Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “multicomponent materials”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Short- to Intermediate-Range Structure, Transport, and Thermophysical Properties of LiF–NaF–ZrF4 Molten Salts

LiF–NaF–ZrF 4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF 4 , 42LiF–29NaF–29ZrF 4 , and 26LiF–37NaF–37ZrF 4 . Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF 4 salt mixtures. It is found that as ZrF 4 mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li + , Na + ) obtained from RIM calculations decreases, while multivalent Zr 4+ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF 4 mol% salts such as in 38LiF–51NaF–11ZrF 4 , an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF 4 mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF 4 salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF 4 mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Fluoroarene Separations in Metal-Organic Frameworks with Two Proximal Mg 2+ Coordination Sites

Fluoroarenes which are widely used in medicinal, agricultural, and materials chemistry, and yet their production remains a critical challenge in organic synthesis. Indeed, the nearly identical physical properties of these vital building blocks hinders their purification by traditional methods, such as flash chromatography or distillation. As a result, the Balz-Schiemann reaction is currently employed to prepare fluoroarenes instead of more atom-economical C-H fluorination reactions, which produce inseparable mixtures of regioisomers. Herein, we propose an alternative solution to this problem: the purification of mixtures of fluoroarenes using metal-organic frameworks (MOFs). Specifically, we demonstrate that controlling the interaction of fluoroarenes with adjacent coordinatively unsaturated Mg 2+ centers within a MOF enables the separation of fluoroarene mixtures with unparalleled selectivities. Liquid-phase multicomponent equilibrium adsorption data and breakthrough measurements coupled with van der Waals-corrected density functional theory calculations reveal that the materials Mg 2 (dobdc) (dobdc 4- = 2,5-dioxidobenzene-1,4-dicarboxylate) and Mg 2 ( m -dobdc) ( m -dobdc 4- = 2,4-dioxidobenzene-1,5-dicarboxylate) are capable of separating the difluorobenzene isomers from one another. Additionally, these frameworks facilitate the separations of fluoroanisoles, fluorotoluenes, and fluorochlorobenzenes. In addition to enabling currently unfeasible separations for the production of fluoroarenes, our results suggest that carefully controlling the interaction of isomers with not one but two strong binding sites within a MOF provides a general strategy for achieving challenging liquid-phase separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine learning-based multidomain processing for texture-based image segmentation and analysis

Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Finally, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hydration and reaction mechanisms on sodium silicate glass surfaces from molecular dynamics simulations with reactive force fields

Recent development of reactive force fields have enabled molecular dynamics simulations of interactions between silicate glasses and water at the atomistic scale. While multicomponent silicate glasses encompass a wide variety of compositions and properties, one common structural feature in these glasses is the combination of the network structure that is made up of silica tetrahedra linked through corner sharing interspersed with network modifiers like alkali and alkaline-earth ions that break up the Si–O–Si linkages by forming nonbridging oxygen. In reactions with water, ion exchange between alkali ions in the glass and proton or hydronium in the solution, as well as hydrolysis reaction of the Si–O–Si linkages and subsequent silanol formation, is observed and well documented. Furthermore, we have used a set of recently developed reactive force field to investigate the reactions between water and the surfaces of silica and sodium silicate glasses of different compositions for reactions up to 8 nanoseconds. Our results indicate sodium leaching into water and diffusion of water molecules up to 25 Å into the glass surface. We examined the structural and compositional changes inside the glass and around the diffused ions and use these to explain the rates of silanol formation at the surface. We also observed proton transport in the glass which has an indirect influence on the silanol formation rates. While the surface of the glass was rough to start with, it undergoes further modification into a hydrated gel-like structure in the glass for up to 5 Å in the higher alkali containing glasses. It was found that the leached sodium ions remain close to the interface and that fragments of silicate network from the surface is capable of dislodging from the bulk glass and enter the aqueous solution. These simulations thus provide insights into the formation and structure of an alteration layers commonly observed in multicomponent silicate glasses corroded in aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A simple model for short-range ordering kinetics in multi-principal element alloys

