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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Analytically differentiable metrics for phase stability

Here, in this work, a long-established but sparsely documented method of obtaining semi-analytic derivatives of thermodynamic properties with respect to equilibrium conditions is briefly reviewed and rigorously derived. This procedure is then leveraged to construct general forms of derivatives of the residual driving force, a metric for measuring phase stability used in CALPHAD model optimization, with respect to overall system and individual phase compositions. Applied examples – calculating heat capacity in the Al-Fe system, thermodynamic factors in the Nb-V-W system, and residual driving force derivatives in the Ni-Ti system – demonstrate the versatility, accuracy, and extensibility of this method. Using the developed method, residual driving force gradients can be applied directly in CALPHAD model optimizers, as well as in materials design frameworks, to identify regions of phase stability with an efficient, gradient-based approach.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analysis of Deformation Mechanisms, Strain Localization, and Fracture in Highly Irradiated Austenitic Steels – Light-Water Reactor Core Materials – via Advanced inSEM Techniques

The present report describes the establishing of an advanced experimental approach – high-resolution digital image correlation or µDIC – for testing miniature irradiated specimens in the ORNL’s Low Activation Materials Development and Analysis (LAMDA) facility. As expected, the µDIC approach will allow for studying strain localization inside grains, for instance, during dislocation channel-grain boundary interaction processes. This information is of high importance for understanding degradation processes in irradiated austenitic steels – in-core materials of Light Water Reactors. The Level 4 report provides a brief introduction, lists materials designated for µDIC testing and delivered in LAMDA this FY, discusses methodical concerns and preliminary observations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Electromagnetic Radiation Effects on MgO-Based Magnetic Tunnel Junctions: A Review

Magnetic tunnel junctions (MTJs) have been widely utilized in sensitive sensors, magnetic memory, and logic gates due to their tunneling magnetoresistance. Moreover, these MTJ devices have promising potential for renewable energy generation and storage. Compared with Si-based devices, MTJs are more tolerant to electromagnetic radiation. In this review, we summarize the functionalities of MgO-based MTJ devices under different electromagnetic irradiation environments, with a focus on gamma-ray radiation. We explore the effects of these radiation exposures on the MgO tunnel barriers, magnetic layers, and interfaces to understand the origin of their tolerance. This review enhances our knowledge of the radiation tolerance of MgO-based MTJs, improves the design of these MgO-based MTJ devices with better tolerances, and provides information to minimize the risks of irradiation under various irradiation environments. This review starts with an introduction to MTJs and irradiation backgrounds, followed by the fundamental properties of MTJ materials, such as the MgO barrier and magnetic layers. Then, we review and discuss the MTJ materials and devices’ radiation tolerances under different irradiation environments, including high-energy cosmic radiation, gamma-ray radiation, and lower-energy electromagnetic radiation (X-ray, UV–vis, infrared, microwave, and radiofrequency electromagnetic radiation). In conclusion, we summarize the radiation effects based on the published literature, which might benefit material design and protection.

42 ENGINEERING↗
DOE OSTI2020

Comparison of Generation of Higher-Order Neutron Scattering Cross Sections

The generation of high-order neutron scattering cross sections consistent with high-fidelity simulations remains an area of active research. Popular options include generating cross sections from continuous energy Monte Carlo calculations or generating cross sections from a deterministic neutron transport calculation with high-fidelity tabulated cross sections. Both options present challenges. Monte Carlo simulations can naturally process continuous energy cross section data and allow for the general description of anisotropic neutron scattering given a scattering law. However, Monte Carlo simulations are inherently related to particle weighting and it has been suggested that this may be unacceptable for generating high-order neutron scattering cross sections. Deterministic neutron transport calculations can easily calculate high-order moments of the flux to appropriately calculate higher-order neutron scattering cross sections but are generally limited by discretization of space, energy, and angle. In this work, the trade-offs between generation of high-order neutron scattering cross sections via Monte Carlo and deterministic neutron transport methods are investigated. The methods implemented in the Monte Carlo computer program Serpent and the deterministic fast reactor neutron cross section generator MC2-3 are compared. Cross sections resulting from these methods are used in Rattlesnake, a deterministic neutron transport code developed by Idaho National Laboratory (INL), and results are compared to a reference continuous energy Monte Carlo calculation. Whereas previous work investigating the effects of anisotropic neutron scattering has focused on light water reactor (LWR) simulations, this work focuses on high-order neutron scattering cross sections as they relate to fast reactor simulations. To investigate the consequences of the Serpent and MC2-3 methodologies, a test problem is developed. The test problem is a one-dimensional geometry with fast reactor materials designed to demonstrate deep penetration and exacerbate the effects of high-order neutron scattering. Based on the results of the deep penetration test problem, it is concluded that P3 neutron scattering cross sections are sufficient to describe anisotropic scattering in fast reactor materials. Any neutron scattering of order higher than P3 offers negligible change in the eigenvalue of the test problem. Additionally, it is determined that the methodology as implemented in Serpent is applicable for generating high-order neutron scattering cross sections through at least P3 in fast reactor materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Final Technical Report

