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At least 955 records · Page 53

Automated crystal orientation and phase mapping in TEM

The paper describes an automated crystal orientation and phase mapping technique that allows nanoscale characterization of crystalline materials with a transmission electron microscope. The template matching strategy used to identify the diffraction patterns is detailed and the resulting outputs of the technique are illustrated. Some examples of applications are used to demonstrate the capability of the tool and potential developments are discussed.

36 MATERIALS SCIENCE↗

Formation of less-known structurally complex ζ{sub b} and orthorhombic quasicrystalline approximant ε{sub n} on solidification of selected Al–Pd–Cr alloys

The evolution of phases was investigated on cooling of Al{sub 71}Pd{sub 24}Cr{sub 5}, Al{sub 73}Pd{sub 20}Cr{sub 7}, and Al{sub 73}Pd{sub 23}Cr{sub 4} alloys from 1350 °C down to ambient temperature with the rate of 10 °C·min{sup −1}. To perform the investigation, differential thermal analysis, synchrotron X-ray powder diffraction, and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy were used. In all the investigated alloys structurally complex phases ε{sub n} (ε{sub 6} + ε{sub 28}) and ζ{sub b}, as well as the δ-phase were identified. Based on the results of differential thermal analysis, sequences of phase transformations were determined. The Al{sub 71}Pd{sub 24}Cr{sub 5} alloy started to solidify at 1031.4 °C through β. Primary dendrites of ζ{sub b} were observed in Al{sub 73}Pd{sub 20}Cr{sub 7} and Al{sub 73}Pd{sub 23}Cr{sub 4} alloys. In the second step of solidification δ and/or ζ{sub b} were formed. The peritectic reaction, liquid + ζ{sub b} + δ → ε{sub n} + ζ{sub b} + δ, leading to the formation of the quasicrystalline approximant ε{sub n} (ε{sub 6} + ε{sub 28}) took place in the final step of solidification at approximately 792 °C. - Highlights: • Structurally complex ε{sub n} (ε{sub 6} + ε{sub 28}), ζ{sub b} and δ-phases were identified. • The Al{sub 71}Pd{sub 24}Cr{sub 5} alloy started to solidify at 1031.4 °C through the primary β phase. • The Al{sub 73}Pd{sub 20}Cr{sub 7} and Al{sub 73}Pd{sub 23}Cr{sub 4} alloys solidified in the same way. • The quasicrystalline approximant ε{sub n} (ε{sub 6} + ε{sub 28}) was formed at approximately 792 °C.

36 MATERIALS SCIENCE↗

The impact of nickel concentration and stacking fault energy on deformation mechanisms in high-purity austenitic Fe-Cr-Ni alloys

Understanding how composition affects deformation mechanisms in austenitic stainless steels is essential for developing accurate predictive models of stress-induced failures and stress corrosion cracking. Nickel (Ni), an element classified as a critical element, plays a crucial role in these processes. It is important to examine how Ni concentration influences stacking fault energy (SFE) and, consequently, the deformation mechanisms of austenitic stainless steels. However, in commercial stainless steels, the effects of other alloying elements and impurities can obscure Ni's role, complicating efforts to isolate its impact. Here, in this study, we use two high-purity Fe-Cr-Ni alloys to investigate how Ni concentration and SFE interact to alter deformation mechanisms and induce martensitic transformation. By combining in situ synchrotron X-ray diffraction (XRD) tensile testing and post-mortem electron microscopy with density functional theory simulations, we gain precise insights into these phenomena. We find that the Fe18Cr10Ni (wt%) alloy, with its low SFE, exhibits higher stacking fault probability, deformation-induced martensitic transformation, and a lesser increase in dislocation density with plastic strain. In contrast, the Fe18Cr14Ni (wt%) alloy, with its higher SFE, shows enhanced deformation twinning and greater dislocation density with increasing strain. These findings from high-purity ternary alloys provide valuable insights that can guide the search for alternative elements to replace Ni while achieving similar effects on phase stability and deformation behavior.