Short-range ordering (SRO) in multi-principal element alloys influences material properties such as strength and corrosion. While some degree of SRO is expected at equilibrium, predicting the kinetics of its formation is challenging. We present a simplified isothermal concentration-wave (CW) model to estimate an effective relaxation time of SRO formation. Estimates from the CW model agree to within a factor of five with relaxation times obtained from kinetic Monte Carlo (kMC) simulations when above the highest ordering instability temperature. Further, the advantage of the CW model is that it only requires mobility and thermodynamic parameters, which are readily obtained from alloy mobility databases and Metropolis Monte Carlo simulations, respectively. The simple parameterization of the CW model and its analytical nature makes it an attractive tool for the design of processing conditions to promote or suppress SRO in multicomponent alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effects of Aluminum and Molybdenum on the Phase Stability of Iron-Chromium Alloys: A First-Principles Study

The interaction between solute atoms is critical to the thermodynamic behavior of Fe-Cr alloys, but the effects of non-dilute Al and Mo on the Fe-Cr phase stability and vacancy formation energy are not clearly understood. In this study, density functional theory, cluster expansion, and Monte Carlo simulation are used to predict the effects of ternary solute elements on the thermodynamic properties in multicomponent Fe-Cr alloys. The machine learning regression approach is applied to train and construct energy models that accurately describe the complex chemical interactions. The computational outcomes include the prediction of the partial ternary phase diagram, mixing enthalpy, and vacancy formation energy for different compositions. The phase boundary calculation predicts a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from a’ precipitates. The mixing enthalpy calculation shows that Mo may also reduce the Cr solubility in bcc Fe. Additionally, the simulations show that non-dilute Cr decreases the vacancy formation energy in bcc Fe, while adding Al results in a less significant effect. Finally, the results demonstrate important applications of using machine learning energy models to study model or commercial alloys with multicomponent solute species and point defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CO 2 hydrogenation over rhodium cluster catalyst nucleated within a manganese oxide framework

Rhodium-based manganese oxide frameworks were explored as a prototype for carbon dioxide reactive capture and conversion. Three-dimensional frameworks of MnOx were utilized as support structures to isolate Rh metal centers. V, Na, and Zn were introduced as counterions to stabilize the structure and for their beneficial effect as promoters. Here, with this multicomponent catalyst, Rh active centers with MnOxs and varied counterions, we were able to selectively tune the catalytic performance of the material via the choice of counterion and structure of the host material. With cryptomelane-type tunnel manganese oxides octahedral molecular sieve (OMS2), we found that Rh-V-OMS2 was highly stable even after 48 hours on stream with a reaction rate of around 1.5x10 -4 mol CO 2 /g Rh /s, surpassing the net reactivity of other initially more active combinations. Furthermore, during CO 2 hydrogenation, in situ XAFS showed that single Rh atoms nucleated into nanoparticles/ sub-nanometer clusters with a coordination number of 5.5 or less. Our finding of the correlation between the reaction rate and particle size offers the potential for enhanced control over the reaction rate by tuning particle size. Our activity study with control experiments demonstrates that the activities of the catalysts are proved due to the unique metal support interaction offered by the Rh-X-MnO.

10 SYNTHETIC FUELS↗
DOE OSTI2022

An ℓ 0 ℓ 2 -norm regularized regression model for construction of robust cluster expansions in multicomponent systems

In this work we introduce ℓ 0 ℓ 2 -norm regularization and hierarchy constraints into linear regression for the construction of cluster expansions to describe configurational disorder in materials. The approach is implemented through mixed integer quadratic programming (MIQP). The ℓ 2 -norm regularization is used to suppress intrinsic data noise, while the ℓ 0 -norm is used to penalize the number of nonzero elements in the solution. The hierarchy relation between clusters imposes relevant physics and is naturally included by the MIQP paradigm. As such, sparseness and cluster hierarchy can be well optimized to obtain a robust, converged set of effective cluster interactions with improved physical meaning. We demonstrate the effectiveness of ℓ 0 ℓ 2 -norm regularization in two high-component disordered rocksalt cathode material systems, where we compare the cross-validation, convergence speed, and the reproduction of phase diagrams, voltage profiles, and Li-occupancy energies with those of the conventional ℓ 1 -norm regularized cluster expansion models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

VTAnDeM: A python toolkit for simultaneously visualizing phase stability, defect energetics, and carrier concentrations of materials

