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  1. Is the Matrix Completion of Reduced Density Matrices Unique?

    Reduced density matrices are central to describing observables in many-body quantum systems. In electronic structure theory, the two-particle reduced density matrix (2-RDM) suffices to determine the energy and other key properties. Recent work has used matrix completion, leveraging the low-rank structure of RDMs and approximate theoretical models, to reconstruct the 2-RDM from partial data and thus reduce the computational cost. However, matrix completion is, in general, an under-determined problem. Revisiting Rosina’s theorem (Rosina, M. Queen’s Papers on Pure and Applied Mathematics, 1968, No. 11, 369), we here show that the matrix completion is unique under certain conditions, identifying the subsetmore » of 2-RDM elements that enables its exact reconstruction from incomplete information. Building on this, we introduce a hybrid quantum–stochastic algorithm that achieves exact matrix completion, demonstrated through applications to the Fermi–Hubbard model.« less
  2. Errors in reconstruction of dichroic X-ray orientation tomography due to polarization rotation of the incident beam

    Dichroic X-ray tomography is a technique in which the crystal orientation or magnetization of a sample is resolved in three dimensions. The best-known uses of this technique are for observation of magnetic moments via circular dichroism, using left- and right-handed circularly polarized X-ray beams. Another variant uses linear dichroism to resolve the crystal orientation. In both these techniques, it is assumed that the absorption of X-rays along a path inside a material can be computed as a line integral of a local absorption coefficient along the ray path. For linear dichroism, this assumption is inaccurate because the polarization of themore » beam changes along the propagation direction when the optic axis of the material is not aligned along the polarization. In this work, a finite-element Maxwell solver is used to simulate tomography and reconstructions. The propagation effect can lead to significant errors in the reconstructed orientations. These errors may be mitigated by taking data at additional angles or by operating at energies at which the dichroism is weak. An iterative approach is proposed which may allow accurate reconstruction with fewer data than would otherwise be required.« less
  3. Interior soft x-ray tomography with sparse global sampling

    To investigate the feasibility of interior imaging reconstruction in soft X-ray tomography for higher-resolution cellular imaging, including whole-cell imaging, we develop an alignment and reconstruction algorithm that combines a small number of sparse whole-cell images with a high-resolution local interior scan. Based on numerical simulations, we demonstrate that combined reconstructions mitigate the depth-of-field limitation in high-resolution scans, enable radiation dose optimization, and yield quantitative X-ray absorption values with sparse sampling. We further validate our numerical approach using experimental data from two different cell types and show that the combined reconstruction reliably provides high spatial resolution within an interior region ofmore » interest of a whole cell. The resulting sparse reconstruction framework offers robust, faithful visualization of cellular organelles in soft X-ray tomography. This mesoscale imaging strategy allows one to ‘scout’ and zoom into selected subcellular volumes of interest, enabling increased spatial resolution without sacrificing larger-volume imaging and providing information on the relative positions of all organelles within a cell.« less
  4. Cathode Upcycling for Direct Recycling of Lithium-Ion Batteries Using a Precipitation Approach

    In line with carbon neutrality goals by 2050, the Li-ion battery market has surged. To enhance battery sustainability and circularity, direct recycling methods aim to recover intact cathode materials. However, end-of-life cathode materials are typically 15-20 years old and often have lower energy densi-ty compared to current cathode materials. In response, we have developed a rapid precipitation process to boost energy density by converting low Ni-compositions, LiNi0.33Co0.33Mn0.33O2 (NMC111), into higher Ni-compositions (NMC622). This process forms a Ni-rich coating that diffuses into the core, in-creasing compositional homogeneity upon high-temperature relithiation. The upcycling process leverages existing infrastructure, offering low capital cost andmore » minimal additional chemical input. Through ex-situ tomographic transmission X-ray microscopy (TXM), we quantify three-dimensional Ni:Co elemental mixing, confirming that elemental content evens at the secondary particle level, but elemental gradients remain at the primary particle level upon relithiation. Ex-situ high-resolution and in-situ wide-angle X-ray diffraction reveals concurrent structural changes during the relithiation process. These findings guide further improvements in synthesis for increased capacity and retention.« less
  5. Discorpy: algorithms and software for camera calibration and correction

    Camera or lens-based detector calibration is essential for spatial accuracy in applications like dimensional tomography, optical metrology, and computer vision. Many methods and software exist yet there is still a lack of approaches that achieve both high accuracy and robustness while being easy to use and capable of handling a wide range of distortions. Radial lens distortion is common in high-resolution X-ray detector optics used in parallel-beam tomography at synchrotrons. Achieving sub-pixel accuracy requires calibrating with an optical target image. Although methods for characterizing radial distortion are well established, acquired images often also include perspective distortion and optical center offset.more » Here, we present our approaches to individually characterize and correct both types of distortion using a single calibration image, implemented in the Discorpy software.« less
  6. Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of focus limits

