facing materials. During normal reactor operation, rare-earth element species are expected to form as byproducts of fission reactions, potentially degrading or altering the performance of both structural and functional materials exposed to salts. Here, in this work, fission product surrogates, europium metal and EuCl 3 , were added to the KCl-MgCl 2 salt while exposing pure Cr specimens in the melt at 600 and 700 °C for 1000 h. Low reactivity was observed with the baseline salt, while both the Eu and EuCl 3 additions to the salt resulted in increased mass loss of Cr specimens. A capillary electrophoresis technique was developed to enable the detection of low parts-per-million (ppm) concentrations of europium (Eu) in chloride salt both before and after exposure of Cr metal. This technique provides a sensitive and reliable method for monitoring trace levels of Eu, which is critical for understanding the interactions and potential contamination effects in salt-facing materials during reactor operation. This quantification technique can be applied to other cationic and anionic contaminants in halide salts.

chloride salt↗
DOE OSTI2026

Point defect energetics in gallium arsenide, a comprehensive density functional theory study

In materials, point defects often control or modify functional properties. To predict the performance of materials intended for application in optoelectronic devices, it is imperative to understand the properties of those point defects. For the first time, all six intrinsic defects of GaAs, a key optoelectronics material, and their charge transition levels are calculated using density functional theory with the HSE06 functional. For comparison, both PBE and r 2 SCAN calculations are also carried out. The HSE06 results are found to be in better agreement with experimental data than previous calculations. In conclusion, the importance of using the exact electron exchange present in hybrid functionals and larger supercells to accurately determine defect levels and ground state defect configurations is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Vacancy-Dependent Diffusion Mechanism in Oxygen-Defective SrFeO 3 Perovskite Materials: First-Principles Density Functional Theory and Experimental Approach

Understanding oxygen diffusion at the atomic scale in SrFeO 3−δ perovskites is crucial for developing oxygen storage materials with optimal performance. Such materials are required to have high stability, corrosion resistance, and acceptable oxygen storage capacity at moderate operating temperatures and pressures. Here, in this study, we used first-principles density functional theory and thermogravimetric analysis to study the vacancy-dependent oxygen diffusion in oxygen-deficient SrFeO 3−δ (δ = 0, 0.065, 0.125, 0.25, 0.5) perovskites. The electronic structures, including the partial- and spin-resolved density of states, for different SrFeO 3−δ phases were calculated and compared with available experimental and theoretical results. By mapping the migration pathways, we investigated diffusion mechanisms and calculated the energy barriers for oxygen diffusion in cubic, orthorhombic, and brownmillerite phases of SrFeO 3−δ perovskites. Using the calculated energy barriers, we deduced the diffusion time scales and diffusion coefficients within SrFeO 3−δ . A diffusion coefficient on the order of 10 –8 m 2 /s was obtained for SrFeO 2.875 . We experimentally investigated the roles of temperature and oxygen partial pressures on the redox kinetics and deduced the kinetics rate and diffusion density, which agreed well with the calculated values for the density of diffusing oxygen vacancy in the lattice. Our results showed that the energy barrier tends to reduce at higher oxygen concentrations. Our results serve as an important guideline for designing oxygen storage materials with optimal redox kinetics.

chemical looping with oxygen uncoupling (CLOU)↗
DOE OSTI2026

Zentropy Theory for Transformative Functionalities of Magnetic and Superconducting Materials

