Materials Science (cond-mat.mtrl-sci)

Abstract: Thin films of HfO2 doped with 4% La were fabricated on LSMO/STO (100) substrates using pulsed laser deposition. The stability of the ferroelectric orthorhombic phase in the hafnia films was investigated with respect to varying oxygen pressure during deposition. X-ray diffraction and X-ray photoelectron spectroscopy measurements were carried out to analyze the structure and composition of the films and correlated with their ferroelectric properties. Surprisingly, the ferroelectricity of the hafnia films showed a dependence on oxygen pressure during deposition of LSMO bottom electrode as well. The reason for this dependence is discussed in terms of the active role of non-lattice oxygen in the ferroelectric switching of hafnia.

Abstract: The utilization of polarized neutrons is of great importance in scientific disciplines spanning materials science, physics, biology, and chemistry. Polarization analysis offers insights into otherwise unattainable sample information such as magnetic domains and structures, protein crystallography, composition, orientation, ion-diffusion mechanisms, and relative location of molecules in multicomponent biological systems. State-of-the-art multilayer polarizing neutron optics have limitations, particularly low specular reflectivity and polarization at higher scattering vectors/angles, and the requirement of high external magnetic fields to saturate the polarizer magnetization. Here, we show that by incorporating 11B4C into Fe/Si multilayers, amorphization and smooth interfaces can be achieved, yielding higher neutron reflectivity, less diffuse scattering and higher polarization. Magnetic coercivity is eliminated, and magnetic saturation can be reached at low external fields (>2 mT). This approach offers prospects for significant improvement in polarizing neutron optics, enabling; nonintrusive positioning of the polarizer, enhanced flux, increased data accuracy, and further polarizing/analyzing methods at neutron scattering facilities.

Abstract: Multi-principal element alloys (MPEAs) are produced by combining metallic elements in what is a diverse range of proportions. MPEAs reported to date have revealed promising performance due to their exceptional mechanical properties. Training a machine learning (ML) model on known performance data is a reasonable method to rationalise the complexity of composition dependent mechanical properties of MPEAs. This study utilises data from a specifically curated dataset, that contains information regarding six mechanical properties of MPEAs. A parser tool was introduced to convert chemical composition of alloys into the input format of the ML models, and a number of ML models were applied. Finally, Gradio was used to visualise the ML model predictions and to create a user-interactive interface. The ML model presented is an initial primitive model (as it does not factor in aspects such as MPEA production and processing route), however serves as a an initial user tool, whilst also providing a workflow for other researchers.

Abstract: For being used as an electrolyte in All Solid State Batteries (ASSB), a solid electrolyte must possess ionic conductivity comparable to that of conventional liquid electrolytes. To achieve this conductivity range, the series Li$_{6.8-y}$Ge$_{0.05}$La$_3$Zr$_{2-y}$Ta$_y$O$_{12}$ ($y = 0, 0.15, 0.25, 0.35, 0.45$) has been synthesized using solid-state reaction method and studied using various characterization techniques. The highly conducting cubic phase is confirmed from XRD analysis. Structural information was collected using SEM and density measurements. The prepared ceramic sample containing 0.25 Ta, sintered at 1050$^\circ$C for 7.30 hrs shows the maximum ionic conductivity of 6.61 x 10$^{-4}$ S/cm at 25$^\circ$C. The air stability of the same ceramic has also been evaluated after exposure for 5 months. The minimum activation energy associated with the maximum conductivity of 0.25 Ta is 0.25 eV. The DC conductivity measurements were done to confirm the ionic nature of conductivity for all ceramic samples. The stable result of ionic conductivity makes the 0.25 Ta containing ceramic sample a promising candidate for solid electrolytes for ASSB applications.

Abstract: Severe plastic deformation changes the microstructure and properties of steels, which may be favourable for their use in structural components of nuclear reactors. In this study, high-pressure torsion (HPT) was used to refine the grain structure of Eurofer-97, a ferritic/ martensitic steel. Electron microscopy and X-ray diffraction were used to characterise the microstructural changes. Following HPT, the average grain size reduced by a factor of $\sim$ 30, with a marked increase in high-angle grain boundaries. Dislocation density also increased by more than one order of magnitude. The thermal stability of the deformed material was investigated via in-situ annealing during synchrotron X-ray diffraction. This revealed substantial recovery between 450 K - 800 K. Irradiation with 20 MeV Fe-ions to $\sim$ 0.1 dpa caused a 20% reduction in dislocation density compared to the as-deformed material. However, HPT deformation prior to irradiation did not have a significant effect in mitigating the irradiation-induced reductions in thermal diffusivity and surface acoustic wave velocity of the material. These results provide a multi-faceted understanding of the changes in ferritic/martensitic steels due to severe plastic deformation, and how these changes can be used to alter material properties.

