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At least 73 records · Page 4

Realizing Complete Solid-Solution Reaction in High Sodium Content P2-Type Cathode for High-Performance Sodium-Ion Batteries

P2-type layered oxides suffer from an ordered Na + /vacancy arrangement and P2→O2/OP4 phase transitions, leading them to exhibit multiple voltage plateaus upon Na + extraction/insertion. The deficient sodium in the P2-type cathode easily induces the bad structural stability at deep desodiation states and limited reversible capacity during Na + de/insertion. These drawbacks cause poor rate capability and fast capacity decay in most P2-type layered oxides. In this work, to address these challenges, a novel high sodium content (0.85) and plateau-free P2-type cathode-Na 0.85 Li 0.12 Ni 0.22 Mn 0.66 O 2 (P2-NLNMO) was developed. The complete solid-solution reaction over a wide voltage range ensures both fast Na + mobility (10 -11 to 10 -10 cm 2 s -1 ) and small volume variation (1.7%). Furthermore, the high sodium content P2-NLNMO exhibits a higher reversible capacity of 123.4 mAhg -1 , superior rate capability of 79.3 mAhg -1 at 20 C, and 85.4% capacity retention after 500 cycles at 5 C. The sufficient Na and complete solid-solution reaction are critical to realizing high-performance P2-type cathodes for sodium-ion batteries.

25 ENERGY STORAGE↗

Accelerated kinetic Monte Carlo: A case study; vacancy and dumbbell interstitial diffusion traps in concentrated solid solution alloys

Vacancy and self-interstitial atomic diffusion coefficients in concentrated solid solution alloys can have a non-monotonic concentration dependence. Here, the kinetics of monovacancies and $\langle$100$\rangle$ dumbbell interstitials in Ni–Fe alloys are assessed using lattice kinetic Monte Carlo (kMC). The non-monotonicity is associated with superbasins, which impels using accelerated kMC methods. Detailed implementation prescriptions for first passage time analysis kMC (FPTA-kMC), mean rate method kMC (MRM-kMC), and accelerated superbasin kMC (AS-kMC) are given. The accelerated methods are benchmarked in the context of diffusion coefficient calculations. The benchmarks indicate that MRM-kMC underestimates diffusion coefficients, while AS-kMC overestimates them. In this application, MRM-kMC and AS-kMC are computationally more efficient than the more accurate FPTA-kMC. Our calculations indicate that composition dependence of migration energies is at the origin of the vacancy’s non-monotonic behavior. In contrast, the difference between formation energies of Ni–Ni, Ni–Fe, and Fe–Fe dumbbell interstitials is at the origin of their non-monotonic diffusion behavior. Additionally, the migration barrier crossover composition—based on the situation where Ni or Fe atom jumps have lower energy barrier than the other one—is introduced. KMC simulations indicate that the interplay between composition dependent crossover of migration energy and geometrical site percolation explains the non-monotonic concentration-dependence of atomic diffusion coefficients.

36 MATERIALS SCIENCE↗

Effect of surface steps on chemical ordering in the subsurface of Cu(Au) solid solutions

Atomic steps are typically assumed to only influence surface phenomena of solids. In contrast, in this paper we show, using in-situ atomic-resolution electron microscopy observations and atomistic modeling, the pronounced effect from surface stepsin inducing compositional and structural evolution in the subsurface of a Cu(Au) solid solution. We find that Au surface segregation results in a stacked sequence of Cu-Au ordered phases in the subsurface. The presence of a monatomic step at the surface induces the formation of an anti-phase boundary that extends from the surface step to deeper layers by maintaining the same composition profile associated with each terrace. The bunching of surface steps induces chemical disordering of the Cu-Au ordered phases in the subsurface region of the bunched steps. These results demonstrate the instant propagation of surface dynamics of atomic steps into the subsurface region and can find broader applicability in utilizing surface defects to tune the composition and structure in the subsurface of alloys.

36 MATERIALS SCIENCE↗

High Voltage Mg-Ion Battery Cathode via a Solid Solution Cr–Mn Spinel Oxide

We discuss how lattice Mg 2+ in a tailored solid solution spinel, MgCrMnO 4 , is electrochemically utilized at high Mn-redox potentials in a nonaqueous electrolyte. Complementary evidence from experimental and theoretical analyses supports bulk Mg 2+ (de)intercalation throughout the designed oxide frame where strong electrostatic interaction between Mg 2+ and O 2- exists. Mg/Mn antisite inversion in the spinel is lowered to similar to 10% via postannealing at 350 degrees C to further improve Mg 2+ mobility. Spinel lattice is preserved upon removal of Mg 2+ without any phase transformations, denoting structural stability at the charged state at a high potential similar to 3.0 V (vs Mg/Mel. Clear remagnesiation upon first discharge, harvesting up to similar to 180 Wh/kg at 60 degrees C is shown. In the remagnesiated state, insertion of Mg 2+ into interstitial sites in the spinel is detected, possibly resulting in partial reversibility which needs to be addressed for structural stability. The observations constitute a first clear path to the development of a practical high voltage Mg-ion cathode using a spinel oxide.