The proposed research developed the zentropy theory through applications to complex magnetic materials and superconductors under the hypothesis that the emergent properties of complex magnetic materials and superconductors can be predicted by statistical mechanics of ergodic microstates with their partition functions computed from DFT-predicted free energies. The key objective is to develop approaches to systematically determine the types and number of microstates and the supercell size in DFT-based calculations through convergency of macroscopic functionalities, with the incorporation of our mixed-space approach accounting for the interactions between periodic supercells. In addition to use scientific intuitions to guide the design of important microstates, the key innovation of the proposed research is to integrate the domain knowledge and the material-property-descriptor database (MPDD) with 4 million microstates, which is supported by our deep neural network machine learning models (SIPFENN: structure-informed prediction of formation energy using neural networks) and integrated with our high throughput DFT Tool Kit (DFTTK). For complex magnetic materials, one of the objectives is to develop approaches to calculate short-range ordering from the statistical distribution of each microstate. For superconductors, the divergency of quasiparticle effective mass at a quantum critical point will be investigated, and the superconducting and non-superconducting microstates will be delineated through analysis of electronic band structure, density of states, charge density, and Fermi surface.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Functionalized Graphene via a One-Pot Reaction Enabling Exact Pore Sizes, Modifiable Pore Functionalization, and Precision Doping

Functionalizing graphene with exact pore size, specific functional groups, and precision doping poses many significant challenges. Current methods lack precision and produce random pore sizes, sites of attachment, and amounts of dopant, leading to compromised structural integrity and affecting graphene’s applications. In this work, we report a strategy for the synthesis of functionalized graphitic materials with modifiable nanometer-sized pores via a Pictet–Spengler polymerization reaction. This one-pot, four-step synthesis uses concepts based on covalent organic frameworks (COFs) synthesis to produce crystalline two-dimensional materials that were confirmed by PXRD, TEM measurements, and DFT studies. These new materials are structurally analogous to doped graphene and graphene oxide (GO) but, unlike GO, maintain their semiconductive properties when fully functionalized.

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

Direct evidence of low work function on SrVO 3 cathode using thermionic electron emission microscopy and high-field ultraviolet photoemission spectroscopy

Perovskite SrVO 3 has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfaces, and recent experimental efforts have consistently demonstrated effective work functions of ~2.7 eV for polycrystalline samples, both results suggesting, but not directly confirming, that some fraction of even lower work function surface is present. In this work, thermionic electron emission microscopy (ThEEM) and high-field ultraviolet photoemission spectroscopy (UPS) are used to study the local work function distribution and measure the work function of a partially oriented- (110)-SrVO 3 perovskite oxide cathode surface. Our results show direct evidence of low work function patches of about 2.0 eV on the cathode surface, with a corresponding onset of observable thermionic emission at 750 °C. We hypothesize that, in our ThEEM and UPS experiments, the high applied electric field suppresses the patch field effect, enabling the direct measurement of local work functions. This measured work function of 2.0 eV is comparable to the previous DFT-calculated work function values of the SrVO-terminated (110) SrVO 3 surface (2.3 eV) and SrO-terminated (100) surface (1.9 eV). The measured 2.0 eV value is also much lower than the work function for the (001) LaB 6 single crystal cathode (~2.7 eV) and comparable to the effective work function of B-type dispenser cathodes (~2.1 eV). If SrVO 3 thermionic emitters can be engineered to access domains of this low 2.0 eV work function, they have the potential to significantly improve thermionic emitter-based technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multi-material direct ink writing and co-sintering of gadolinium oxide – zirconium oxide components

Methods for fabrication of multi-material or functionally graded ceramic composite architectures are of interest for numerous applications. However, conventional co-sintering of multi-material ceramic parts is a challenge because differences in the sintering behavior of the two materials leads to interfacial strain and, ultimately, component failure. Direct ink writing (DIW) is an extrusion-based additive manufacturing process that excels at multi-material printing because multiple extrusion nozzles can be installed on the same gantry system. Furthermore, the use of DIW as a method to fabricate multi-material ceramic green bodies offers an additional variable for controlling and potentially matching sintering kinetics in the slurry formulation used for two dissimilar feedstocks. In the work documented in this manuscript, we explored two strategies to successfully co-sinter multi-material ceramic oxides: slurry optimization to match sintering kinetics and material gradients to step from one material to another. This manuscript also quantifies the allowable mismatch that avoids part cracking in solid solution forming multi-material systems and discusses best strategies to reduce mismatch during co-sintering. Inks composed of gadolinium oxide (Gd 2 O 3 ) and zirconium oxide (ZrO 2 ), a surrogate for uranium oxide (UO 2 ), were thermally matched, which resulted in a sintering mismatch reduction of over 10%. It was found that ~1% mismatch is tolerable during debind cycles and that ~5% mismatch is manageable during sintering cycles after slurry formulations are optimized to match the sintering behavior. Use of continuous gradients is shown to reduce sintering mismatch, although geometric resolution may be lost due to solid solution formation.

