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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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DOE OSTI2019

Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy

The third Sandia Fracture Challenge illustrated the geometric and material uncertainties presented by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Particularly, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This method allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.

316L stainless steel↗
DOE OSTI2023

The AXEAP2 program for K β X-ray emission spectra analysis using artificial intelligence

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant K β XES of 3 d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 ( AXEAP2 ), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3 d electron spin state, 3 d –3 p electron exchange force and K β emission core-hole lifetime.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Critical parameters in the faculty application process: A data-driven analysis

We report the process of applying for science and engineering faculty positions requires a significant time investment for both the applicant and hiring committees. Applicants face the daunting task of sifting through a seemingly endless amount of information related to crafting their application packages in preparation for a review process that is inherently highly subjective. Furthermore, while scientific supervisors are often the most valuable resource during this stage of career development, many years may have passed since they went through this process themselves, and they may not have experience with recent developments in the application process. Because expectations and job responsibilities of new faculty members are rapidly evolving, certain guidelines are often not clearly specified, and it is therefore becoming difficult for applicants to most effectively prepare their application documents. Without access to adequate information and assistance, early-career researchers in particular can be placed at a significant disadvantage in the hiring process. Several recent articles can help prospective applicants in materials science and engineering land a faculty position,1,2,3 but there is nevertheless a need for clarification on expectations for different aspects of the faculty application materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Determining the Oxygen Stoichiometry of Cobaltite Thin Films

Transition metal oxides (TMO) are promising materials to realize low-power neuromorphic devices. Their physical properties critically depend on their oxygen vacancy concentrations, whose experimental determination remains a challenging task. Here we focus on cobaltites, in particular La 1-x Sr x CoO 3-d (LSCO), and we present a strategy to identify fingerprints of oxygen vacancies in X-ray absorption (XA) spectra. Using a combination of experiment and theory, we show that the variation of the oxygen vacancy concentration in the perovskite phase of LSCO is correlated with the change of the relative peak positions of the O K-edge XA spectra. Furthermore, we also identify an additional geometrical fingerprint that captures both the changes of the Co-O bond length and Co-O-Co bond angle in the material due to the presence of oxygen vacancies. Finally, we predict the oxygen vacancy concentration of experimental samples and show how the resistivity of the oxide material may be tuned as a function of the defect concentration present in the system.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Durability of Corrosion Protection Coatings for Aluminum

The objectives of this work are to (1) Develop a detailed understanding of the performance characteristics of current chromate conversion coatings utilized in the stockpile, (2) Develop a detailed understanding of the performance characteristics of non-chromate conversion coatings proposed to replace their chromate containing counterparts, (3) Provide an informed assessment of the aforementioned coatings and a recommended course of action for the replacement of chromate containing coatings, and (4) establish subject matter expertise in the area of replacement coatings to provide as a resource to the systems organization. These tasks will be accomplished by performing a battery of traditional corrosion performance tests (in accordance with the relevant MIL specifications governing chemical conversion coatings), combined with more detailed analytical evaluation of the materials. Coatings will be evaluated in the as-received and artificially aged condition. Four different coating systems were evaluated on four different aluminum alloys in FY16. The focus this year was to continue populating the test matrix initiated in previous years, evaluating key coatings in the as received and thermally stressed condition, as compared to the baseline chromate conversion coating (Bonderite (formerly Alodine) 1200S). The non-chromate conversion coatings included Bonderite T 5900, Chemeon TCP-HF, Luster-On Aluminescent, and MacDermid-Enthone Iridite NCP. A second effort that was completed this year was to address a bottleneck that existed for the electrical evaluation of the coatings, assessing their conformance with the MIL specification. An electromechanical test system was assembled and automated, eliminating the need to rely on an already heavily utilized mechanical test system and operator in Materials Mechanics and Tribology.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Correlation of Surface Acoustic Wave (SAW) force myography sensor output with elbow joint torque