36 MATERIALS SCIENCE↗

Coupling computational thermodynamics with density-function-theory based calculations to design L12 precipitates in Fe Ni based alloys

Achieving a high-volume fraction of thermodynamically stable L12-type precipitates that are resistant to coarsening is of great importance for the development of low-cost FeNi based austenitic steels. With the aid of computational thermodynamics, this work designed two model alloys: Fe-37.4Ni-6.1Al-2.9Ti (FNAT) and Fe-45.2Ni-5.9Al-8.5Si (FNAS). Both alloys were designed to contain a similar amount of L1 2 precipitate in Fe-Ni matrix without forming other precipitates. Density-Function-Theory (DFT) calculation was coupled with computational thermodynamics to predict the critical radius at which the precipitates change shape from spherical to cuboidal. The calculation results suggest that critical radius for the FNAT alloy is about two orders of magnitude larger than that for the FeNiAlSi alloy. Phase stability and morphology of the L1 2 precipitates in these two alloys were experimentally investigated through X-ray diffraction, atom probe tomography, and scanning and transmission electron microscopy. The L1 2 precipitates in the FeNiAlSi system were found to be cuboidal and rod shaped, with much larger size than the spherical ones in the FeNiAlTi system, agreeing with the calculation results. This work suggested that coupling computational thermodynamics with DFT calculations can be reliably used to design L1 2 precipitates in FeNi based alloys.

36 MATERIALS SCIENCE↗

Novel NiAl-strengthened high entropy alloys with balanced tensile strength and ductility

A single phase, face-centered-cubic (FCC) Al 0.3 CoCrFeNi high entropy alloy usually has low yield strength. Here, a precipitate-strengthened Al 0.3 CoCrFeNi has been developed, exhibiting enhanced yield strength while retaining good ductility, which is attributed to a novel microstructure comprising a finely distributed, needle-like B2 phase within the grains of the FCC matrix and a granular σ phase along the grain boundaries. Such a microstructure was obtained by a two-step heat treatment of an as-cast Al 0.3 CoCrFeNi, whose parameters were determined by integrating CALPHAD-based thermodynamic calculations with microstructural characterization by atom probe tomography. In situ neutron diffraction, in conjunction with crystal-plasticity finite-element simulations, has revealed the strengthening effect owing to the load partitioning between the constituent phases. Here, this work has important implications for understanding phase stability and deformation mechanisms in multi-principal component alloys, and paves the way for developing novel microstructures in complex alloys using correlative techniques.

36 MATERIALS SCIENCE↗

Accurate prediction of short-range order and its effect on thermodynamic, structural, and electronic properties of disordered alloys: exemplified in archetypical Cu 3 Au

Electronic-structure methods based on density-functional theory (DFT) were used to quantify the effect of chemical short-range order (SRO) on thermodynamic, structural, and electronic properties of archetypal face-centered-cubic (fcc) Cu3Au alloy. We showed that SRO can be tuned to alter bonding and lattice dynamics (i.e., phonons) and detail how these properties are changed with SRO. Thermodynamically favorable SRO significantly improved the phase stability of fcc Cu3Au from -0.0343 eV-atom -1 to –0.0682 eV-atom -1 . We used our DFT-based linear-response theory to predict SRO and its electronic origin, and accurately estimate the observed transition temperature, ordering instability (L1 2 ), and Warren-Cowley SRO parameters, in agreement with experiments. The accurate prediction of real-space SRO gives an edge over computationally and resource intensive approaches such as monte-carlo methods or experiments, which will enable large scale molecular dynamic simulations by providing supercells with optimized SRO. Here we also analyzed phonon dispersion and estimated the vibrational entropy change (from 9kB at 300 K to 6kB at 100 K) in fcc Cu3Au. We established from SRO analysis that exclusion of chemical interactions may lead to a skewed view of true properties in chemically complex alloys. The first-principles methods described in this work are generally applicable to any arbitrary solid-solution alloys, including multi-principal-element alloys, therefore, holds promise for designing technologically useful materials.

