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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Computational thermodynamics-guided alloy design and phase stability in CoCrFeMnNi-based medium-and high-entropy alloys: An experimental-theoretical study

A computational thermodynamics approach has been employed to design CoCrFeMnNi-based medium- and high-entropy alloys (M/HEAs) with systematically varied compositions (Co(( 80-X )/ 2 )Cr(( 80-X )/ 2 )Fe X Mn 10 Ni 10 with x = 30, 40, and 50 at.%) and phase stability. Since the formation of sigma phase, usually brittle and undesirable, is a common concern, when this class of alloys is subjected to elevated temperatures (600–1000 °C), predicting its formation becomes essential. Thus, its formation and the phase equilibria were studied using the CALPHAD method, and two empirical methods, namely, valence electron concentration (VEC) and paired sigma-forming element (PSFE). Isothermal aging treatments at 900–1100 °C for 20 h were performed, since CALPHAD and VEC/PSFE predictions diverged. Both prediction methods were compared with experimental characterization by a combination of scanning electron microscopy and high-energy synchrotron X-ray diffraction. In conclusion, the predictions from the VEC/PSFE and CALPHAD calculations (depending on the database used) were shown to be quite accurate.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Autonomous alloy composition optimization using molecular dynamics guided by a large language model

Here, we present an autonomous materials discovery framework that couples a large language model (LLM) with molecular dynamics (MD) simulations to optimize Fe–Cr–Mn alloy compositions for tensile strength. Starting from six distinct compositions, the LLM operated as an intelligent agent, iteratively proposing changes based on prior simulation results and constraints. Over 50 iterations per case, the LLM adaptively explored the composition space, identifying high-strength regions, not easily accessible by conventional methods. The highest strength, 18.7 GPa, was achieved with Fe 71 Cr 25 Mn 4 composition, identified from a Fe 75 Cr 20 Mn 5 starting point. The LLM autonomously adjusted its strategy in real time, demonstrating closed-loop decision-making using commodity hardware. This approach showcases the potential of LLMs as scientific co-pilots, capable of accelerating materials discovery and generalizable to other domains like biology and drug design.

Autonomy↗
DOE OSTI2023

DFT-guided flux synthesis of a family of layered titanates crystallizing in the lepidocrocite structure type

K 0.72 Fe 0.72 Ti 1.28 O 4 , a layered mixed-metal oxide belonging to the lepidocrocite structure type, was grown out of a molten KCl-KF mixture at 800 °C. The compound crystallizes in orthorhombic space group Cmcm and exhibits two-dimensional layers, composed of mixed Fe/Ti octahedra, separated by K ions that occupy the interlayer layer space and maintain charge balance. The targeted flux synthesis of the Ni, Cu, and Zn analogs was carried out after density functional theory (DFT) calculations predicted their formation to be energetically favorable, yielding K 0.77 Ni 0.38 Ti 1.62 O 4 , K 0.77 Cu 0.38 Ti 1.62 O 4 , and K 0.73 Zn 0.36 Ti 1.64 O 4 . Finally, all four compositions can be prepared via conventional solid-state technique by mixing and heating stoichiometric amounts of KNO 3 , appropriate transition metal oxide precursors, and TiO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Leveraging computational thermodynamics to guide SiC-ZrC chemical vapor deposition process development

Here using the CALPHAD approach to understand zirconium carbide deposition, a series of phase equilibria were calculated from a custom thermodynamic database based on a literature source, and the equilibria were used to explore the potential chemical vapor deposition (CVD) processing space in the ZrCl 4 -CH 3 SiCl 3 -CH 4 -H 2 system as a function of pressure, temperature, and gas composition. Several gas ratios were considered. At a given ZrCl 4 :CH 3 SiCl 3 ratio within the range studied, the most important factor was found to be the ratios of CH 4 :ZrCl 4 , wherein the nature of the composition – carbide vs. silicide – could be controlled. A pure binary composition of ZrC and SiC is expected to form by increasing the initial amount of methane and decreasing the amount of hydrogen from values predicted purely based on thermodynamic equilibrium. Rietveld analysis of the x-ray diffractograms from corresponding experimental depositions confirmed that increasing the CH 4 :ZrCl 4 ratio increased the fraction of carbon-containing species (SiC, ZrC) and decreased the fraction of non-carbides (ZrSi, ZrSi 2 , etc.), as predicted from the CALPHAD results.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure-guided design of a broadly cross-reactive multivalent group a streptococcal vaccine

