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Neutron scattering and simulation unveil amorphous clathrate hydrate structures. THF·17(H 2 O) yields three amorphs under high pressure and low temperature, resembling amorphous ice. THF and water's local structure is shaped by guest–host interactions.
A methodology for simulating the growth of long through cracks in the skin of pressurized aircraft fuselage structures is described. Crack trajectories are allowed to be arbitrary and are computed as part of the simulation. The interaction between the mechanical loads acting on the superstructure and the local structural response near the crack tips is accounted for by employing a hierarchical modeling strategy. The structural response for each cracked configuration is obtained using a geometrically nonlinear shell finite element analysis procedure. Four stress intensity factors, two for membrane behavior and two for bending using Kirchhoff plate theory, are computed using an extension of the modified crack closure integral method. Crack trajectories are determined by applying the maximum tangential stress criterion. Crack growth results in localized mesh deletion, and the deletion regions are remeshed automatically using a newly developed all-quadrilateral meshing algorithm. The effectiveness of the methodology and its applicability to performing practical analyses of realistic structures is demonstrated by simulating curvilinear crack growth in a fuselage panel that is representative of a typical narrow-body aircraft. The predicted crack trajectory and fatigue life compare well with measurements of these same quantities from a full-scale pressurized panel test.
Mn-redox-based oxides and oxyfluorides are considered the most promising earth-abundant high-energy cathode materials for next-generation lithium-ion batteries. While high capacities are obtained in high-Mn content cathodes such as Li- and Mn-rich layered and spinel-type materials, local structure changes and structural distortions ( often lead to voltage fade, capacity decay, and impedance rise, resulting in unacceptable electrochemical performance upon cycling. In the present study, structural transformations that exploit the high capacity of Mn-rich oxyfluorides while enabling stable cycling, in stark contrast to commonly observed structural changes that result in rapid performance degradation, are reported. Here, it is shown that upon cycling of a cation-disordered rocksalt (DRX) cathode (Li 1.1 Mn 0.8 Ti 0.1 O 1.9 F 0.1 , an ultrahigh capacity of ≈320 mAh g –1 (energy density of ≈900 Wh kg –1 ) can be obtained through dynamic structural rearrangements upon cycling , along with a unique voltage profile evolution and capacity rise. At high voltage, the presence of Mn 4+ and Li + vacancies promotes local cation ordering, leading to the formation of domains of a “δ phase” within the disordered framework. On deep discharge, Mn 4+ reduction, along with Li + insertion transform the structure to a partially ordered DRX phase with a β'-LiFeO 2 -type arrangement. At the nanoscale, domains of the in situ formed phases are randomly oriented, allowing highly reversible structural changes and stable electrochemical cycling. These new insights not only help explain the superior electrochemical performance of high-Mn DRXbut also provide guidance for the future development of Mn-based, high-energy density oxide, and oxyfluoride cathode materials.
Understanding and controlling protein motion at atomic resolution is a hallmark challenge for structural biologists and protein engineers because conformational dynamics are essential for complex functions such as enzyme catalysis and allosteric regulation. Time-resolved crystallography offers a window into protein motions, yet without a universal perturbation to initiate conformational changes the method has been limited in scope. Here we couple a solvent-based temperature jump with time-resolved crystallography to visualize structural motions in lysozyme, a dynamic enzyme. We observed widespread atomic vibrations on the nanosecond timescale, which evolve on the submillisecond timescale into localized structural fluctuations that are coupled to the active site. An orthogonal perturbation to the enzyme, inhibitor binding, altered these dynamics by blocking key motions that allow energy to dissipate from vibrations into functional movements linked to the catalytic cycle. Because temperature jump is a universal method for perturbing molecular motion, the method demonstrated here is broadly applicable for studying protein dynamics.
The vibration of an experimental flexible space truss is controlled with internal control forces produced by several proof mass actuators. Four candidate control law strategies are evaluated in terms of performance and robustness. These control laws are experimentally implemented on a quasi free-free planar truss. Sensor and actuator dynamics are included in the model such that the final closed loop is self-equilibrated. The first two control laws considered are based on direct output feedback and consist of tuning the actuator feedback gains to the lowest mode intended to receive damping. The first method feeds back only the position and velocity of the proof mass relative to the structure; this results in a traditional vibration absorber. The second method includes the same feedback paths as the first plus feedback of the local structural velocity. The third law is designed with robust H infinity control theory. The fourth strategy is an active implementation of a viscous damper, where the actuator is configured to provide a bending moment at two points on the structure. The vibration control system is then evaluated in terms of how it would benefit the space structure's position control system.