The capture of CO2 and its simultaneously conversion to useful chemical fuels driven by solar energy represents one of the best solutions to resolve our growing energy and environmental concerns. The most critical challenge to this endeavor is the rational design of a photocatalytic architecture that can effectively couple a given photosensitizer (PS) with an appropriate catalyst, thereby enabling efficient photosensitization of a multi-electron reduction catalysis. This research program aims to address this challenge using an interdisciplinary approach that combines innovative material design and synthesis, fundamental mechanistic studies, and photocatalytic performance evaluation. The strategies include 1) constructing and investigating a novel class of 2D COF hybrid photocatalysts with an effective photoactive organic building block as PS and a precisely incorporated CO2 reduction molecular catalyst (MC); and 2) mechanistic origins of CO2 photoreduction using a set of complementary time-resolved and in situ spectroscopic techniques. The novelty of the proposed hybrid system lies in the unprecedented combination of the unique advantage of porous crystalline COF PS with the precise catalytic function of MC for photocatalytic CO2 reduction. In the periods of the support (09/01/2019-12/31/2022), we have made research progress in four projects: 1) Exploring 2D COFs with incorporated Mn complex for light driven CO2 reduction; 2) The dependence of excited state and charge transfer dynamics on monomer structure of 2D COFs; and 3) Control over Charge Separation by Imine Structural Isomerization in Covalent Organic Frameworks with Implications on CO2 Photoreduction; and 4) The impact of monomer structure on the photoluminescence properties of COFs. We found that both monomer structure and linker chemistry can effectively impact the excited state dynamics, charge transfer properties, and photoluminescence quantum yields, the important properties that dictate their applications in photocatalysis. In addition, we found that the direction of imine linker determines charge transfer direction and thus controls the types of catalytic reactions (e.g. water oxidation or CO2 reduction reactions). The result from these fundamental studies provides important information for correlating the structure of the COF photocatalysts with their photophysical properties and catalytic functions, paving the way for their novel application in photocatalysis. We expect that our findings will contribute to addressing current shortcomings of semiconductor- and molecular-based photocatalytic systems that suffer from inefficient light harvesting and charge separation and poor adsorption and activation of reactants. In turn, this research will contribute towards the development of novel photocatalytic systems for CO2 reduction to generate renewable chemical fuels and simultaneously address the problem of mitigating climate change due to CO2 accumulation. In addition, the experimental approaches employed in this research can be easily transferred to other energy technologies and are expected to broadly impact fields involving photocatalysis, optoelectronic devices, and solar energy conversion. The proposed research has also been integrated with educational activities and serve as a basis to raise awareness around the critical issues of global energy production and consumption, and to develop the next generation of solar energy scientists.

14 SOLAR ENERGY↗
DOE OSTI2025

Low-Cost Sulfur Thermal Storage for Solar Industrial Process Heat Applications

Industrial process heat (IPH) is one of the largest energy demands in U.S., representing about 10% of all domestic energy consumption. Fuel costs to generate this industrial process heat are generally a top three cost for industry, a major component in American manufacturing competitiveness. Roughly 60% of US IPH demand (about 6,500 TBtu annually) falls in the medium-temperature range of 100–250 °C. While concentrated solar thermal (CST) technologies can provide a cost-effective source of heat in this temperature range, solar intermittency limits their adoption in industries that operate 24/7. Element 16 Technologies, Inc. developed a low-cost sulfur thermal energy storage (TES) technology to bridge this gap by capturing excess solar heat during the day and dispatching it reliably during non-solar hours. The core innovation is the use of sulfur, an abundant, industrial waste byproduct that costs ten times less than molten salt used in commercial TES systems. The overall goal of the project was to advance the design and development of molten sulfur TES to a manufacturing-relevant prototype stage for solar industrial process heat applications, while establishing and validating a realistic pathway to commercial success. Key tasks included corrosion and mechanical durability testing to identify cost-effective materials, design investigations using physics-based simulation tools, techno-economic evaluations of system lifetime costs, and pilot-scale testing for performance verification. Corrosion testing of steel alloys under cyclic molten sulfur conditions showed that austenitic stainless steels in the 300 series performed particularly well, with no structural degradation of welds or joints. Thermal cyclic testing of pilot sulfur TES units up to 1.5 MWh quantified charge/discharge rates, heat losses, round-trip efficiency and validated the system's capability to operate effectively under intermittent charging conditions. A techno-economic model, informed by sulfur TES performance model validated using pilot test data, showed that hybrid solar+sulfur TES+NG boiler systems are economically competitive with incumbent natural gas boilers for multiple locations in the southwest US. In summary, this project established molten sulfur TES as a technically viable pathway to improve economic competitiveness of American manufacturing by lowering the cost of solar industrial process heat.

