Andrej Singer

Composite electrodes combine cathode active materials with solid electrolytes and are essential for all-solid-state batteries. Nevertheless, co-sintering these components introduces mechanical stresses that can lead to microstructural degradation. Understanding how defects evolve at the grain level during thermal processing is critical for designing mechanically robust composite electrodes. Here, we use in situ Bragg coherent diffractive imaging (BCDI) to investigate the microstructural stability of two key materials: the cathode LiNi0.8Mn0.1Co0.1O2 (NMC811) and the solid electrolyte Al-doped cubic Li7La3Zr2O12 (c-LLZO). We track individual crystalline grains during heating and image three-dimensional displacement fields to visualize embedded dislocations. NMC811 exhibits remarkable stability, with stationary dislocation networks and minimal changes in diffraction patterns up to 400 °C. In contrast, c-LLZO undergoes significant microstructural changes beginning at 200 °C, including strain relaxation and dislocation migration. These findings reveal that c-LLZO exhibits dislocation mobility at moderate temperatures that may enable it to accommodate thermal stresses when co-sintered with more rigid oxide cathodes, while the stability of NMC811 ensures structural integrity of the active material. This work demonstrates the value of single-grain characterization for understanding mechanical compatibility in composite electrode design.

Tantalum dioxide (TaO2) is a metastable tantalum compound. Here, we report the epitaxial stabilization of TaO2 on Al2O3 (11̄02) (r-plane sapphire) substrates using suboxide molecular-beam epitaxy and thermal laser epitaxy, demonstrating single-oriented, monodomain growth of anisotropically strained thin films. Microstructural investigation is performed using synchrotron x-ray diffraction and scanning transmission electron microscopy. The tetravalent oxidation state of tantalum is confirmed using x-ray absorption and photoemission spectroscopy as well as electron energy-loss spectroscopy. Optical properties are investigated via spectroscopic ellipsometry and reveal a 0.3 eV Mott gap of the tantalum 5d electrons. Density-functional theory and group theoretical arguments are used to evaluate the limited stability of the rutile phase and reveal the potential to unlock a hidden metal–insulator transition concomitant with a structural phase transition to a distorted rutile phase, akin to NbO2. Our study expands the understanding of tantalum oxides and paves the way for their integration into next-generation electronic and photonic devices.

Electrocatalysts, suitable for at-scale applications, must integrate high activity, long-term durability, and cost-effectiveness, with the latter presenting a major challenge for platinum-group-metal (PGM) electrocatalysts. Alkaline systems enable the use of cost-effective transition metals. However, developing non-PGM electrocatalysts that can catalyze the high-potential oxygen evolution reaction (OER) with high stability remains challenging. Here, we report on metallic Ni catalysts with a Co- and Fe-rich shell (Ni@FeCo), which, during OER operation, transforms into an active oxide shell. Anion exchange membrane water electrolyzers (AEMWEs) employing Ni@FeCo catalysts exhibited excellent performance, reaching 10 A cm^–2 at 2.18 V. Operando X-ray characterizations revealed the oxidation of Co and Fe, while Ni remained mostly metallic across all AEMWE operating potentials. Structural characterization, by scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS), revealed that the active Ni@FeCo catalysts feature a metallic Ni core and a Ni–Fe–Co spinel oxide shell. This metal-core/oxide-shell heterostructure provides efficient electron transport and OER activity while remaining stable under AEMWE operating potentials. The Ni@FeCo catalysts operated stably for over 1700 h in an AEMWE, highlighting their potential for practical applications and demonstrating a viable strategy for designing nonprecious metal-based electrocatalysts.

Symmetry-lowering structural phase transitions result in multiple degenerate structures whose coexistence is determined by macroscopic strain compatibility. In quantum materials, these structural transformations often couple to electronic degrees of freedom, yet how the structural arrangements influence electronic phase transitions remains poorly understood. By analyzing hundreds of diffraction peaks from X-ray reciprocal space mapping, we determine the lattice basis vectors and mutual orientations of all coexisting phases in epitaxial V2O3 thin films after a symmetry-lowering transformation coincident with a metal-insulator transition. We identify the orientations of interfaces between all coexisting structures using the theory of martensitic phase transformations and find that the low temperature structure comprises finely tuned layered mixtures of alternating twin variants, akin to metal alloys. By comparing films grown on various substrate orientations, we show that the metal-insulator transition temperature increases monotonically with the degree to which these layered mixtures satisfy macroscopic strain compatibility imposed by the substrate.

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

We investigate the ultrafast structural dynamics of LaAlO_{3} thin films driven by short mid-infrared laser pulses at 20 THz. Time-resolved x-ray diffraction reveals an immediate lattice expansion and an acoustic breathing mode of the film. First-principles theory and a spring-mass model identify the direct coupling between coherently driven infrared-active phonons and strain as the underlying mechanism. Time-resolved optical birefringence measurements confirm that the amplitude of this acoustic mode scales linearly with the pump fluence, in agreement with theory. Furthermore, time-resolved x-ray diffuse scattering indicates that THz excitation enhances crystallinity by inducing a nonthermal increase in structural symmetry originating from preexisting defects. These findings highlight the potential of a multimodal approach-combining time-resolved x-ray and optical measurements and first-principles theory-to elucidate and control structural dynamics in nanoscale materials.

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample's local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

The discovery of superconductivity in La₃Ni₂O₇ under pressure has motivated the investigation of a parent spin density wave (SDW) state, which could provide the underlying pairing interaction. Here, we employ resonant soft x-ray scattering and polarimetry on thin films of bilayer La₃Ni₂O₇ to determine that the magnetic structure of the SDW forms unidirectional diagonal spin stripes with moments lying within the NiO₂ plane and perpendicular to Q_SDW, but without evidence of the strong charge disproportionation typically associated with other nickelates. These stripes form anisotropic domains with shorter correlation lengths perpendicular versus parallel to Q_SDW, revealing nanoscale rotational and translational symmetry breaking analogous to the cuprate and Fe-based superconductors, with possible Bloch-like antiferromagnetic domain walls separating orthogonal domains.

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor—corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series; however, traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.