36 MATERIALS SCIENCE↗

Critical mineral substitutions in IN617: A combined computational and experimental approach to performance evaluation and feasibility

Addressing the escalating demand for critical minerals (CMs) driven by global climate change initiatives, this study explores compositional modifications to Inconel 617 (IN617) by substituting cobalt (Co) with manganese (Mn) across various atomic percentages. Here, we conducted a computational feasibility study employing Molecular Dynamics (MD) simulations to provide strategic guidance for experimental validation. The simulations analyzed tensile strength and corrosion resistance for five modified compositions (M1 to M5) to identify optimal properties. Tensile tests on cubic simulation cells were performed to generate stress-strain curves, revealing the impact of Co replacement with Mn on tensile strength—a metric correlated with hardness. Oxygen penetration simulations were conducted to evaluate corrosion resistance, indicating that reduced oxygen penetration depth corresponds to enhanced resistance. Promising compositions underwent phase diagram calculations for assessing phase stability. The optimal composition (M1), characterized by high tensile strength and minimal oxygen penetration, was chosen for experimental validation using induction melting and friction stir consolidation techniques. The materials were further characterized using SEM-EDS, XRD, and Vickers hardness testing. Our findings suggest that Mn substitution in IN617 can yield mechanical performance at par with high-Co alloys in energy-critical applications.

36 MATERIALS SCIENCE↗

Perovskite Quantum Dot Solar Cells: Mapping Interfacial Energetics for Improving Charge Separation

Colloidal halide perovskite nanocrystals or quantum dots (QDs) show similar defect tolerance as thin film perovskite materials with added nanoscale phenomena. Perovskite QD solar cells have demonstrated efficiencies of 16.6%, greater than that of any other QD material system. While the efficiency lags behind the best thin-film perovskite devices, these solar cells could have advantages over the thin-film versions in terms of processability, phase stability, and high open-circuit voltages. However, some operating principles behind perovskite quantum dot device stacks and the associated electric field properties are still unknown. Here, we characterize the junction structure within perovskite QD solar cells, by exposing functioning cross-sections and using nanometer-scale Kelvin probe force microscopy to offer insight into the selection and performance of charge selective contacts. We also evaluated various solar cell device architectures with different selective contacts to isolate the role of each junction in device performance. We show that in high-performance n-i-p architectures, both electron- and hole-transport layer (HTL) interfaces possess a strong electric field, but in the case of the inverted p-i-n architecture, we find that high interfacial recombination at the HTL/QD junction is responsible for subpar device performance. Perovskite QD and thin film materials can synergistically be combined to offer more design flexibility in PV devices, and here we demonstrate that the interface between perovskite thin films and QDs are relatively benign and amenable for synergistic device design.

14 SOLAR ENERGY↗

Density functional theory study of defects in unalloyed δ-Pu

Using density functional theory, we explore in this paper various classical point and complex defects within the face-centered cubic unalloyed δ-plutonium matrix that are potentially induced from self-irradiation. For plutonium only defects, the most energetically stable defect is a distorted split-interstitial. Gallium, the δ-phase stabilizer, is thermodynamically stable as a substitutional defect, but becomes unstable when participating in a complex defect configuration. Finally, complex uranium defects may thermodynamically exist as uranium substitutional with neighboring plutonium interstitial and stabilization of uranium within the lattice is shown via partial density of states and charge density difference plots to be 5f hybridization between uranium and plutonium.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗

Thermodynamics of cesium lead halide (CsPbX 3 , x= I, Br, Cl) perovskites

Inorganic cesium lead halide (CsPbX 3 , X = I, Br, Cl) perovskites have sparked broad interest for photovoltaic applications, which, however, may be plagued by chemical and thermodynamic instabilities. This work reports calorimetric studies on the formation enthalpies of CsPbX 3 perovskites, confirming decreasing thermodynamic stability in the order CsPbCl 3 , CsPbBr 3 and CsPbI 3 . Cesium lead iodide (CsPbI 3 ), one of the most studied inorganic lead halide perovskites, is shown to be only barely stable from a thermodynamic perspective. This order of increasing instability is consistent with the variation of Goldschmidt tolerance factor. The perovskites are high temperature entropy-stabilized phases with respect to low temperature polymorphs, with the thermodynamics of phase transitions reflecting the above thermodynamic trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hydration structure and energetics of iron-exchanged montmorillonite