Accurate assessment of skeletal muscle forces and net joint torque is essential for preventing fatigue-related injuries, optimizing physical training, and monitoring disease progression in neuromuscular conditions. However, existing joint torque evaluation techniques are hindered by limited portability and high operational costs, confining their use to controlled laboratory or clinical settings. Despite substantial advances in wearable joint torque estimation systems, ongoing challenges such as power constraints, bulky wired setups, and susceptibility to environmental or motion artifacts underscore the urgent need for truly batteryless, wireless solutions deployable in real-world settings. This paper proposes a novel surface acoustic wave (SAW)-based force myography (FMG) system for noninvasive measurement of joint torque, validated against a gold-standard electromechanical dynamometer. The approach uses a single SAW sensor embedded in an armband to detect volumetric biceps brachii changes, with a second-order polynomial mapping sensor output and elbow angle to torque. Seven participants were tested in both isometric (15°–90°) and isokinetic (10°/s and 20°/s) supinated elbow flexion tasks. Under isometric conditions, subject-specific calibration achieved a normalized root-mean-square error (NRMSE) of 13.6% ± 6.0% and R 2 = 0.834 ± 0.180, while a group-level model yielded 14.4% ± 6.8% and 0.808 ± 0.208, respectively. For isokinetic trials, the group model produced an NRMSE of 24.1% ± 6.6% at 10°/s and 24.9% ± 08.9% at 20°/s, highlighting the feasibility of using a single-sensor SAW-FMG setup across different speeds. Because SAW devices support wireless, battery-free operation, the proposed system offers a pathway to portable, real-time monitoring for sports medicine, rehabilitation, and clinical diagnostics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Spider silk MASP1 and MASP2 proteins as carbon fiber precursors

The objective of this project is to develop an unconventional non-petroleum based carbon fiber precursor which has the potential to be produced in high yield and quantities. Methods will be developed to produce pilot-scale quantities of fibers from spider silk proteins with mechanical properties at least 75% that of the natural dragline silk fibers in tensile strength and elongations of less than 5%. The precursor fibers will be converted to carbon fibers, with a goal of >250Ksi strength and 1-2% elongation. Cost analysis will be performed and the process optimized. Task 1: Subtask 1. Protein production: We exceeded the go/ no go milestone of 1.0g/L of one of the spider silk protein (MSp2) purified last FY and have now increased from 5L to 500L fermentations. We have made a series of changes to the purification protocol from the initial report last FY. These led to a reduction in the time needed for the purification and reduced the purification costs by nearly 90%. Subtask 2. Fiber spinning: The major focus has been to produce more material to send 24 fiber thread to ONRL. We are still developing the methodology to successfully spin 24 fiber yarns. This involves both the spinning dope solutions as well as the methods to keep the fibers from fusing during the post spin stretch. The second area of focus has been to standardize the spin dopes for making the fibers. We now know that the conductivity (indicative of salt remaining with the protein after purification) is an important factor in successful spinning as is the pH. We now know that we need to be below 600 uS conductivity and that the most effective pH is protein dependent. Subtask 3. Silkworm silk: We have found the transgenic silkworms made using gene replacement at the fibroin light chain instead of heavy chain as we did previously have a higher tensile strength. See figures below showing the curve for the top end of the cocoon fibers. This tensile strength is the same as the average for spider dragline silk. Task 2. Carbonization: The major accomplishment in the latter part of the work is that the ONRL group has successfully heated the spider silk protein fibers all the way up to 1700°C and produced a very competitive carbon fiber based on mechanical properties. Several important factors were discovered during these initial trials: 1) the ramp speed for increasing the temperature is critical; 2) maintaining tension on the fiber during the heating process because as it is heated it tends to expand; and 3) narrow temperature window in which stretching the fiber during heating leads to much better final materials. Task 3. Techno-Economic Methods: The techno-economic analysis was expanded to determine the relative cost of production with the bacterial production system compared to the transgenic alfalfa and goat production systems. The comparisons show two important things. For all systems the key factor in the final price is the amount of spider silk protein produced for whatever measure of volume or weight is used. Second alfalfa can be the cheapest but is subject to the possible regulatory control unless the US develops a more comprehensive approach to GMOs. The silkworm analysis was not completed due to a variety of confounding factors. The primary one was that if the production were shifted overseas then the cost would likely be nearly equivalent to current silk prices of $5-15/kg. However if concerns about the location of production is important then it would need to be done in the US and initial costs would be much higher but if the later scenario is utilized then the cost would be lowered but it was not possible to calculate exact costs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Intramolecular Noncovalent Interactions Facilitate Thermally Activated Delayed Fluorescence (TADF)

In the conventional molecular design of thermally activated delayed fluorescence (TADF) organic emitters, simultaneously achieving a fast rate of reverse intersystem crossing (RISC) from the triplet to the singlet manifold and a fast rate of radiative decay is a challenging task. A number of recent experimental data, however, point to TADF emitters with intramolecular π-π interactions as a potential pathway to overcome the issue. Here, we report a comprehensive investigation of TADF emitters with intramolecular π…π or lone-pair…π non-covalent interactions. We uncover the impact of those intramolecular non-covalent interactions on the TADF properties. In particular, we find that folded geometries in TADF molecules can trigger lone-pair…π interactions, introduce a n → π* character of the relevant transitions, enhance the singlet-triplet spin-orbit coupling, and ultimately greatly facilitate the RISC process. Furthermore, this work provides a robust foundation for the molecular design of a novel class of highly efficient TADF emitters in which intramolecular non-covalent interactions play a critical function.