The YNiO 3 nickelate is a paradigm d-electron oxide that manifests the intriguing temperature-mediated sequence of three phases transitions from (i) magnetically ordered insulator to (ii) paramagnetic (PM) insulator and then to (iii) PM metal. Such phenomena raised the question of the nature of the association of magnetism and structural symmetry breaking with the appearance in (i) and (ii) and disappearance in (iii) of insulating band gaps. It is demonstrated here that first-principles mean-field–like density-functional theory (DFT), driven by molecular dynamics temperature evolution, can describe not only the origin of the magnetically long-range ordered insulating phase (i), but also the creation of an insulating paramagnet (ii) that lacks spin- long-range order, and of a metallic paramagnet (iii) as temperature rises. This approach provides the patterns of structural and magnetic symmetry breaking at different temperatures, in parallel with band gaps obtained when the evolving geometries are used as input to DFT electronic band-structure calculations. This disentangles the complex interplay among spin, charge, and orbital degrees of freedom. Analysis shows that the success in describing the rise and fall of the insulating band gaps along the phase transition sequence is enabled by allowing sufficient flexibility in describing diverse local structural and magnetic motifs as input to DFT. This entails the use of sufficiently large supercells that allow expressing structural disproportionation of octahedra, as well as a description of PM phases as a distribution of local magnetic moments (rather than using a single averaged moment). It appears that the historic dismissal of mean-field–like DFT as being unable to describe such Mott-like transitions was premature, as it was based on consideration of averaged crystallographic unit cells, a description that washes out local symmetry-breaking motifs. The magnetically ordered insulating YNiO 3 phase (i) and the PM insulating phase (ii) result in DFT from allowing symmetry breaking, evident already by considering the athermal internal energy. In contrast, the PM metallic phase (iii) is formed thermally by smearing out thus weakening symmetry breaking. Analysis of snapshots of the different forms of structural vs magnetic symmetry breaking shows that only the loss of the polymorphous distribution of magnetic moments existing in (ii) causes the fall of the band gap, resulting in the metallic state in (iii). Finally, the interesting conclusion is that such a description of the rise [in phases (i) and (ii)] and fall [in phase (iii)] of the insulating gap does not rely on the traditional Mott-like strong correlation understanding, but on breaking and remaking of magnetic and structural symmetries reflected in energy lowering.
Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.
Efficient photovoltaic cells based on thin films of solution-processed 2D Ruddlesden–Popper hybrid perovskites (RPPs) represent an exciting breakthrough due to their enhanced tunability and chemical stability relative to those fabricated from 3D phases. However, reports of efficient charge separation and current collection are in apparent contradiction with the well-known enhancement of the exciton binding energy in multilayered halide perovskites, which should lower the device’s internal quantum efficiency and voltage. This controversy has led to various proposals for the electronic and physical structure of RPP thin films, including phase inhomogeneity as the driving force for exciton dissociation and transport. We address this apparent paradox in high-quality hot-cast RPP films by correlating ultrafast transient absorption spectroscopy with X-ray scattering measurements. We show that a hot-casting fabrication method produces highly phase pure n = 3 (BA) 2 (MA) n-1 Pb n I 3n+1 RPP structures. The high-phase purity and large grain sizes allow us to observe vertical transport of excitons via a diffusive process and allow us to determine that charge separation is primarily driven by dissociation at surface localized subgap electronic states. Furthermore, we analyze the differential absorption kinetics in films of varying thickness to directly determine that the excitonic diffusion constant is ~0.18 cm 2 s –1 . We propose that a surface localized structural distortion, observed using surface selective grazing incidence X-ray scattering measurements, is responsible for the creation of the surface localized defect states. We find that the density and spatial distribution of these defect states is a function of preparation conditions.