The interaction between iron released from corroded steel canisters and bentonite is a key process influencing the long-term performance of nuclear waste repositories. In particular, the migration of Fe²⁺ into montmorillonite (Mnt) interlayers may alter its hydration, swelling, and ion-transport properties. In the present study, molecular dynamics simulations were performed to investigate the hydration behavior, structural response, and transport properties of Fe-exchanged montmorillonite (Fe-Mnt) under varying hydration states. The simulations focus on short- to intermediate-time-scale Fe 2+ and Fe 3+ interlayer exchange and hydration effects, and do not consider long-term structural substitution, Fe-bearing clay phase stabilization, or secondary iron mineral precipitation. Systems containing Na + -, Fe 2+ -, and Fe 3+ -Mnt were examined using both periodic and edge-exposed configurations to evaluate interlayer structure, ion exchange, and free energy of Fe intercalation. The results show that Fe ions influence the interlayer spacing primarily at low water contents (<1 bilayer), where Fe-Mnt exhibits a d-spacing 1–2 Å larger than Na-Mnt due to stronger hydration. The calculated hydration energies follow the order Fe 2+ $<$Fe 3+ $<$Na + . Both water and ion diffusion coefficients decrease upon Fe ion intercalation, with Fe 2+ ions diffusing an order of magnitude more slowly than those of Na + . Free energy profiles further confirm that Fe 2+ and Fe 3+ ions are thermodynamically favored in the interlayer, with Fe 3+ being the most stable. In conclusion, these findings provide molecular-scale insights into the mechanisms of Fe–Na exchange and their implications for bentonite alteration in repository environments.

clay↗

Probing Electrochemical Mg-Ion Activity in MgCr 2– x V x O 4 Spinel Oxides

Mg migration in oxide spinels is impeded by strong affinity between divalent Mg and oxygen, suggesting a necessity of exploring new chemistry of solid lattices for functional Mg-ion electrode materials. Cationic mobility with a suitable activation energy in Cr spinels is Tn. evidenced by theoretical and experimental results, while redox potentials of V are appropriate to operate with currently limited candidates of '''c" nonaqueous electrolytes. By controlling the structure, composition, and complexity, a largely solid-solution MgCrVO 4 spinel was synthesized, which, unlike nanocornposites, can bring together the advantages of each transition metal in the lattice. The spinel was successfully synthesized by a simple solid-state reaction with minor inactive Cr- or V-rich components, which was confirmed via 25 Mg MAS NMR and high-resolution X-ray diffraction analyses. A thermally and anodically stable Mg(TPFA) 2 /triglyme electrolyte was utilized for high-temperature electrochemistry and lowering kinetic barriers at and across interfaces so as to observe intercalation behavior of Mg in the designed lattice. Multimodal characterization confirmed an apparent bulk demagnesiation from MgCrVO 4 with partial reversibility by probing evolution of the local and long-range structure as well as vanadium and chromium electronic states within the lattice. Furthermore, characterization experiments also provided a direct evidence for (de)intercalation reactions that occurred without any major competitive conversion reactions or insertion of protons into the lattice, except for the formation of a surface rock salt phase upon charge. These findings in Mg-ion activity expand opportunities to design Mg spinel oxide materials while highlighting the need to identify the origins of reversibility challenges due to, but not limited to, phase stability, particularly for the charged states, barriers at the interface, electrolyte stabilities, and desolvation phenomena, collectively hindering practical use as cathode materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Optimization of Random Phase Approximation Calculations for Improved Energies of Molecules, Solids, and Surfaces