36 MATERIALS SCIENCE↗
DOE OSTI2017

High-Throughput Study of Diffusion and Phase Transformation Kinetics of Magnesium-Based Systems for Automotive Cast Magnesium Alloys

The objective of the proposed study is to establish a scientific foundation on kinetic modeling of diffusion, phase precipitation, and casting/solidification, in order to accelerate the design and optimization of cast magnesium (Mg) alloys for weight reduction of U.S. automotive fleet. The team has performed the following tasks: 1) study diffusion kinetics of various Mg-containing binary systems using high-throughput diffusion multiples to establish reliable diffusivity and mobility databases for the Mg-aluminum (Al)-zinc (Zn)-tin (Sn)-calcium (Ca)-strontium (Sr)-manganese (Mn) systems; 2) study the precipitation kinetics (nucleation, growth and coarsening) using both innovative dual-anneal diffusion multiples and cast model alloys to provide large amounts of kinetic data (including interfacial energy) and microstructure atlases to enable implementation of the Kampmann-Wagner numerical model to simulate phase transformation kinetics of non-spherical/non-cuboidal precipitates in Mg alloys; 3) implement a micromodel to take into account back diffusion in the solid phase in order to predict microstructure and microsegregation in multicomponent Mg alloys during dendritic solidification especially under high pressure die-casting (HPDC) conditions; and, 4) widely disseminate the data, knowledge and information using the Materials Genome Initiative infrastructure (http://www.mgidata.org) as well as publications and digital data sharing to enable researchers to identify new pathways/routes to better cast Mg alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I ( V )-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er 0.99 ,Zr 0.01 )MnO 3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er 0.99 ,Zr 0.01 )MnO 3 . Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Review of Artificial Abrasion Test Methods for PV Module Technology

This review is intended to identify the method or methods--and the basic details of those methods--that might be used to develop an artificial abrasion test. Methods used in the PV literature were compared with their closest implementation in existing standards. Also, meetings of the International PV Quality Assurance Task Force Task Group 12-3 (TG12-3, which is concerned with coated glass) were used to identify established test methods. Feedback from the group, which included many of the authors from the PV literature, included insights not explored within the literature itself. The combined experience and examples from the literature are intended to provide an assessment of the present industry practices and an informed path forward. Recommendations toward artificial abrasion test methods are then identified based on the experiences in the literature and feedback from the PV community. The review here is strictly focused on abrasion. Assessment methods, including optical performance (e.g., transmittance or reflectance), surface energy, and verification of chemical composition were not examined. Methods of artificially soiling PV modules or other specimens were not examined. The weathering of artificial or naturally soiled specimens (which may ultimately include combined temperature and humidity, thermal cycling and ultraviolet light) were also not examined. A sense of the purpose or application of an abrasion test method within the PV industry should, however, be evident from the literature.

14 SOLAR ENERGY↗
DOE OSTI2020

Constructing protein polyhedra via orthogonal chemical interactions

Many proteins exist naturally as symmetrical homooligomers or homopolymers. The emergent structural and functional properties of such protein assemblies have inspired extensive efforts in biomolecular design2–5. As synthesized by ribosomes, proteins are inherently asymmetric. Therefore, they must acquire multiple surface patches that selectively associate to generate the different symmetry elements needed to form higher-order architectures—a daunting task for protein design. Here we address this problem using an inorganic chemical approach, whereby multiple modes of protein–protein interactions and symmetry are simultaneously achieved by selective, ‘one-pot’ coordination of soft and hard metal ions. We show that a monomeric protein (protomer) appropriately modified with biologically inspired hydroxamate groups and zinc-binding motifs assembles through concurrent Fe 3+ and Zn 2+ coordination into discrete dodecameric and hexameric cages. Our cages closely resemble natural polyhedral protein architectures and are, to our knowledge, unique among designed systems in that they possess tightly packed shells devoid of large apertures. At the same time, they can assemble and disassemble in response to diverse stimuli, owing to their heterobimetallic construction on minimal interprotein-bonding footprints. With stoichiometries ranging from [2 Fe:9 Zn:6 protomers] to [8 Fe:21 Zn:12 protomers], these protein cages represent some of the compositionally most complex protein assemblies—or inorganic coordination complexes—obtained by design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Emerging iongel materials towards applications in energy and bioelectronics