25 ENERGY STORAGE↗

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

Density functional theory calculations↗

From suppressed void growth to significant void swelling in NiCoFeCr complex concentrated solid-solution alloy

Void swelling can result in dimensional instability and undermine the safe operation of nuclear reactors. Current strategies to inhibit void swelling mainly focus on enhancing defect absorption and recombination by introducing high-density defect sinks. Complex concentrated solid-solution alloys (CSAs), including high-entropy alloys, can withstand severe radiation damage due to their inherent chemical complexity without interfaces. However, the underlying mechanisms for void suppression in CSAs are far from clear. In this research, we studied the void evolution with respect to irradiation depths, doses, and temperatures in equiatomic NiCoFeCr under 3 MeV Ni ion irradiations. At relatively low doses (16 and 54 displacements per atom, dpa), voids form mainly outside of the ion-damaged region, and void formation in the peak damage region is suppressed, leading to negligible swelling. However, with further increase of dose (86 up to 250 dpa), significant void growth occurs in the peak damage region and extended dislocation lines dominate instead of short dislocation lines and loops formed at lower doses. From 500 to 700 °C, the dislocation density decreases while dislocations grow. Although the overall void swelling increases dramatically from 500 to 580 °C at 54 dpa, void growth in the peak damage region is still suppressed. The transition from suppressed void growth to significant void swelling is attributed to dislocation evolution and local chemical inhomogeneity (enrichment of Fe/Cr in the matrix) at higher doses. Our study shows that controlling element diffusion and defect evolution through tuning chemical complexity can further enhance the swelling resistance of CSAs.

36 MATERIALS SCIENCE↗

Phase stability, mechanical properties, and ion irradiation effects in face-centered cubic CrFeMnNi compositionally complex solid-solution alloys at high temperatures

We report two CrFeMnNi face-centered cubic complex concentrated solid-solution alloys (CSA) have been evaluated for phase stability, mechanical properties, and radiation damage effects from heavy ions. Cr 18 Fe 27 Mn 27 Ni 28 and Cr 15 Fe 35 Mn 15 Ni 35 were predicted by thermodynamic calculations to phase separate and maintain a single phase at 700 °C, respectively. Aging experiments at this temperature confirmed varying degrees of precipitation of a body-centered cubic phase in both Cr 18 Fe 27 Mn 27 Ni 28 and Cr 15 Fe 35 Mn 15 Ni 35 . The alloys showed promising strength in tensile deformation at room temperature, with yield strengths of 155 MPa and 151 MPa for Cr 18 Fe 27 Mn 27 Ni 28 and Cr 15 Fe3 5 Mn 15 Ni 35 , respectively. At 500 °C, the yield strength of Cr 18 Fe 27 Mn 27 Ni 28 fell to 93 MPa, and to 100 MPa in Cr 15 Fe3 5 Mn 15 Ni 35 . Unlike Cr 18 Fe 27 Mn 27 Ni 28 , Cr 15 Fe 35 Mn 15 Ni 35 gained some ductility at 500 °C compared to room temperature. The two CSAs were irradiated to 75 dpa at 500 °C in the plateau region of the displacement curve using 3.7 MeV Ni 2+ ions, alongside model alloy 709 as a reference. Irradiation results produced similar densities and sizes of dislocations loops in the two CSAs compared to the reference. However, while large voids form in the plateau region of Cr 18 Fe 27 Mn 27 Ni 28 , small voids form just beyond the displacement peak of Cr 15 Fe 35 Mn 15 Ni 35 . Atom probe tomography and energy dispersive X-ray spectroscopy-equipped scanning transmission electron microscopes were used to characterize the alloys for changes in chemical distribution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Melting and crystallization peculiarities in Cd0.50Mn0.50Te solid solutions

We investigated melting and crystallization processes of Cd0.50Mn0.50Te. Using differential thermal analysis (DTA) we studied the solid-liquid equilibrium temperature range, the temperature dependence of the volume fraction of the solid phase in the melt, and crystallization peculiarities. We observed that changing a dwell temperature from 1346 to 1354 K results in an increase of the crystallization temperature of the Cd0.50Mn0.50Te melt. Since the melt is in a semi-liquid state, its crystallization occurs over a solid-phase domain. It was found that a slightly overheated Cd0.50Mn0.50Te melt (up to 10 K) crystallizes more slowly than more overheated melts, and that the Cd0.50Mn0.50Te solid solutions are in a semi-liquid state in the temperature range of 1338 – 1354 K.

