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At least 91 records · Page 5

High-throughput synthesis and corrosion behavior of sputter-deposited nanocrystalline Al x (CoCrFeNi) 100-x combinatorial high-entropy alloys

High-entropy alloys (HEAs) are inherently complex and potentially span a vast composition space, making their research and discovery challenging. In the present study, high-throughput synthesis of an Al x (CoCrFeNi) 100-x combinatorial material library covering x = 4.5–40 atomic percent Al is achieved, using magnetron cosputtering. The effects of Al on the microstructure and corrosion behavior are investigated. With the increased amount of Al, crystal-structures of thin films transform from face-centered cubic (FCC) to body-centered cubic (BCC). Both the FCC and BCC thin films demonstrate a uniform elemental distribution. Corrosion characteristics of combinatorial samples immersed in the 3.5 wt% (wt%) NaCl solution are evaluated via electrochemical tests. Complementary X-ray photoelectron spectroscopy analysis reveals the compositional variation of passivated films formed on the sample surface after immersion. The results show that the Al x (CoCrFeNi) 100-x HEA thin films possess outstanding corrosion-resistant properties, but the resistance diminishes with the increasing Al content. The decreased corrosion resistance is revealed to be directly related to the constituents of passivated films.

36 MATERIALS SCIENCE↗

Guided combinatorial synthesis and automated characterization expedites the discovery of hard, electrically conductive Pt x Au 1-x films

Sputter-deposited Pt-Au thin films have been reported to develop a hard, stable, nanocrystalline structure, yet little is known about how these characteristics vary with Pt x Au 1-x composition and process conditions. Toward this end, this document describes an extensive, combinatorial Pt-Au thin film library including characterized film compositions, structure, and properties. Complemented by kinematic Monte Carlo simulations of codeposition, a broad range of Pt x Au 1-x compositions (from x ~ 0.02 to 0.93) was first established by sputtering with varied magnetron powers and gun tilt angles. Further, the produced films were subsequently interrogated using automated nanoindentation, x-ray reflectivity, x-ray diffraction, atomic force microscopy, surface profilometry, four-point probe sheet resistance techniques, and wavelength dispersive spectroscopy in order to determine how hardness, modulus, density, surface roughness, structure, and resistivity vary with film stoichiometry and process parameters. Combinatorial films displayed an assortment of properties with the hardness of some films exceeding values reported previously for this material system. High hardness, high modulus, and low resistivity were generally attained when using increased deposition energy and reduced angle-of-incidence processes. Overall, the research identified promising, new Pt x Au 1-x compositions for future study and pinpointed strategies for improved deposition.

36 MATERIALS SCIENCE↗

High-throughput, combinatorial synthesis of multimetallic nanoclusters

Significance Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computational Investigation of MAX as Intercalation Host for Rechargeable Aluminum‐Ion Battery

Abstract Layered carbides and their analogs with MAX phase (general formula AM n+1 X n ) have emerged as promising candidates for energy storage and conversion applications. One frontier for energy storage is using MAX as an Al‐ion intercalation electrode. Given that many MAXs have Al as the A sites, the structure can potentially serve as a stable host for Al intercalation. Here in this work, 425 ternary MAX Al‐ion battery electrodes are computationally enumerated. Specifically, first principal phase diagram calculations are performed on the combinatorial space of 17 types of typical transition metals, five types of anions (C, N, B, Si, and P), three types of stoichiometries (n = 1, 2, and 3) and two types of layered stackings (α and β). Among all the ternary MAX materials, 44 candidates show reasonable synthetic accessibility, and six with extraordinary performance are predicted to be promising Al‐ion battery electrodes. With the phase stability, and electrochemical performance (average voltage, theoretical capacity, energy density, and Al diffusion barrier), the work provides a comprehensive computational assessment of the great opportunities behind MAX‐based Al‐ion batteries.

Chemistry↗

Combinatorial measurement of critical cooling rates in aluminum-base metallic glass forming alloys

Direct measurement of critical cooling rates has been challenging and only determined for a minute fraction of the reported metallic glass forming alloys. Here, we report a method that directly measures critical cooling rate of thin film metallic glass forming alloys in a combinatorial fashion. Based on a universal heating architecture using indirect laser heating and a microstructure analysis this method offers itself as a rapid screening technique to quantify glass forming ability. We use this method to identify glass forming alloys and study the composition effect on the critical cooling rate in the Al–Ni–Ge system where we identified Al 51 Ge 35 Ni 14 as the best glass forming composition with a critical cooling rate of 10 4 K/s.