    Electron ptychography is a powerful computational method for atomic-resolution imaging with high contrast for weakly and strongly scattering elements. Modern algorithms coupled with fast and efficient detectors allow imaging specimens with tens of nanometers thicknesses with sub-0.5 Ångstrom lateral resolution. However, the axial resolution in these approaches is currently limited to a few nanometers, limiting their ability to solve novel atomic structures ab initio. Here, we experimentally demonstrate multi-slice ptychographic electron tomography, which allows atomic resolution three-dimensional phase-contrast imaging in a volume surpassing the depth of field limits. We reconstruct tilt-series 4D-STEM measurements of a $$\mathrm{Co_3O_4}$$ nanocube, yielding 2 Åmore » axial and 0.7 Å transverse resolution in a reconstructed volume of $$\mathrm{(18.2\,nm)^3}$$. Our results demonstrate a 13.5-fold improvement in axial resolution compared to multi-slice ptychography while retaining the atomic lateral resolution and the capability to image volumes beyond the depth of field limit. Multi-slice ptychographic electron tomography significantly expands the volume of materials accessible using high-resolution electron microscopy. We discuss further experimental and algorithmic improvements necessary to also resolve single weakly scattering atoms in 3D.« less
  7. Three-dimensional structure of buried heterointerfaces revealed by multislice ptychography

    Here, we report on the three-dimensional (3D) structure determination of a twisted hexagonal boron nitride (h-BN) heterointerface from a single-view dataset using multislice ptychography. We identify the buried heterointerface between two twisted h-BN flakes with a lateral resolution of 0.57 Å and a depth resolution of 2.5 nm. The latter represents a significant improvement (∼2.7 times) over the aperture-limited depth resolution of incoherent imaging modes, such as annular-dark-field scanning transmission electron microscopy. This improvement is attributed to the diffraction signal extending beyond the aperture edge, with the depth resolution set by the curvature of the Ewald sphere. Future advancements inmore » this approach could enhance the depth resolution to the subnanometer level and enable the identification of individual dopants, defects, and color centers in twisted heterointerfaces and other materials.« less
  8. Atom Probe Tomography Investigation of Clustering in Model P2O5-Doped Borosilicate Glasses for Nuclear Waste Vitrification

    Atom probe tomography (APT) has been utilized to investigate the microstructure of two model borosilicate glasses designed to understand the solubility limits of phosphorous pentoxide (P2O5). This component is found in certain high-level radioactive defence wastes destined for vitrification, where phase separation can potentially lead to a number of issues relating to the processing of the glass and its long-term chemical and structural stability. The development of suitable focused ion beam (FIB)-preparation routes and APT analysis conditions were initially determined for the model glasses, before examining their detailed microstructures. In a 3.0 mol% P2O5-doped glass, both visual inspection and sensitivemore » statistical analysis of the APT data show homogeneous microstructures, while raising the content to 4.0 mol% initiates the formation of phosphorus-enriched nanoscale precipitates. This study confirms the expected inhomogeneities and phase separation of these glasses and offers routes to characterizing these at near-atomic scale resolution using APT.« less
  9. X-ray scattering based scanning tomography for imaging and structural characterization of cellulose in plants

    X-ray and neutron scattering have long been used for structural characterization of cellulose in plants. Due to averaging over the illuminated sample volume, these measurements traditionally overlooked the compositional and morphological heterogeneity within the sample. Here, a scanning tomographic imaging method is described, using contrast derived from the X-ray scattering intensity, for virtually sectioning the sample to reveal its internal structure at a resolution of a few micrometres. This method provides a means for retrieving the local scattering signal that corresponds to any voxel within the virtual section, enabling characterization of the local structure using traditional data-analysis methods. This ismore » accomplished through tomographic reconstruction of the spatial distribution of a handful of mathematical components identified by non-negative matrix factorization from the large dataset of X-ray scattering intensity. Joint analysis of multiple datasets, to find similarity between voxels by clustering of the decomposed data, could help elucidate systematic differences between samples, such as those expected from genetic modifications, chemical treatments or fungal decay. The spatial distribution of the microfibril angle can also be analyzed, based on the tomographically reconstructed scattering intensity as a function of the azimuthal angle.« less
  10. DLSIA: Deep Learning for Scientific Image Analysis

    DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting formore » X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.« less
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