Kopach, Oleh↗

The dynamic evolution of swelling in nickel concentrated solid solution alloys through in situ property monitoring

Defects and microstructural features spanning the atomic level to the microscale play deterministic roles in the expressed properties of materials. Yet studies of material evolution in response to environmental stimuli most often correlate resulting performance with one dominant microstructural feature only. In this work, the dynamic evolution of swelling in a series of Ni-based concentrated solid solution alloys under high-temperature irradiation exposure is observed using continuous, in situ measurements of thermoelastic properties in bulk specimens. Unlike traditional evaluation techniques which account only for volumetric porosity identified using electron microscopy, direct property evaluation provides an integrated response across all defect length scales. In particular, the evolution in elastic properties during swelling is found to depend significantly on the entire size spectrum of defects, from the nano- to meso-scales, some of which are not resolvable in imaging. Observed changes in thermal transport properties depend sensitively on the partitioning of electronic and lattice thermal conductivity. This emerging class of in situ experiments, which directly measure integrated performance in relevant conditions, provides unique insight into material dynamics otherwise unavailable using traditional methods.

36 MATERIALS SCIENCE↗

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗

Molecular dynamics simulation of vacancy and void effects on strain-induced martensitic transformations in Fe-50 at.% Ni model concentrated solid solution alloy

Strain-induced martensitic transformations can improve the strength and ductility of some face centered cubic (FCC) metals and alloys. Irradiation-induced defects such as vacancies, dislocation loops, and voids activate martensitic transformations over a wider range of mechanical loading conditions than in pristine material. Furthermore, the mechanisms underlying irradiation-enabled martensite transformations remain unclear. In this work, we use molecular dynamics simulations to study the effect of vacancies and voids on strain-induced martensitic transformations in a model concentrated solid solution alloy Fe-50 at.% Ni. It has been found that single vacancies have no resolvable effect on the transformation because they reduce the stacking fault energy by a relatively insignificant margin and do so only if the vacancy is located on the stacking fault plane. Voids, however, activate the martensite transformation through shear strain accumulation around the void due to dislocation pileup. The larger the void, the more pronounced this effect.

36 MATERIALS SCIENCE↗

Correlating electronic properties with M-site composition in solid solution Ti y Nb 2- y CT x MXenes

High electrical conductivity is desired in MXene films for applications such as electromagnetic interference shielding, antennas, and electrodes for electrochemical energy storage and conversion applications. Due to the acid etching-based synthesis method, it is challenging to deconvolute the relative importance that factors such as chemical composition and flake size contribute to resistivity. To understand the intrinsic and extrinsic contributions to the macroscopic electronic transport properties, a systematic study controlling compositional and structural parameters was conducted with eight solid solutions in the Ti y Nb 2-y CT x system. Here, in particular, we investigated the different roles played by metal (M)-site composition, flake size, and d-spacing on macroscopic transport. Hard x-ray photoemission spectroscopy and spectroscopic ellipsometry revealed changes to electronic structure induced by the M-site alloying. Consistent with the spectroscopic results, the low- and room-temperature conductivities and effective carrier mobility are correlated with the Ti content, while the impact of flake size and d-spacing is most prominent in low-temperature transport. The results provide guidance for designing and engineering MXenes with a wide range of conductivities.

36 MATERIALS SCIENCE↗

Linker-Based Bandgap Tuning in Conductive MOF Solid Solutions

Herein, the synthesis of Cu 3 (HAB) x (TATHB) 2-x (HAB: hexaaminobenzene, TATHB: triaminotrihydroxybenzene) is reported. Synthetic improvement of Cu 3 (TATHB) 2 leads to a more crystalline framework with higher electrical conductivity value than previously reported. The improved crystallinity and analogous structure between TATHB and HAB enable the synthesis of Cu 3 (HAB) x (TATHB) 2-x with ligand compositions precisely controlled by precursor ratios. The electrical conductivity is tuned from 4.2 × 10 -8 to 2.9 × 10 -5 S cm -1 by simply increasing the nitrogen content in the crystal lattice. Furthermore, computational calculation supports that the solid solution facilitates the band structure tuning. In conclusion, it is envisioned that the findings not only shed light on the ligand-dependent structure–property relationship but create new prospects in synthesizing multicomponent electrically conductive metal-organic frameworks (MOFs) for tailoring optoelectronic device applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Whole-Voltage-Range Solid-Solution Reaction in Layered Oxide Cathode of Sodium-Ion Batteries