36 MATERIALS SCIENCE↗

High‐Throughput Study of Amorphous Stability and Optical Properties of Superlattice‐Like Ge–Sb–Te Thin Films

A high‐throughput ion beam sputtering system is used to synthesize compositional gradient superlattice‐like (SLL) thin film libraries of Ge–Sb–Te alloys over the entire phase diagram. Here, the optical properties and structural evolution of the Ge–Sb–Te combinatorial SLL thin film are investigated. A systematic screening over the annealing temperature, annealing time, and modulation period has elucidated the critical factors that affect the stability of the metastable phase and optical properties. It is found that amorphous stability and optical constant are highly dependent on the modulation period and chemical composition of the thin film. This data‐driven approach offers new perspectives for accelerating the development of new materials with excellent optical and amorphous stability and for exploring their mechanisms, by greatly expanding the dataset of Ge–Sb–Te alloys with SLL structures through high‐throughput experiments.

36 MATERIALS SCIENCE↗

Development of a versatile, high-temperature, high-throughput ion irradiation system

We report ion irradiation has long been used as a surrogate for neutron irradiation experiments which are comparatively slower, more expensive, and often can leave materials activated and difficult to handle post-irradiation. However, with the growing use of combinatorial synthesis methods and high-throughput, automated characterization techniques, ion irradiation facilities will need to upgrade their capabilities to be able to accommodate these new material platforms. To address this challenge, a high-throughput ion irradiation system has been developed at the University of Wisconsin-Madison Ion Beam Laboratory (IBL). The system is capable of accommodating large sample arrays of arbitrary geometries and heat samples individually using an infrared laser to enable high-temperature, high-throughput ion irradiation while mitigating the risk of annealing the irradiation damage in nearby samples. To control stage motion, laser power, and beam current measurement, among other tasks, the program Chronos was developed which enables automated high-throughput irradiation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

Materials Science↗

A combinatory ferroelectric compound bridging simple ABO 3 and A-site-ordered quadruple perovskite

The simple ABO 3 and A-site-ordered AA' 3 B 4 O 12 perovskites represent two types of classical perovskite functional materials. There are well-known simple perovskites with ferroelectric properties, while there is still no report of ferroelectricity due to symmetry breaking transition in A-site-ordered quadruple perovskites. Here we report the high pressure synthesis of an A-site-ordered perovskite PbHg 3 Ti 4 O 12 , the only known quadruple perovskite that transforms from high-temperature centrosymmetric paraelectric phase to low-temperature non-centrosymmetric ferroelectric phase. The coordination chemistry of Hg 2+ is changed from square planar as in typical A-site-ordered quadruple perovskite to a rare stereo type with 8 ligands in PbHg 3 Ti 4 O 12 . Thus PbHg 3 Ti 4 O 12 appears to be a combinatory link from simple ABO 3 perovskites to A-site-ordered AA' 3 Ti 4 O 12 perovskites, sharing both displacive ferroelectricity with former and structure coordination with latter. This is the only example so far showing ferroelectricity due to symmetry breaking phase transition in AA' 3 B 4 O 12 -type A-site-ordered perovskites, and opens a direction to search for ferroelectric materials.

36 MATERIALS SCIENCE↗

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↗

Investigation of synchrotron X-ray induced oxidation of Ag–Cu thin-film

Combinatorial Ag-Cu thin-films were irradiated by synchrotron X-ray in air to investigate the beam damage on the surface of the thin-film. The main effect was found to be oxidation with the oxidation state primarily depending on film composition: CuO formed on the pure Cu film, whereas Cu 2 O formed in the presence of Ag. Meanwhile, formation of crystalline Ag 2 O 2 was favored and preferentially in the Ag-rich area. Lastly, these results are of great importance in studying the oxidation of noble metal nano-film, and in the characterization of nano-films using synchrotron X-ray.

36 MATERIALS SCIENCE↗

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗

Bayesian optimization with active learning of design constraints using an entropy-based approach

Abstract The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.

36 MATERIALS SCIENCE↗

Combinatorial investigation of the Mn–Ge–N chemical system containing ternary nitrides Mn 3 GeN and MnGeN 2

By changing nitrogen chemical potential during synthesis of Mn–Ge–N ternary nitrides, both wurtzite MnGeN 2 and antiperovskite Mn 3 GeN ternary phases are prepared. Antiperovskite films are optically opaque and conductive, while wurtzite films with Mn/(Mn + Ge) ≤ 0.5 transmit light above ~ 2 eV. Alloys of Mn 3 GeN with Si and Al are also investigated. Mn 3 (Ge 1−x Al x )N alloys with 0.07 ≤ x ≤ 0.16 exhibit a cubic (rather than tetragonal) structure. Mn 3 (Ge 1−x Si x )N with x ≤ 0.05 maintains the tetragonal structure but becomes cubic when x > 0.05. This study shows that care must be taken in the synthesis of Mn–Ge–N and similar nitrides, especially when materials are integrated into devices not amenable to structural and chemical probing.