14 SOLAR ENERGY↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗
DOE OSTI2021

Full spectrum optical constant interface to the Materials Project

Optical constants characterize the interaction of materials with light and are important properties in material design. Here we present a Python-based Corvus workflow for simulations of full spectrum optical constants from the visible and ultraviolet to hard x-ray wavelengths based on the real-space Green’s function code FEFF10 and structural data from the Materials Project (MP). The Corvus workflow manager and its associated tools provide an interface to FEFF10 and the MP database. The workflow parallelizes the FEFF computations of optical constants over all absorption edges for each material in the MP database specified by a unique MP-ID. The workflow tools determine the distribution of computational resources needed for that case. Similarly, the optical constants for selected sets of materials can be computed in a single-shot. Additionally, to illustrate the approach, we present results for several elemental solids in the periodic table, as well as a sample compound, and compare our predictions with experimental results. In addition, we provide a database of calculated results for all elements for which there is a stable elemental solid at standard conditions available in the Materials Project database. As in x-ray absorption spectra, these results are interpreted in terms of an atomic-like background and fine-structure contributions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dislocation dynamics in heterogeneous nanostructured materials

Crystalline materials can be strengthened by introducing dissimilar phases that impede dislocation glide. At the same time, the changes in microstructure and chemistry usually make the materials less ductile. One way to circumvent the strength–ductility dilemma is to take advantage of heterogeneous nanophases which simultaneously serve as dislocation barriers and sources. Owing to their superior mechanical properties, heterogeneous nanostructured materials (HNMs) have attracted a lot of attention worldwide. Nevertheless, it has been difficult to characterize dislocation dynamics in HNMs using classical continuum models, mainly due to the challenges in describing the elastic and plastic heterogeneity among the phases. Here, in this work, we advance a phase-field dislocation dynamics (PFDD) model to treat multi-phase materials, consisting of phases differing in composition, structural order, and size in the same system. We then apply the advanced PFDD model to exploring two important but divergent materials design problems in HNMs: dislocation/obstacle interactions and dislocation/interface interactions. Results show that the interactions between a dislocation and distribution of obstacles varying in structure and composition cannot be understood by simply interpolating from their individual interactions with a dislocation. It is also found that materials containing interfaces with nanoscale thicknesses and compositional gradients have a much higher dislocation bypass stress than those with sharp interfaces, providing an explanation for the simultaneous high strength and toughness of thick interface-containing nanolaminates as observed in recent experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Elucidating the Onset of Plasticity in Sliding Contacts Using Differential Computational Orientation Tomography

Abstract Depending on the mechanical and thermal energy introduced to a dry sliding interface, the near-surface regions of the mated bodies may undergo plastic deformation. In this work, we use large-scale molecular dynamics simulations to generate “differential computational orientation tomographs” (dCOT) and thus highlight changes to the microstructure near tribological FCC alloy surfaces, allowing us to detect subtle differences in lattice orientation and small distances in grain boundary migration. The analysis approach compares computationally generated orientation tomographs with their undeformed counterparts via a simple image analysis filter. We use our visualization method to discuss the acting microstructural mechanisms in a load- and time-resolved fashion, focusing on sliding conditions that lead to twinning, partial lattice rotation, and grain boundary-dominated processes. Extracting and laterally averaging the color saturation value of the generated tomographs allows us to produce quantitative time- and depth-resolved maps that give a good overview of the progress and severity of near-surface deformation. Corresponding maps of the lateral standard deviation in the color saturation show evidence of homogenization processes occurring in the tribologically loaded microstructure, frequently leading to the formation of a well-defined separation between deformed and undeformed regions. When integrated into a computational materials engineering framework, our approach could help optimize material design for tribological and other deformation problems. Graphic Abstract .