Abstract: Boron (B) containing III-nitride materials, such as wurtzite (B,Ga)N alloys, have recently attracted significant interest to tailor the electronic and optical properties of optoelectronic devices operating in the visible and ultraviolet spectral range. However, the growth of high quality samples is challenging and B atom clustering is often observed in (B,Ga)N alloys. To date, fundamental understanding of the impact of such clustering on electronic and optical properties of these alloys is sparse. In this work we employ density functional theory (DFT) in the framework of the meta generalized gradient approximation (modified Becke Johnson (mBJ) functional) to provide insight into this question. We use mBJ DFT calculations, benchmarked against state-of-the-art hybrid functional DFT, on (B,Ga)N alloys in the experimentally relevant B content range of up to 7.4%. Our results reveal that B atom clustering can lead to a strong reduction in the bandgap of such an alloy, in contrast to alloy configurations where B atoms are not forming clusters, thus not sharing nitrogen (N) atoms. We find that the reduction in bandgap is linked mainly to carrier localization effects in the valence band, which stem from local strain and polarization field effects. However, our study also reveals that the alloy microstructure of a B atom cluster plays an important role: B atom chains along the wurtzite c-axis impact the electronic structure far less strongly when compared to a chain formed within the c-plane. This effect is again linked to local polarization field effects and the orbital character of the involved valence states in wurtzite BN and GaN. Overall, our calculations show that controlling the alloy microstructure of (B,Ga)N alloys is of central importance when it comes to utilizing these systems in future optoelectronic devices with improved efficiencies.

Abstract: Finite-temperature structures of Cu, Ag, and Au metal nanoclusters are calculated in the entire temperature range from 0 K to melting using a computational methodology that we proposed recently [Settem \emph{et al.}, Nanoscale, 2022, 14, 939]. In this method, Harmonic Superposition Approximation (HSA) and Parallel Tempering Molecular Dynamics (PTMD) are combined in a complementary manner. HSA is accurate at low temperatures and fails at higher temperatures. PTMD, on the other hand, effectively samples the high temperature region and melting. This method is used to study the size- and system-dependent competition between various structural motifs of Cu, Ag, and Au nanoclusters in the size range 1 to 2 nm. Results show that there are mainly three types of structural changes in metal nanoclusters depending on whether a solid-solid transformation occurs. In the first type, global minimum is the dominant motif in the entire temperature range. In contrast, when a solid-solid transformation occurs, the global minimum transforms either completely to a different motif or partially resulting in a co-existence of multiple motifs. Finally, nanocluster structures are analyzed to highlight the system-specific differences across the three metals.

Abstract: Achieving superconductivity at room temperature can lead to substantially advancements in industry and technology. Recently, a compound known as Cu-doped lead-apatite Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O ($0.9 < x < 1.1$), referred to as LK-99, has been reported to exhibit unusual electrical and magnetic behaviors that appear to resemble a "superconducting transition" above room temperature. We collected compound samples containing the nominal Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O phase, which were synthesized by three independent groups, and studied their chemical, magnetic, and electrical properties at the microscale to overcome difficulties in bulk measurements. Through the utilization of optical, scanning electron, atomic force, and scanning diamond nitrogen-vacancy microscopy techniques, we are able to establish a link between local magnetic properties and specific microscale chemical phases. Our findings indicate that while the Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O phase seems to have a mixed magnetism contribution, a significant fraction of the diamagnetic response can be attributed to Cu-rich regions (e.g., Cu$_2$S from chemical reaction). Additionally, our micro-region electrical measurements reveal a phenomenon of current path jumping and a change in resistance states of Cu$_2$S. This provides a potential explanation for the electrical behavior observed in compounds related to Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O.

Abstract: Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared-errors between 98-119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies.By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importances, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. This framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

Abstract: Antiperovskites are a rich family of compounds with applications in battery cathodes, superconductors, solid-state lighting, and catalysis. Recently, a novel series of antimonide phosphide antiperovskites (A$_3$SbP, where A = Ca, Sr, Ba) were proposed as candidate photovoltaic absorbers due to their ideal band gaps, small effective masses and strong optical absorption. In this work, we explore this series of compounds in more detail using relativistic hybrid density functional theory. We reveal that the proposed cubic structures are dynamically unstable and instead identify a tilted orthorhombic Pnma phase as the ground state. Tilting is shown to induce charge localisation that widens the band gap and increases the effective masses. Despite this, we demonstrate that the predicted maximum photovoltaic efficiencies remain high (24-31% for 200 nm thin films) by bringing the band gaps into the ideal range for a solar absorber. Finally, we assess the band alignment of the series and suggest hole and electron contact materials for efficient photovoltaic devices.

Abstract: X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes. However, rapid, automated and reliable analysis method of XRD data matching the incoming data rate remains a major challenge. To address these issues, we present CrystalShift, an efficient algorithm for probabilistic XRD phase labeling that employs symmetry-constrained pseudo-refinement optimization, best-first tree search, and Bayesian model comparison to estimate probabilities for phase combinations without requiring phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase-mapping, CrystalShift offers quantitative insights into materials' structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery.

Abstract: The present study demonstrates the effectiveness of incorporating oxygen atoms into the Sb2Te3 thin film, leading to an improved power factor and reduction in thermal conductivity. Based on the experimental evidence, it can be inferred that oxygen-related impurities preferentially wet the grain boundary (GB) and introduce a double Schottky barrier at the GB interface, promoting energy-dependent carrier scattering, ultimately leading to a rise in the Seebeck coefficient. Additionally, the introduction of chemisorbed oxygen creates a high mobility state within the valence band of Sb2Te3, as corroborated by theoretical calculations, resulting in a significantly increased electrical mobility. These factors collectively contribute to improved thermoelectric performance. A set of Scanning probe (SPM) techniques is used to experimentally confirm the alteration in charge transport resulting from the oxygen-passivated grain boundary. Additionally, Scanning Thermal Microscopy (SThM) is employed to observe the spatial variations of thermal conductivity at the nanoscale regime. This study presents a comprehensive microscopic investigation of the impact of oxygen on the phonon and charge carrier transport characteristics of Sb2Te3 thermoelectric materials and indicates that incorporating oxygen may represent a feasible approach to improve the thermoelectric efficiency of these materials.