Snarr, Patrick L. [Oak Ridge National Laboratory (↗
DOE OSTI2026

Latent space dynamics identification for interface tracking with application to shock-induced pore collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

Gaussian process↗
DOE OSTI2024

Dynamic Implications of Noncovalent Interactions in Amphiphilic Single-Chain Polymer Nanoparticles

Single-chain polymer nanoparticles (SCNPs) combine the chemical diversity of synthetic polymers with the intricate structure of biopolymers, generating versatile biomimetic materials. The mobility of polymer chain segments at length scales similar to secondary structural elements in proteins is critical to SCNP structure and thus function. However, the influence of noncovalent interactions used to form SCNPs (e.g., hydrogen-bonding and biomimetic secondary-like structure) on these conformational dynamics is challenging to quantitatively assess. To isolate the effects of noncovalent interactions on SCNP structure and conformational dynamics, we synthesized a series of amphiphilic copolymers containing dimethylacrylamide and monomers capable of forming these different interactions: (1) di(phenylalanine) acrylamide that forms intramolecular β-sheet-like cross-links, (2) phenylalanine acrylamide that forms hydrogen-bonds but lacks a defined local structure, and (3) benzyl acrylamide that has the lowest propensity for hydrogen-bonding. Each SCNP formed folded structures comparable to those of intrinsically disordered proteins, as observed by size exclusion chromatography and small angle neutron scattering. The dynamics of these polymers, as characterized by a combination of dynamic light scattering and neutron spin echo spectroscopy, was well described using the Zimm with internal friction (ZIF) model, highlighting the role of each noncovalent interaction to additively restrict the internal relaxations of SCNPs. These results demonstrate the utility of local scale interactions to control SCNP polymer dynamics, guiding the design of functional biomimetic materials with refined binding sites and tunable kinetics.

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

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2025

Catalytic Difunctionalization of Cyclic Dienes: Direct Entry to Novel ROMP Monomers

We developed a catalytic platform to convert simple hydrocarbon feedstocks into valuable, tunable materials by leveraging nickel-catalyzed difunctionalization of cyclic dienes to access a novel class of cyclic alkene monomers. These monomers undergo ring-opening metathesis polymerization (ROMP) to yield sequence-controlled polymers with defined stereochemistry. Through mechanistic studies and catalyst optimization, we established a scalable, gram-level synthesis for selective diarylation, and expanded the reaction scope to include arylalkylation through rationally tuning the organoboron coupling partner. The resulting polymers were systematically studied to understand how steric, electronic, and stereochemical features influence polymerization behavior and bulk material properties. Functionalized derivatives bearing sulfonated groups were explored as proton-exchange membranes, and chemical recycling pathways were developed to recover monomers from the final materials. This work bridges small-molecule catalysis and macromolecular design, enabling access to tunable, recyclable polymers from abundant hydrocarbon starting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantum effects on the dynamics and properties of soft materials