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fluoropolymer Composites from Partially Perfluoroalkylated Waste Polyethylene

Chemically modified plastics have emerged as practical solutions to plastic waste increases. Here, the inherent novelty of decorating polymer chains with chemical functionality results in distinct properties that expand the available application space. Nevertheless, developing designer materials for specific applications beyond compatibilization or mild property enhancement is difficult due to the synergistic effects of both the polar functionality imparted and the parent materials' intrinsic properties. By incorporating perfluoro-alkyl side-chains onto the backbone of dehydrogenated waste HDPE, unique surface properties intermediate between polytetrafluoroethylene (PTFE, the model fluoropolymer) and HDPE become apparent, while the overall material mechanical and thermal properties result in more LLDPE-like materials. This is demonstrated through moderate decreases in the surface free energy of the perfluoroalkylated polyolefin surface (increase in H 2 O contact angle of ~ 6°) and increased ordering under shear when blended with PTFE nanoparticles where the crossover point occurred at higher strains. Critically, perfluoroalkylated HDPE possesses improved rheological modification properties at elevated temperatures with PTFE nanoparticles, resulting in more thermally robust and stable composite materials.

fluoropolymer↗
DOE OSTI2024

Influence of Water Sorption on Ionic Conductivity in Polyether Electrolytes at Low Hydration

Ion-containing polymers are subject to a wide range of hydration conditions across electrochemical and water treatment applications. Significant work on dry polymer electrolytes for batteries and highly swollen membranes for water purification has informed our understanding of ion transport under extreme conditions. However, knowledge of intermediate conditions (i.e., low hydration) is essential to emerging applications (e.g., electrolyzers, fuel cells, and lithium extraction). Ion transport under low levels of hydration is distinct from the extreme conditions typically investigated, and the relevant physics cannot be extrapolated from existing knowledge, stifling materials design. In this study, we conducted ion transport measurements in LiTFSI-doped polyethers that were systematically hydrated from dry conditions. A semiautomated apparatus that performs parallel measurements of water uptake and ionic conductivity in thin-film polymers under controlled humidity was developed. For the materials and swelling range considered in this study (i.e., <0.07 g water/g dry polymer electrolyte), ionic conductivity depends nonlinearly on water uptake, with the initial sorbed water weakly affecting conductivity. With additional increases in swelling, more significant increases in conductivity were observed. Remarkably, changes in conductivity induced by water sorption were correlated with the number of water molecules per lithium ion, with the normalized molar conductivity of different samples effectively collapsing onto one another until this unit of hydration exceeded the solvation number of lithium ions under aqueous conditions. Furthermore, these results provide important knowledge regarding the effects of trace water contamination on conductivity measurements in polymer electrolytes and demonstrate that the lithium-ion solvation number marks a key transition point regarding the influence of water on ion transport in ion-containing polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tuning charge carrier dynamics through spacer cation functionalization in layered halide perovskites: an ab initio quantum dynamics study

Dion–Jacobson (DJ) phase two-dimensional (2D) hybrid halide perovskites are promising for environmentally stable optoelectronic device applications due to their attractive photophysical properties, including long charge carrier lifetime and partially suppressed non-radiative losses. However, the atomistic details of the structure–property relationship are severely limited, which substantially restricts the strategic materials designed for these layered halide perovskites (LHPs). Here, we combine nonadiabatic molecular dynamics and time-domain density functional theory to understand the effects of spacer cation functionalization on the ground and excited-state charge carrier dynamics in DJ phase LHPs. Our in-depth study reveals that the fluorination of spacer cations considerably restricts the thermal motions of LHPs at ambient conditions. The compact structure weakens the overall electron–phonon interactions and reduces the thermal fluctuations of the band edges in real-time. These dynamic modifications partially mitigate the non-radiative recombination, prolonging the lifetime of photogenerated charge carriers. The suppressed carrier loss mechanism strongly suggests that the fluorinated spacer cation-based LHPs would exhibit enhanced performance as optoelectronic materials. These systematic simulations of excited state carrier dynamics elaborate that chemically viable functionalization of the spacer cations is a robust approach to improve the photophysical properties of 2D halide perovskites strategically. These insights also guide us to propose a few potential strategies to design highly beneficial spacer cations of LHPs that can be introduced in next-generation optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CEGANN: CRYSTAL EDGE GRAPH ATTENTION NEURAL NETWORK

SF-22-156 Machine learning (ML) models and applications in materials design and discovery typically involve the use of feature representations or descriptors followed by a learning algorithm that maps them to user desired properties of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments and suffer from transferability issues across different application domains and/or material classes. The CEGANN code provides a unified interface to facilitate material characterization across materials across multiple scales (from atomic to mesoscale) and diverse classes of materials ranging from metals oxides, non-metals, and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. CEGANN implements a Graph Attention Network (GAT) type convolution architecture. The details of network architecture can be found in the paper https://doi.org/10.48550/arXiv.2207.10168. The software comes with pretrained examples and dataset for the classification of the following representative systems: (1) Structure-level representation such as space group (2) Structural dimensionality (e.g., bulk, 2D, clusters etc.) (3) Grain boundary identification (4) Nucleation and growth of a zeolite polymorph (5) Characterization of binary mesophases and their phase transitions (6) Growth of ice. The code is written in python programming language.