Phase stability, defect formation energies, and carrier concentrations are closely interrelated features of semiconductors. Due to their joint dependence on the multidimensional chemical potential space, it is challenging to quantitatively establish patterns between these quantities in a given semiconductor, especially when the semiconductor is comprised of multiple elements. To enable synchronous visualization and analysis of these complementary material properties and their interdependence, we developed the Visualization Toolkit for Analyzing Defects in Materials (VTAnDeM). This python-based toolkit allows users to interactively explore how defect formation energies and carrier concentrations vary across the composition and chemical potential spaces of multicomponent semiconductors. Here, we illustrate the computational workflow that employs VTAnDeM as a post-processing tool for first-principles calculations and describe the data organization and theory underlying the visualization scheme. Furthermore, we believe that this software will serve as a useful tool for simultaneously visualizing the often complex and non-intuitive chemical potential – defect – carrier concentration phase space of semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

General Strategy for Incorporation of Functional Group Handles into Covalent Organic Frameworks via the Ugi Reaction

The library of imine-linked covalent organic frameworks (COFs) has grown significantly over the last two decades, featuring a variety of morphologies, pore sizes, and applications. An array of synthetic methods has been developed to expand the scope of the COF functionalities; however, most of these methods were designed to introduce functional scaffolds tailored to a specific application. Having a general approach to diversify COFs via late-stage incorporation of functional group handles would greatly facilitate the transformation of these materials into platforms for a variety of useful applications. In this paper, we report a general strategy to introduce functional group handles in COFs via the Ugi multicomponent reaction. To demonstrate the versatility of this approach, we have synthesized two COFs with hexagonal and kagome morphologies. We then introduced azide, alkyne, and vinyl functional groups, which could be readily utilized for a variety of post-synthetic modifications. This facile approach enables the functionalization of any COFs containing imine linkages.

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

Predicting short-range order evolution in WTaCrVHf refractory high-entropy alloys

Short-range order (SRO) in multicomponent concentrated alloys affects their mechanical response. Hence, is paramount to understand how composition modifies the chemical ordering in the system to design materials with optimal properties. Here, in this work, we present a methodology to predict the SRO and thermodynamic properties in chemically complex systems and apply it to the WTaCrVHf quinary alloy. We observe that the addition of Hf significantly modifies the SRO, mainly at intermediate to low temperatures, matching experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis

This report is the final documentation for the one-year LDRD project 226360: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. As Sandia has successfully developed in-house X-ray diffraction tools for study of atomic structure in experiments, it has become increasingly important to develop computational analysis methods to support these experiments. When dynamically compressed lattices and orientations are not known a priori, the identification requires a cumbersome and sometimes intractable search of possible final states. These final states can include phase transition, deformation and mixed/evolving states. Our work consists of three parts: (1) development of an XRD simulation tool and use of traditional data science methods to match XRD patterns to experiments; (2) development of ML-based models capable of decomposing and identifying the lattice and orientation components of multicomponent experimental diffraction patterns; and (3) conducting experiments which showcase these new analysis tools in the study of phase transition mechanisms. Our target material has been cadmium sulfide, which exhibits complex orientation-dependent phase transformation mechanisms. In our current one-year LDRD, we have begun the analysis of high-quality c-axis CdS diffraction data from DCS and Thor experiments, which had until recently eluded orientation identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Elastocaloric evidence for a multicomponent superconductor stabilized within the nematic state in Ba(Fe 1− x Co x ) 2 As 2

The iron-based high-$T_c$ superconductors (SCs) exhibit rich phase diagrams with intertwined phases, including magnetism, nematicity, and superconductivity. The superconducting $T_c$ in many of these materials is maximized in the regime of strong nematic fluctuations, making the role of nematicity in influencing the superconductivity a topic of intense research. Here, in this study, we use the AC elastocaloric effect (ECE) to map out the phase diagram of Ba(Fe 1− x Co x ) 2 As 2 near optimal doping. The ECE signature at $T_c$ on the overdoped side, where superconductivity condenses without any nematic order, is quantitatively consistent with other thermodynamic probes that indicate a single-component superconducting state. In contrast, on the slightly underdoped side, where superconductivity condenses within the nematic phase, ECE reveals a second thermodynamic transition proximate to and below $T_c$. We rule out magnetism and reentrant tetragonality as the origin of this transition and find that our observations strongly suggest a phase transition into a multicomponent superconducting state. This implies the existence of a subdominant pairing instability that competes strongly with the dominant $s^±$ instability. Our results highlight the significant role of nematic order in determining the pairing symmetry close to optimal doping in this extensively studied iron-based SC, while also demonstrating the power of ECE in uncovering strain-tuned phase diagrams of quantum materials.

Ghosh, Sayak [Stanford University, CA (United Stat↗
DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