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Intertwining of different quantum degrees of freedom manifests exotic quantum phenomena in many-body systems, especially in reduced dimensionality. Here we show that monolayered NbTe2 serves as an ideal platform where lattice, charge, and spin degrees of freedom manifest cooperatively, leading to a new and threading order of chirality. By using spin-polarized scanning tunneling microscopy/spectroscopy, we reveal that the root19 * root19 phase of NbTe2 is encoded with both alternating-chiral atomic displacements and charge density waves, characterized by two chiral units of opposite handedness within the reconstructed cell. We show unambiguous evidence for emergent spin polarizations spreading over the primitive cell, with the magnetization orientation synchronized with alternating handedness of chiral order. Our first-principles studies identify the origin of intertwined orders being correlation driven, with the threading order of chirality emerging when the on-site Coulomb repulsion exceeds a critical value. The spin ordering is further shown to be of hybrid ferrimagnetic nature, contributed by the itinerant electrons and localized d-orbitals. Collectively, these findings expand the realm of chiral order in correlated electron systems, and facilitate an appealing platform for chiral spintronic and related applications.

We present a deep-learning based computing framework for fast-and-accurate CT (DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to improve the testing speed and accuracy of COVID-19 (plus its variants) via a DL-based approach for CT image enhancement and classification. The image enhancement network is adapted from DDnet, short for DenseNet and Deconvolution based network. To demonstrate its speed and accuracy, we evaluated DL-FACT across several sources of COVID-19 CT images. Our results show that DL-FACT can significantly shorten the turnaround time from days to minutes and improve the COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool for medical professionals in diagnosing and monitoring COVID-19., Comment: 10 pages

Computed Tomography (CT) is an advanced imaging technology used in many important applications. Here we present a deep-learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high resolution (HR) CT images. The method synergistically combines a SR model in sinogram domain, a deblur model in image domain, and the iterative framework into a CT SR algorithm super resolution and deblur based iterative reconstruction (SADIR). We incorporated the CT domain knowledge into the SADIR and unrolled it into a DL network (SADIR Net). The SADIR Net is a zero shot learning (ZSL) network, which can be trained and tested with a single sinogram in the test time. The SADIR was evaluated via SR CT imaging of a Catphan700 physical phantom and a biological ham, and its performance was compared to the other state of the art (SotA) DL-based methods. The results show that the zero-shot SADIR-Net can indeed provide a performance comparable to the other SotA methods for CT SR reconstruction, especially in situations where training data is limited. The SADIR method can find use in improving CT resolution beyond hardware limits or lowering requirement on CT hardware.

We design a hybrid graphene/hexagonal boron nitride superlattice monolayer and investigate its thermoelectric properties using density functional theory and Boltzmann transport equations with the relaxation time accurately treated by electron-phonon coupling calculations. Compared with that of pristine graphene, the lattice thermal conductivity of the superlattice structure is more than two orders of magnitude lower due to the enhanced three-phonon scattering process originated from the mixed-bond characteristics. Besides, the coexistence of light and heavy bands around the Fermi level leads to an ultrahigh power factor along the zigzag direction, where the highest ZT value of ~2.5 can be achieved for the n-type system at 1100 K. Moreover, it is noted that the carrier transport near the valance band minimum is almost entirely contributed by the graphene part of the superlattice. As a consequence, the thermoelectric performance of p-type system can be enhanced to be comparable with that of n-type one by appropriate substitution of nitrogen atom with phosphorus, which can suppress the lattice thermal conductivity but nearly have no influence on the hole transport.

In sparse-view Computed Tomography (CT), only a small number of projection images are taken around the object, and sinogram interpolation method has a significant impact on final image quality. When the amount of sparsity (the amount of missing views in sinogram data) is not high, conventional interpolation methods have yielded good results. When the amount of sparsity is high, more advanced sinogram interpolation methods are needed. Recently, several deep learning (DL) based sinogram interpolation methods have been proposed. However, those DL-based methods have mostly tested so far on computer simulated sinogram data rather experimentally acquired sinogram data. In this study, we developed a sinogram interpolation method for sparse-view micro-CT based on the combination of U-Net and residual learning. We applied the method to sinogram data obtained from sparse-view micro-CT experiments, where the sparsity reached 90%. The interpolated sinogram by the DL neural network was fed to FBP algorithm for reconstruction. The result shows that both RMSE and SSIM of CT image are greatly improved. The experimental results demonstrate that this sinogram interpolation method produce significantly better results over standard linear interpolation methods when the sinogram data are extremely sparse.

Photon-counting computed tomography (PCCT) with energy discrimination capabilities hold great potentials to improve the limitations of the conventional CT, including better signal-to-noise ratio (SNR), improved contrast-to-noise ratio (CNR), lower radiation dose, and most importantly, simultaneous multiple material identification. One potential way of material identification is via calculation of effective atomic number and effective electron density from PCCT image data. However, the current methods for calculating effective atomic number and effective electron density from PCCT image data are mostly based on semi-empirical models and accordingly are not sufficiently accurate. Here, we present a physics-based model to calculate the effective atomic number and effective electron density of various matters, including single element substances, molecular compounds, and multi-material mixtures as well. The model was validated over several materials under various combinations of energy bins. A PCCT system was simulated to generate the PCCT image data, and the proposed model was applied to the PCCT image data. Our model yielded a relative standard deviations for effective atomic numbers and effective electron densities at less than 1%. Our results further showed that five different materials can be simultaneously identified and well separated in a effective atomic number - effective electron density map. The model could serve as a basis for simultaneous material identification from PCCT.

Ternary half-Heusler compounds with widely tunable electronic structures, present a new platform to discover topological insulators. Due to time-consuming computations and synthesis procedures, the identification of new topological insulators is however a rough task. Here, we adopt a compressed-sensing approach to rapidly screen potential topological insulators in half-Heusler family, which is realized via a two-dimensional descriptor that only depends on the fundamental properties of the constituent atoms. Beyond the finite training data, the proposed descriptor is employed to screen many new half-Heusler compounds, including those with integer and fractional stoichiometry, and a larger number of possible topological insulators are predicted.

Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a novel two-dimensional descriptor by applying an artificial intelligence (AI) based approach for fast and reliable identification of the topological characters of a drastically expanded range of materials, without prior determination of their specific symmetries and detailed band structures. By leveraging this descriptor that contains only the atomic number and electronegativity of the constituent species, we have readily scanned a huge number of alloys in the tetradymite family. Strikingly, nearly half of which are identified to be topological insulators, revealing a much larger territory of the topological materials world. The present work also attests the increasingly important role of such AI-based approaches in modern materials discovery.