36 MATERIALS SCIENCE↗

Aluminum Ultra-conductors for Energy-Efficient Aerospace Busbar Applications (Abstract)

In this project, we will develop aluminum ultra-conductors with graphene additives demonstrating enhanced electrical conductivity at 90 °C compared to electric grade aluminum alloy AA1100 (43% IACS at 90 °C). While ultra-conductivity has been developed in copper and copper alloys, it is yet to be reported extensively in aluminum-based materials. This project will scale initial work done at PNNL on aluminum ultra-conductors using shear-assisted processing and extrusion (ShAPETM), a novel solid phase processing technique. Ultra-conductors are an emerging class of composites, comprised of a metal substrate with small quantities of nanocrystalline additives such as graphene or carbon nanotubes that demonstrate enhanced conductivity at relevant operating temperatures. Aluminum ultra-conductors can improve efficiency and power density while reducing the demand for copper in a wide range of applications, such as power transmission cables and electric motors. Busbars are an important component in aerospace systems that require lightweight and high-current power distribution including both future electric vertical take-off and landing (eVTOL) aircrafts and current aircraft electrical systems. We will accelerate aluminum ultra-conductor composite formulation development using combinatorial synthesis and testing methods aided by process/microstructure modeling, developed previously at PNNL. Eaton will test the properties of the ShAPE aluminum ultra-conductor feedstock (used to make the busbars) in relevant operating conditions (20 – 90 °C), predict the improvement in busbar performance when manufactured with ultra-conductors over commercial conductors (such as AA1100), and perform technoeconomic analysis to evaluate the potential for commercialization of ShAPE aluminum ultra-conductors. The project is expected to have a budget of $\$375$K, with $\$300$K in federal funding and $\$75$K in-kind cost-share contribution from Eaton over a period of performance of 24 months. Of the $\$300$K of federal funds, $\$140$K is allocated for CRADA activities that generate intellectual property (IP), and the remaining $\$160$K is reserved for modeling, material testing, characterization, travel, and reporting-related activities.

36 MATERIALS SCIENCE↗

Progress in Understanding the Origins of Excellent Corrosion Resistance in Metallic Alloys: From Binary Polycrystalline Alloys to Metallic Glasses and High Entropy Alloys

Some of the factors responsible for good corrosion resistance of select polycrystalline and emerging alloys in chloride solutions are discussed with a goal of providing some perspectives on the current status and future directions. Traditional metallic glass alloys, single phase high entropy alloys (HEAs), early metallic glasses, and high entropy metallic glasses are all emerging corrosion-resistant alloys (CRAs) that utilize traditional strategies for improved corrosion resistance as well as take advantage of some other novel beneficial attributes. These materials enjoy many degrees of freedom as far as choice of both composition and structure, providing great flexibility in the pursuit of superior corrosion resistance. The new materials depart from classical solvent-solute type polycrystalline binary or ternary alloys. Thus, such emerging materials provide significant opportunities to achieve even greater improvements in corrosion resistance in harsh environments. Several examples of the unique corrosion properties of selected materials in the context of modern theories of corrosion are discussed herein. Discussion is restricted to solid-solution binary or ternary polycrystalline alloys, several metallic glass alloys, and single phase HEAs. A common feature of many CRAs is that composition and microstructure often affect both passivity and resistance to localized corrosion that can be divided into initiation, stabilization, and propagation stages. Enormous complexities in protective oxide structures and chemistries and the large number of combinatorial possibilities in newer materials such as HEAs preclude trial-and-error approaches and perhaps even combinatorial experimental design. Computational materials methodologies will be required in the search for new corrosion-resistant alloys in these material classes. The search must consider the best scientific insights available regarding how major and minor alloy additions, as well as various microstructural attributes, contribute to corrosion mitigation. Additional scientific insights, as they emerge, will enable choices beyond the reliance on high concentrations of alloying elements that are known to affect passivity breakdown and pit stabilization. A challenge is to connect the “basic attributes” of an alloy with its properties. The strength of this connection will likely require new scientific principles enabling deep multiphysics insights in order to link feature(s) such as composition and metallurgical phases to the desired corrosion properties. Application of data informatics will likely also play a role given the plethora of variables that are important in corrosion and the difficulty in assessing all relationships. Here, the opportunity exists to accelerate the design of emerging materials for high corrosion resistance.

36 MATERIALS SCIENCE↗