Ternary transition metal dichalcogenides (TMDs) provide a versatile platform to explore novel electronic and magnetic ground states via compositional substitution and local structural modulations. Using a combination of scanning tunneling microscopy and spectroscopy (STM/S), magnetic property measurements, and density functional theory (DFT) calculations, the emergence of local magnetic moments driven by the clustering of Ta atoms in ternary TaWSe 2 single crystals is analyzed. STM topography reveals triangular clusters of Ta atoms embedded within W-rich regions of TaWSe 2 . These clusters exhibit a consistent shape and an orderly arrangement throughout the surfaces. DFT calculations show that these Ta clusters induce local strain, giving rise to localized magnetic moments. The magnetic behavior is further corroborated by temperature-dependent magnetization measurements, which exhibit a magnetic anomaly at ∼50 K. In conclusion, this study offers a pathway to engineer magnetism in TMD systems with potential applications in spintronic and quantum materials.
Abstract During solid‐state calcination, with increasing temperature, materials undergo complex phase transitions with heterogeneous solid‐state reactions and mass transport. Precise control of the calcination chemistry is therefore crucial for synthesizing state‐of‐the‐art Ni‐rich layered oxides (LiNi 1‐x‐y Co x Mn y O 2 , NRNCM) as cathode materials for lithium‐ion batteries. Although the battery performance depends on the chemical heterogeneity during NRNCM calcination, it has not yet been elucidated. Herein, through synchrotron‐based X‐ray, mass spectrometry microscopy, and structural analyses, it is revealed that the temperature‐dependent reaction kinetics, the diffusivity of solid‐state lithium sources, and the ambient oxygen control the local chemical compositions of the reaction intermediates within a calcined particle. Additionally, it is found that the variations in the reducing power of the transition metals (i.e., Ni, Co, and Mn) determine the local structures at the nanoscale. The investigation of the reaction mechanism via imaging analysis provides valuable information for tuning the calcination chemistry and developing high‐energy/power density lithium‐ion batteries.
Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications.
Here, a semisupervised machine learning method for the discovery of structure-spectrum relationships is developed and then demonstrated using the specific example of interpreting x-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of >50 000 simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature but also reveal unintuitive ones that are visually indiscernible in large datasets. The results suggest that the RankAAE methodology has great potential to assist researchers in interpreting complex scientific data, testing physical hypotheses, and revealing patterns that extend scientific insight.
In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.
In this study, we introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures. Our work addresses the challenges of instance segmentation of plant point clouds, including the difficulty of annotating and labeling point clouds, the loss of local structural information in neural network components, and the generation of large numbers of incorrect small clusters due to poor choices of the loss function. One of the key contributions of our approach is a digital twin of sorghum, i.e., a procedural sorghum model, which was used to generate point clouds of sorghum fields. This allowed us to create a large-scale, annotated, synthetic dataset of sorghum plants that we used to train our PlantSegNet model. We demonstrated the effectiveness of our method in segmenting instances of sorghum leaves grown in outdoor field settings. To the best of our knowledge, this is the first study to address this specific instance segmentation problem for plants grown in such a setting. We compared our proposed method with other state-of-the-art methods for indoor settings, including SGPN and TreePartNet, on both synthetic and real data. Furthermore, our results show that PlantSegNet outperforms these methods regarding accuracy, robustness, and efficiency.
Radiation-magnetohydrodynamic simulations were able to reproduce features of the plasma structures observed in recent experiments1, where a laser was used to ablate plasma in a 3 MG magnetic field. The laser ablates the plasma, and localized structures are formed due to the inhibition of heat flow by the magnetic field. The large magnetic pressures cause the plasma to pinch. In an azimuthal fi eld, a disc-shaped plasma is generated. According to simulations, the disc has electron densities that are underdense to the laser, ranging from 10 18 to 10 19 cm -3 , and electron temperatures in the 300 to 1000 eV range during its evolution, similar to experimental data.
