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The novel series of hole-doped quadruple manganite perovskites Hg$_{1-x}$Na$_x$Mn$_3$Mn$_4$O$_{12}$ (HNMO) has been synthesized and its charge and orbital order behavior investigated through high-resolution synchrotron powder x-ray diffraction techniques. Through careful Rietveld refinements of structural models $via$ symmetry-motivated approaches, we show that the ground state of HNMO compositions adopts a polar $G$-type charge and orbital ordered state, which is rare in manganite perovskites, and is robust as a sole phase up to a critical doping level. Upon this critical doping, coincident with that in which colossal magnetoresistance (CMR) is maximal in canonical manganite perovskites, electronic phase separation occurs between $G$-type and orbital order with charge disorder-type states. The latter state has recently been identified in Ca$_{1-x}$Na$_x$Mn$_3$Mn$_4$O$_{12}$ perovskites, and proposed to be the competing insulating state from which CMR phenomena emerges. We show the mechanism for the formation of the $G$-type state is due to charge transfer processes which may occur through a coupling of distortions involving structural and charge and orbital degrees of freedom, ultimately driving the polar ground state through an improper-like ferroelectric polarization mechanism. These results will act as an important recipe for designing novel ferroelectric-active materials, in addition to expanding the richness of charge and orbital ordered states in manganite perovskites., Comment: 11 pages, 8 figures

The PZT/FeGa thin film memtranstor was prepared and the modulation of the magnetoelectric coefficient by external magnetic and electric fields was studied. The magnetoelectric coefficient of the PZT/FeGa memtranstor can be reversed by flipping the direction of magnetization of FeGa or ferroelectric polarization of PZT. Notably, the sign of the magnetoelectric coefficient can be switched repeatedly by reversing ferroelectric polarization of PZT when the external magnetic field remains constant. Moreover, the binary switching behavior can still be maintained under zero DC bias magnetic field. When the polarization direction remains stable, the magnetoelectric coefficient also does not change. This means that the magnetoelectric coefficient of PZT/FeGa is non-volatile. Furthermore, the retention and endurance characteristics of the PZT/FeGa thin film memtranstor have been investigated. These findings demonstrate the potential of the PZT/FeGa thin film memtranstor for non-volatile memory applications., Comment: 10 pages, 4 figures

Shear wall structures are widely used in high-rise residential buildings, and the layout of shear walls requires many years of design experience and iterative trial and error. Currently, there are methods based on heuristic algorithms, but they generate results too slowly. Those based on Generative Adversarial Networks (GANs) or Graph Neural Networks (GNNs) can only generate single arrangements and require large amounts of training data. At present, Stable Diffusion is being widely used, and by using the Low-Rank Adaptation (LoRA) method to fine-tune large models with small amounts of data, good generative results can be achieved. Therefore, this paper proposes a personalized AI assistant for shear wall layout based on Stable Diffusion, which has been proven to produce good generative results through testing.

Eddy current testing (ECT) is one of the most popular Nondestructive Testing (NDT) techniques, especially for conductive materials. Reconstructing the crack profile from measured EC signals is one of the main goals of ECT. This task is highly challenging, as the EC signals are nonlinear responses resulted from the presence of cracks, and reconstructing the crack profile requires establishing the forward model of the nonlinear electromagnetic dynamics and solving its inverse problem, which is an ill-posed numerical optimization problem. Instead of solving the inverse problem numerically, we propose to directly learn the inverse mapping from EC signals to crack profiles with a deep encoder-decoder convolutional neural network named EddyNet. EddyNet is trained on a set of randomly generated crack profiles and the corresponding simulated EC responses generated from a realistic forward model. On the held-out test data, EddyNet achieved a mean absolute error of 0.198 between predicted profiles and ground truth ones. Qualitatively, the geometries of predicted profiles are visually similar to the ground truth profiles. Our method greatly reduces the usual reliance on domain experts, and the reconstruction is extremely fast both on GPUs and on CPUs. The source code of EddyNet is released on https://github.com/askerlee/EddyNet., Comment: Accepted by Second Workshop on Machine Learning and the Physical Sciences, NeurIPS 2019

A series of unprecedentedly wide Curie-temperature windows (CTWs) between 40 and 450 K are realized by employing the isostructural alloying principle for the strongly coupled magnetostructural phase transitions in a single host system. The CTWs provide a design platform for magneto-multifunctional multiferroic alloys that can be manipulated in a quite large temperature space in various scales and patterns, as well as by multiple physical fields., Comment: 43 pages, 4 figs, 1 supporting file. Accepted by Advanced Electronic Materials

Electrical injection of spin-polarized electrons into a semiconductor, large spin diffusion length, and an integration friendly platform are desirable ingredients for spin-based devices. Here we demonstrate lateral spin injection and detection in germanium nanowires, by using ferromagnetic metal contacts and tunnel barriers for contact resistance engineering. Using data measured from over 80 samples, we map out the contact resistance window for which lateral spin transport is observed, manifestly showing the conductivity matching required for spin injection. Our analysis, based on the spin diffusion theory, indicates that the spin diffusion length is larger than 100 {\mu}m in germanium nanowires at 4.2 K.

We demonstrate the fabrication of high-performance Ge-SixGe1-x core-shell nanowire field-effect transistors with highly doped source and drain, and systematically investigate their scaling properties. Highly doped source and drain regions are realized by low energy boron implantation, which enables efficient carrier injection with a contact resistance much lower than the nanowire resistance. We extract key device parameters, such as intrinsic channel resistance, carrier mobility, effective channel length, and external contact resistance, as well as benchmark the device switching speed and ON/OFF current ratio., Comment: 5 pages, 4 figures. IEEE Transactions on Electron Devices (in press)