CHAN, HENRYT↗
DOE OSTI2023

Temperature-Dependent Optical and Structural Properties of Chiral Two-Dimensional Hybrid Lead-Iodide Perovskites

Layered hybrid halide perovskites gain chirality via the incorporation of chiral organic cations in the spacer layer. These so-called chiral two-dimensional (2D) halide perovskites have attracted considerable interest recently for chiral optoelectronic, spintronic, and ferroelectric applications. However, the effect of temperature on these materials, especially how the structure changes with temperature and its impact on the chiral optoelectronic properties, remains an open question. Here, we study the effect of temperature change on chiral optoelectronic and structural properties through temperature-dependent circularly polarized photoluminescence (CPPL) microscopy and synchrotron powder X-ray diffraction as well as pair distribution function analysis to elucidate the intrinsic chiral optoelectronic and structural variations for R-, S-, and racemic-methylbenzylamine lead iodide. Here our results show that the temperature-induced band gap changes indicate a strong electron–phonon coupling compared to the thermal-induced lattice expansion in chiral 2D perovskites. From powder diffraction measurements, a monotonic lattice expansion is observed on heating with no structural phase transitions over 90–360 K detected for the three samples studied herein. Locally, a strongly anisotropic and even negative expansion in some components of the instantaneous Pb–I pair-distance distribution is suggestive of coupling between dynamical intralayer distortions and lattice expansion. This work provides insights into the fundamental understanding of the temperature effect on chiral optoelectronic and structural properties of chiral 2D perovskites, which can lead to new chiral materials design strategies for future chiral optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Self-Assembled BaTiO 3 –Au x Ag 1– x Low-Loss Hybrid Plasmonic Metamaterials with an Ordered “Nano-Domino-like” Microstructure

Metallic plasmonic hybrid nanostructures have attracted enormous research interest due to the combined physical properties coming from different material components and the broad range of applications in nanophotonic and electronic devices. However, the high loss and narrow range of property tunability of the metallic hybrid materials have limited their practical applications. In this study, a metallic alloy-based self-assembled plasmonic hybrid nanostructure, i.e., a BaTiO 3 –Au x Ag 1– x (BTO) vertically aligned nanocomposite, has been integrated by a templated growth method for low-loss plasmonic systems. Comprehensive microstructural characterizations including high-resolution scanning transmission electron microscopy (HRSTEM), energy-dispersive X-ray spectroscopy (EDS), and three-dimensional (3D) electron tomography demonstrate the formation of an ordered “nano-domino-like” morphology with Au 0.4 Ag 0.6 nanopillars as cylindrical cores and BTO as square shells. By comparing with the BTO–Au hybrid thin film, the BTO–Au 0.4 Ag 0.6 alloyed film exhibits much broader plasmon resonance, hyperbolic dispersion, low-loss, and thermally robust features in the UV–vis–NIR wavelength region. This study provides a feasible platform for a complex alloyed plasmonic hybrid material design with low-loss and highly tunable optical properties toward all-optical integrated devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Crack mitigation in additively manufactured AlCrFe 2 Ni 2 high-entropy alloys through engineering phase transformation pathway

The far-from-equilibrium solidification during additive manufacturing often creates large residual stresses that induce solid-state cracking. Here we present a strategy to suppress solid-state cracking in an additively manufactured AlCrFe 2 Ni 2 high-entropy alloy via engineering phase transformation pathway. We investigate the solidification microstructures formed during laser powder-bed fusion and directed energy deposition, encompassing a broad range of cooling rates. At high cooling rates (10 4 –10 6 K/s), we observe a single-phase BCC/B2 microstructure that is susceptible to solid-state cracking. At low cooling rates (10 2 –10 4 K/s), FCC phase precipitates out from the BCC/B2 matrix, resulting in enhanced ductility (~10 %) and resistance to solid-state cracking. Site-specific residual stress/strain analysis reveals that the ductile FCC phase can largely accommodate residual stresses, a feature which helps relieve residual strains within the BCC/B2 phase to prevent cracking. Our work underscores the value of exploiting the toolbox of phase transformation pathway engineering for material design during additive manufacturing.

36 MATERIALS SCIENCE↗