Two-dimensional iron chalcogenide intercalates display a remarkable correlation of the interlayer spacing with enhancement of the superconducting critical temperature (T c ). In this work, synchrotron X-ray absorption (XAS; at the Fe and Se K-edges) and emission (XES; at the Fe Κβ) spectroscopies allow one to discuss how the important rise of T c (~44 K) in the molecule-intercalated Li x (C 5 H 5 N) y Fe 2-z Se 2 relates to the electronic and local structural changes felt by the inorganic host upon doping (x). XES shows that widely separated layers of edge-sharing FeSe 4 tetrahedra carry low-spin moieties, with a local Fe magnetic moment slightly reduced compared to the parent β-Fe 2-z Se 2 . Pre-edge XAS expresses the progressively reduced mixing of metal 3d-4p states upon lithiation. Doping-mediated local lattice modifications, probed by conventional T c optimization measures (cf. the anion height and FeSe 4 tetrahedra regularity), become less relevant when layers are spaced far away. On the basis of extended X-ray absorption fine structure, such distortions are compensated by a softer Fe network that relates to Fe-site vacancies, alleviating electron-lattice correlations and superconductivity. Density functional theory (DFT) guided modification of the isolated Fe 2-z Se 2 (z, vacant sites) planes, resembling the host layers, identify that Fe-site deficiency occurs at low energy cost, giving rise to stretched Fe sheets, in accordance with experiments. Here, the robust high-T c in Li x (C 5 H 5 N) y Fe 2-z Se 2 , arises from the interplay of electron-donating spacers and the iron selenide layer's tolerance to defect chemistry, a tool to favorably tune its Fermi surface properties.
The integration of catalysts onto the surface of membranes enables simultaneous physical separation and catalytic transformation of constituents in a feed stream, facilitating improved contaminant removal and fouling mitigation. Distillation membranes are a particularly attractive platform for catalytic membranes because they reject nonvolatile species and exhibit exceptional resistance to oxidative and radical-driven degradation. However, imparting catalytic functionality onto hydrophobic, porous distillation membranes has proven challenging since the membranes used are chemically inert and difficult to modify. Furthermore, catalysts on the membrane surface can decrease hydrophobicity and increase the membrane’s susceptibility to pore wetting and failure. In this work, we create a catalytic distillation membrane by coating a polytetrafluoroethylene membrane surface with titanium dioxide (TiO 2 ) via plasma-assisted atomic layer deposition (ALD). By precisely tuning the ALD parameters, we demonstrate localized growth of TiO 2 near (within approximately 1 μm) the surface of polytetrafluoroethylene membranes, forming a catalytically active interface while preserving the underlying hydrophobic pore structure. Localized growth of TiO 2 is confirmed by electron microscopy and spectroscopy techniques, and membranes coated with 500 cycles of ALD show pressure tolerance up to 12.8 bar and higher than 95% salt rejection in pressure-driven distillation. Photocatalytic activity is demonstrated via the degradation of methylene blue dye under UV irradiation, where increasing TiO2 loading leads to an enhancement in dye degradation. These results establish a general strategy for integrating catalytic functionality into chemically inert, hydrophobic membranes without compromising distillation performance, providing a pathway toward multifunctional membranes that couple advanced oxidation with membrane separation for water treatment.
Dirac and Weyl semimetals are a central topic of contemporary condensed matter physics, and the discovery of new compounds with Dirac/Weyl electronic states is crucial to the advancement of topological materials and quantum technologies. Here we show a widely applicable strategy that uses high configuration entropy to engineer relativistic electronic states. We take the A MnSb 2 ( A = Ba, Sr, Ca, Eu, and Yb) Dirac material family as an example and demonstrate that mixing of Ba, Sr, Ca, Eu and Yb at the A site generates the compound (Ba 0.38 Sr 0.14 Ca 0.16 Eu 0.16 Yb 0.16 )MnSb 2 (denoted as A 5 MnSb 2 ), giving access to a polar structure with a space group that is not present in any of the parent compounds. A 5 MnSb 2 is an entropy-stabilized phase that preserves its linear band dispersion despite considerable lattice disorder. Although both A 5 MnSb 2 and A MnSb 2 have quasi-two-dimensional crystal structures, the two-dimensional Dirac states in the pristine A MnSb 2 evolve into a highly anisotropic quasi-three-dimensional Dirac state triggered by local structure distortions in the high-entropy phase, which is revealed by Shubnikov–de Haas oscillations measurements.