We present an optimized random phase approximation method (optRPA26) that significantly improves upon conventional RPA through an optimized choice of reference orbitals and energy components, rather than a modification of the RPA correlation functional itself. The method employs an empirically constructed hybrid functional to generate DFT orbitals to evaluate the RPA correlation energy, which is then scaled by a constant. Comprehensive benchmarks across molecules, bulk solids, and surface systems demonstrate that optRPA26 consistently achieves high accuracy, with mean absolute errors of 0.05 eV for W4-11-RE reaction energies, 0.07 eV for cohesive energies, 0.09 eV for metal oxide formation energies, 0.11–0.12 eV for adsorption of small molecules on metals, and 0.06 eV for adsorption on oxides. In addition, optRPA26 correctly captures phase stability in metal oxides and magnetic metals. The optRPA26 approach can be run using standard RPA implementations, highlighting its potential as a general-purpose reference method that can accurately capture covalent, ionic, metallic, and van der Waals bonding in molecules, solids, and interfaces.

Adsorption↗

“Teamwork Makes the Dream Work”: Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions

Crystal structure prediction has been a grand challenge in material science owing to the large configurational space that one must explore. Evolutionary (genetic) algorithms coupled with first principles calculations are commonly used in crystal structure prediction to sample the ground and metastable states of materials based on configurational energies. However, crystal structure predictions at finite temperature (T), pressure (P), and composition (X) require a free-energy-based search that is often computationally expensive and tedious. Here in this paper, we introduce a new machine-learning workflow for structure prediction that is based on a concept inspired by the evolution of human tribes in primitive society. Our tribal genetic algorithm (GA) combines configurational sampling with evolutionary optimization to accurately predict entropically stabilized phases at finite (T, P, X), at a computational cost that is an order of magnitude smaller than that required for a free-energy-based search. In a departure from standard GA techniques, the populations of individuals are divided into multiple tribes based on a bond-order fingerprint, and genetic operations are modified to ensure that cluster configurations are sampled adequately to capture entropic contributions. Team competition introduced into the evolutionary process allows winning teams (representing a better set of individuals) to expand their sizes; this translates into a more expanded search of the phase space allowing us to explore solutions near possible global minimum. Each team explores a specific section of the structural phase space and avoids bias on solutions arising from the use of individual populations in a purely energy-based search. We demonstrate the efficacy of our approach by performing the structural prediction of a representative two-dimensional two-body system as well as Lennard-Jones clusters over a range of temperatures up to its melting point. Our approach outperforms the standard GA approaches and enables structural search under “real nonambient conditions” on both bulk systems and finite-sized clusters.

36 MATERIALS SCIENCE↗

Tailored Synthesis of the Narrowest Zigzag Graphene Nanoribbon Structure by Compressing the Lithium Acetylide under High Temperature

Scientists are searching for the goal-directed methods to synthesize graphene nanoribbons (GNRs) with a particular edge type and width, which determines their electronic transport properties. A series of Li zigzag GNRs (ZGNRs) with different widths were predicted under high pressure with a stoichiometric ratio of Li n +1 C 2 n , which indicates a route to prepare ultranarrow GNRs. Here, with thermodynamics and ab initio Gibbs free-energy calculations by quasi-harmonic approximation, we investigated the phase stabilities of the Li GNR compounds under high pressure and high temperature. We have also identified Li graphenide LiC 2 ( n = ∞) and Li polyacenide Li 3 C 4 (n = 2) experimentally at the predicted pressure and temperature conditions using in situ X-ray diffraction, which can be recognized as the two end members of Li n +1 C 2 n , with the widest and narrowest ZGNR structures. High temperature and the temperature gradient increased the degree of polymerization and facilitated the formation of wider GNR or carbon slices. Furthermore, this suggests that by controlling temperature and pressure, we may get ultranarrow Li ZGNRs composed of a limited number of parallel carbon chains, such as 3- or 4-zigzag GNR, which is ready to be protonated or functionalized to form atomically ordered ZGNRs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

36 MATERIALS SCIENCE↗