The quantum effects of nuclear and electronic motion play an important role in the structure, dynamics, and function of soft materials, yet they are difficult to capture with conventional classical simulations or static electronic–structure methods. In this work several complementary approaches for treating quantum effects in polymeric and soft–matter systems are demonstrated, with a focus being on the hydrogen-bonded networks, ion and charge transport, and photoactive chromophores. The proton transfer, tunneling, and isotope effects are captured within the reduced-dimensionality models by implementing grid-based nuclear quantum dynamics in terms of the discrete variable and Fourier bases. The nuclear quantum dynamics is extended to larger systems by employing the quantum trajectories and quantum–thermal bath schemes combined with on-the-fly electronic structure, enabling the description of high-dimensional polymeric environments at feasible cost. The dynamics in the electronic degrees of freedom, simulating the optical response in large chromophores such as chlorophylls, is performed using the real-time time-dependent density functional theory implemented in the real-space multigrid (RMG) code. These approaches are demonstrated on case studies of the proton and hydroxide transport in hydrated polymer membranes, charge transfer in conjugated polymers, and the optical spectra of chlorophyll chromophores relevant to polymerized chlorophyll materials and chlorophyll–polymer hybrids. The reviewed methods and applications highlight practical routes of including quantum effects in simulations of soft functional materials.

Garashchuk, Sophya [Univ. of South Carolina, Colum↗
DOE OSTI2024

High performance Calcium Copper Titanate/Polyimide dielectrics composites enabled through colloidal stabilization

Transition to electrified transportation demands advanced power electronic components with the capability to improve range, reliability, and cost of ownership to accelerate mass market adoption. Thus, improvement in the current designs, manufacturing processes, materials, and components capable of providing reliable and efficient operation at higher temperatures play important roles in meeting future needs. To address these challenges, this article focuses on novel dielectric materials designed to reduce the volume of the most important and bulky component of a power electronic system, a capacitor. A new composite dielectric material was developed by integrating the positive attributes of both polymer and ceramic capacitors to overcome the challenges of state-of-the-art dielectric materials. Further, the developed composite properties have been evaluated and showed promising results, achieving a dielectric constant of 250 at 100 Hz, 25°C, unseen in current literature. Additionally, these materials can function at high temperatures (>150°C) with good breakdown strength, providing promising working conditions for capacitors, especially in electric vehicle applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Fabrication of ceramic to metal graded structure for high temperature CSP receiver application using solid state additive manufacturing

The overarching goal of this project is to come up with an innovative joining solution for integration of SiC ceramic to structural materials such as stainless steels (SS)/Ni-base alloys, where intimate metallurgical bonding between SiC ceramic and the structural metallic alloy is demonstrated at the interface. Implementation of a simple, yet robust joining method would help in increasing the application space of SiC in a high-temperature CSP (concentrated solar power) plant, especially in the receiver section. This research goal has been achieved by creating a functionally graded material (FGM) interface between SiC and the selected structural material with the help of powder metallurgy (PM) approach and demonstrate intimate metallurgical bonding between monolithic RB-SiC and 316L stainless steel alloy blanks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Random heteropolymers as enzyme mimics

Despite successes in replicating the primary–secondary–tertiary structure hierarchy of protein, it remains elusive to synthetically materialize protein functions that are deeply rooted in their chemical, structural and dynamic heterogeneities. We propose that for polymers with backbone chemistries different from that of proteins, programming spatial and temporal projections of sidechains at the segmental level can be effective in replicating protein behaviours; and leveraging the rotational freedom of polymer can mitigate deficiencies in monomeric sequence specificity and achieve behaviour uniformity at the ensemble level. Here, guided by the active site analysis of about 1,300 metalloproteins, we design random heteropolymers (RHPs) as enzyme mimics based on one-pot synthesis. We introduce key monomers as the equivalents of the functional residues of protein and statistically modulate the chemical characteristics of key monomer-containing segments, such as segmental hydrophobicity. The resultant RHPs form pseudo-active sites that provide key monomers with protein-like microenvironments, co-localize substrates with catalytic or cofactor-binding sidechains and catalyse reactions such as oxidation and cyclization of citronellal with isopulegol/menthoglycol selectivity. This RHP design led to enzyme-like materials that can retain catalytic activity under non-biological conditions, are compatible with scalable processing and have expanded substrate scope, including environmentally long-lasting antibiotic tetracycline.

36 MATERIALS SCIENCE↗