The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal region of the M protein is variable in sequence, defines the M/emm type, and contains epitopes that elicit opsonic antibodies that protect animals from challenge infections. Although there are >200 M types of Strep A, there is now evidence that structurally related M proteins can be grouped into clusters and that immunity may be cluster-specific in addition to M type-specific. This observation has led to recent studies of structure-based design of multivalent M peptide vaccines to select peptides predicted to cross-react with heterologous M types to improve vaccine coverage. In the current study, we have applied a refined series of peptide structural algorithms to predict immunological cross-reactivity among 117N-terminal M peptides representing the most prevalent M types of Strep A. Based on the results of the structural analyses, in combination with global M type prevalence data, we constructed a 32-valent vaccine containing 19 cross-reactive vaccine candidates predicted to cross-react with 37 heterologous M peptides to which were added 13 type-specific M peptides. Further, the 4-protein recombinant vaccine was immunogenic in rabbits and elicited significant levels of antibodies against 31/32 (97%) vaccine peptides and 28/37 (76%) peptides predicted to cross-react. The vaccine antisera also promoted opsonophagocytic killing of vaccine and cross-reactive M types of Strep A. Based on a recent analysis of M type prevalence of Strep A, the potential global coverage of the 32-valent vaccine is ~90%, ranging from 68% in Africa to 95% in North America. Our results indicate the utility of structure-based design that may be applied to future studies of broadly protective M peptide vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Structure-guided utilization of lignocellulose for catalysis, energy, and biomaterials

As a complex composite of cellulose, hemicellulose, and lignin, plant lignocellulose has long served as a major resource for biomass conversion, materials engineering, and bio-based product development. High-resolution structural insights enabled by solid-state nuclear magnetic resonance (ssNMR) now allow the mapping of polymer interfaces, identification of functional group accessibility, and tracking of molecular organization during processing, all of which are critical factors for optimizing catalytic strategies. These insights could drive transformative progress in lignocellulose-based applications, including selective depolymerization, improved pretreatment design, and efficient upcycling of lignin into resins, plastics, and biomedical materials. In industry-relevant contexts, such as biofuel generation and renewable material manufacturing, understanding the hydration dynamics, cross-linking patterns, and structural heterogeneity is also essential. The ability to visualize these features in native biomass presents a unique opportunity to develop new strategies for sustainability and performance. As the structural toolbox continues to expand, it is becoming a central enabler for innovations in renewable energy, green chemistry, and advanced bioproducts.

bioproduct↗
DOE OSTI2023

Guiding centre motion for particles in a ponderomotive magnetostatic end plug

The Hamiltonian dynamics of a single particle in a rotating plasma column, interacting with an magnetic multipole is perturbatively solved for up to second order, using the method of Lie transformations. First, the exact Hamiltonian is expressed in terms of canonical action-angle variables, and then an approximate integrable Hamiltonian is introduced, using another set of actions and angles, which describe the centre of oscillation for the particle. The perturbation introduces an effective ponderomotive potential, which to leading order is positive. At the second order, the pseudopotential consists of a sum of terms of the Miller form, and can have either sign. Additionally, at second order, the ponderomotive interaction introduces a modification to the particle effective mass, when considering the motion along the column axis. It is found that particles can be axially confined by the ponderomotive potentials, but acquire radial excursions which scale as the confining potential. The radial excursions of the particle along its trajectory are investigated, and a condition for the minimal rotation frequency for which the particle remains radially confined is derived. Last, we comment on the changes to the aforementioned solution to the pseudopotentials and particle trajectory in the case of resonant motion, that is, a motion which has the same periodicity as the perturbation.

Rubin, T. (ORCID:0000000164850096)↗
DOE OSTI2022

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis

Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

42 ENGINEERING↗