In the past two decades, ionic liquids (ILs) have blossomed as versatile task-specific materials with a unique combination of properties, which can be beneficial for a plethora of different applications. The additional need of incorporating ILs into solid devices led to the development of a new class of ionic soft-solid materials, named here iongels. Nowadays, iongels cover a wide range of materials mostly composed of an IL component immobilized within different matrices such as polymers, inorganic networks, biopolymers or inorganic nanoparticles. Here this review aims at presenting an integrated perspective on the recent progress and advances in this emerging type of material. We provide an analysis of the main families of iongels and highlight the emerging types of these ionic soft materials offering additional properties, such as thermoresponsiveness, self-healing, mixed ionic/electronic properties, and (photo)luminescence, among others. Next, recent trends in additive manufacturing (3D printing) of iongels are presented. Finally, their new applications in the areas of energy, gas separation and (bio)electronics are detailed and discussed in terms of performance, underpinning it to the structural features and processing of iongel materials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Radiation behavior of high-entropy alloys for advanced reactors. Final report

In the first task, we have demonstrated the radiation damage and the recrystallization behaviors in multicomponent alloys through molecular-dynamics simulations. It is found that by alloying with atoms of different sizes, the atomic-level strain increases, and the propensity of the radiation-induced crystalline to amorphous transition increases as the defects cluster in the cascade body. Recrystallization of the radiation induced supercooled or glass regions show that by tuning the composition and the equilibrium temperature, the multicomponent alloys can be healed. The crystalline-amorphous-crystalline transitions predict the potential high radiation resistance in multicomponent alloys. In the second task, three types of high-entropy alloys (HEAs) were fabricated from AlCoCrFeNi and AlCuCrFeNi quinary alloys. Hardness and reduced contact modulus were measured using nanoindentation tests. Heavy ion irradiation were performed using 10 MeV gold and 5 MeV nickel to study radiation effects. Al 0.5 CrCuFeNi 2 shows phase separation upon the presence of copper. Both hardness and contact modulus exhibit the same trend as increasing the applied load, and it indicates that excessive free volume may alter the growth rate of the plastic zone. The as-cast Al 0.1 CoCrFeNi specimen undergone the hot isostatic pressing (HIP) process and steady cooling rate which mitigate the quenching effect. The swelling behavior was characterized by the atomic force microscopy (AFM), and the swelling rate is approximately 0.02% dpa. Selected area diffraction (SAD) patters show irradiation-induced amorphization throughout the ion projected range. Within the peak damage region, an amorpous ring is observed, and a mixture of amorphous/ crystalline structure at deeper depth is found. The Al 0.3 CoCrFeNi HEAs shows good radiation resistance up to 60 peak dpa. No voids or dislocations are observed. The crystal structures remain face-centered-cubic (FCC) before and after 5 MeV Ni irradiation. Higher dpa might be required to study defects formation mechanisms. In the third task, all the constituent binary and ternary systems of the Al-Co-Cr-Fe-Ni system were thermodynamically modeled within the whole composition range. Comparisons between the calculated phase diagrams and literature data are in good agreement. The multi-component thermodynamic database of the Al-Co-Cr-Fe-Ni system was then obtained via extrapolation. The current Al-Co-Cr-Fe-Ni thermodynamic database enables us to carry out the calculations of phase diagrams, which can be used as useful guidelines to identify the Al-Co-Cr-Fe-Ni HEAs with desirable microstructures. In the fourth task, we discuss how as-cast and homogenized phases can be identified, what phases are usually found in the as-cast and homogenized conditions, and what the thermodynamics and kinetics of phase transformations are in the AlCoCrFeNi HEA. The microstructure and phase composition were studied in as-cast and homogenized conditions. It showed the dendritrical structure in the as-cast condition consisting primarily of a nano-lamellar mixture of A2 [disorder body-centered-cubic (BCC)] and B2 (ordered BCC) phases, in addition to a very small amount of A1 [disorder face-centered-cubic (FCC)] phases. The homogenization heat treatment resulted in an increase in the volume fraction of the A1 phase and formation of a Sigma phase. Tensile properties in as-cast and homogenized conditions are reported at 700 °C. Thermodynamic modeling of non-equilibrium and equilibrium phase diagrams for the AlCoCrFeNi HEA gave good agreement with the experimental observations of the phase contents. The reasons for the improvement of ductility after the heat treatment are discussed.

36 MATERIALS SCIENCE↗