Layered manganese-based oxides (LMOs) are promising cathode materials for sodium-ion batteries (SIBs) due to their versatile structures. However, the Jahn–Teller effect of Mn 3+ induces severe distortion of MnO 6 octahedra, and the resultant low symmetry is responsible for the gliding of MnO 2 layers and then inferior multiple-phase transitions upon Na + extraction/insertion. Here, hexagonal P2-Na 0.643 Li 0.078 Mn 0.827 Ti 0.095 O 2 is synthesized through the incorporation of Li and Ti into the distorted orthorhombic P'2-Na 0.67 MnO 2 to function as a phase-transition-free oxide cathode. Further, it is revealed that Li in both the transition-metal and Na layers enhances the covalency of Mn–O bonds and allows degeneracy of Mn 3d e g orbitals to favor the formation of hexagonal phase, and the high strength of Ti–O bonds reduces the electrostatic interaction between Na and O for suppressed Na + /vacancy rearrangements. These collectively lead to a whole-voltage-range solid-solution reaction between 1.8 and 4.3 V with a small volume variation of 1.49%. This rewards its excellent cycling stability (capacity retention of 90% after 500 cycles) and rate capability (89 mAh g -1 at 2000 mA g -1 ).

25 ENERGY STORAGE↗

A Principle for Highly Active Metal Oxide Catalysts via NaCl-Based Solid Solution

Toward the preparation of industrial metal oxide catalysts, sacrificial organic templates, excessive solvents, complex impregnation, and drying steps are generally required. Here, we report a versatile rule for the simple synthesis of highly porous metal oxides with well-dispersed noble metal species. Porous metal oxides (Co 3 O 4 , Fe x O y , and Cr 2 O 3 ) are obtained with some surface areas (e.g., Cr 2 O3: 224 m 2 ·g -1 ) beyond the record value. Surprisingly, small noble metal nanoparticles (e.g., Pd: 3.1 and Pt: 3.2 nm) could be incorporated by this solid-state process simultaneously. Corresponding Rh-Co 3 O 4 , Pd-Fe x O y , and Pt-Cr 2 O 3 exhibit excellent performance: CH 4 combustion (T90 = ~360°C and thermal stability: >100 h at 680°C), hydrogenation of nitrobenzene and derivatives (turnover number [TON] = 2.49 × 104, 300 mmol per run), and reversed water gas shift (RWGS) reaction (44% CO 2 conversion with ~98% CO selectivity and thermal stability: >100 h at 500°C), respectively. Therefore, current principle via a NaCl-based solid solution could provide a solid-state, fast, and efficient route for processing metal oxide catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Thermodynamic Modeling of Complex Solid Solutions in the Lu-H-N System via Graph Neural Network Accelerated Monte Carlo Simulations

Metal hydrides are important across diverse applications, such as hydrogen storage, batteries, gas sensors, nuclear reactions, and high-temperature superconductivity. Previous computational studies of metal hydrides under extreme pressures, e.g., 𝑂⁡(10 2 ) ⁢GPa, usually treat them as stoichiometric compounds without considering interstitial lattice disorder. As pressures become more moderate in the 𝑂⁡(10 0 ) ⁢GPa and below range, hydrogen disorder at interstitial lattice sites becomes prominent, e.g., in the N-doped Lu hydride that was recently claimed superconducting near 1 GPa. Further adding compositional complexity from alloying and/or multielement interstitial occupation makes elucidating pressure- and temperature-dependent observables intractable by first-principles calculations alone. We therefore propose a lattice graph neural-network surrogate modeling approach to predict configuration- and pressure-dependent equation-of-state properties. Their efficiency permits Monte Carlo simulations to calculate Gibbs energies and pressure-dependent phase diagrams, thereby revealing insights into the synthesis conditions required for achieving desired phase equilibria. We demonstrate this concept for the compositionally complex cubic Lu(H,N,Va) 3 system where three constituents (hydrogen, nitrogen and vacancy) have disordered multielement interstitial occupancies and insights into pressure-dependent phase equilibria are critically needed, e.g., N-doping levels can significantly lower dehydrogenation temperatures and provide a new strategy to optimize hydrogen-storage alloys. This work can improve the thermodynamic understanding of the Lu-H-N system and help rational synthesis of N-doped Lu hydrides, but more generally demonstrates an efficient approach to model pressure-dependent thermodynamics of multicomponent solid solutions.

Monte Carlo methods↗