Enhanced near-infrared optical transmission in zinc germanium phosphide crystals via precise magnesium doping.

A zinc germanium phosphorus (ZnGeP2) crystal with a chalcopyrite structure is an efficient frequency converter in the mid-infrared region. However, point defect-induced optical absorption at the pumping wavelength (near infrared region) blocked the further application of ZnGeP2. To alleviate the absorption losses caused by point defects, in situ magnesium doping compensation was presented during the ZnGeP2 bulk crystal growth process via the vertical Bridgman method. Combined with theoretical calculations, the structural distortion of the magnesium-doped ZnGeP2 crystals in different orientations was illustrated. The thermodynamic and kinetic stability of the magnesium-doped ZnGeP2 structure were demonstrated. The transmission results indicated the improvement of transmittance within a wavelength range of 1.8-2.4 µm when doped with magnesium, which revealed the powerful ability of the appropriate dopant in optimizing near-infrared optical properties. Thus, the introduction of magnesium is a practical approach to improve the transmittance performance and extend the pumping source wavelengths of ZnGeP2 crystals.

Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.

Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and edges are invalid information or even interference information for the current task. It is necessary to reduce the network dimension and extract the core network. To address the problem of extracting and utilizing the core network, a core network extraction model (CWGCN) based on channel weighting and graph convolutional network and a graph convolutional network model (CCSR-GCN) based on channel convolution and style-based recalibration for emotion recognition have been proposed. The CWGCN model automatically extracts the core network and the channel importance parameter in a data-driven manner. The CCSR-GCN model innovatively uses the output information of the CWGCN model to identify the emotion state. The experimental results on SEED show that: (1) the core network extraction can help improve the performance of the GCN model; (2) the models of CWGCN and CCSR-GCN achieve better results than the currently popular methods. The idea and its implementation in this paper provide a novel and successful perspective for the application of GCN in brain network analysis of other specific tasks. The code is available at https://github.com/ykhdu/CWGCN-CCSR-GCN.

A Fast-Charge Graphite Anode with a Li-Ion-Conductive, Electron/Solvent-Repelling Interface.

Graphite has been serving as the key anode material of rechargeable Li-ion batteries, yet is difficultly charged within a quarter hour while maintaining stable electrochemistry. In addition to a defective edge structure that prevents fast Li-ion entry, the high-rate performance of graphite could be hampered by co-intercalation and parasitic reduction of solvent molecules at anode/electrolyte interface. Conventional surface modification by pitch-derived carbon barely isolates the solvent and electrons, and usually lead to inadequate rate capability to meet practical fast-charge requirements. Here we show that, by applying a MoOx-MoNx layer onto graphite surface, the interface allows fast Li-ion diffusion yet blocks solvent access and electron leakage. By regulating interfacial mass and charge transfer, the modified graphite anode delivers a reversible capacity of 340.3 mAh g-1 after 4000 cycles at 6 C, showing promises in building 10-min-rechargeable batteries with a long operation life.

Ni, Co, and Yb Cation Co-doping and Defect Engineering of FeOOH Nanorods as an Electrocatalyst for the Oxygen Evolution Reaction.

Electrocatalytic water splitting is a feasible technology that can produce hydrogen from renewable sources. The oxygen evolution reaction (OER), which has a slower kinetics and higher overpotential than the hydrogen evolution reaction, is the bottleneck that limits the overall water splitting. It is essential to develop efficient OER catalysts to reduce the anode overpotential. Herein, Ni,Co,Yb-FeOOH nanorod arrays grown directly on a carbon cloth are synthesized by a simple one-step hydrothermal method. The doped Ni2+ and Co2+ can occupy Fe2+ and Fe3+ sites in FeOOH, increasing the concentration of oxygen vacancies (VO), and the doped Yb3+ with a larger ionic radius can occupy the interstitial sites, which leads to more edge dislocations. VO and edge dislocations greatly enrich the active sites in FeOOH/CC. In addition, density functional theory calculations confirm that doping of Ni2+, Co2+, and Yb3+ modulates the electronic structure of the main active Fe sites, bringing its d-band center closer to the Fermi level and reducing the Gibbs free energy change of the rate-determining step of the OER. When the current density reaches 10 mA cm-2, the overpotential of Ni,Co,Yb-FeOOH/CC is only 230.9 mV, and the Tafel slope is 22.7 mV dec-1. In particular, a mechanism of multi-cation doping synergistic interaction with the oxygen vacancy and edge dislocation to enhance the OER catalytic activity of the material is proposed.

Dual-Branch Discrimination Network Using Multiple Sparse Priors for Image Deblurring.

Blind image deblurring is a challenging problem in computer vision, aiming to restore the sharp image from blurred observation. Due to the incompatibility between the complex unknown degradation and the simple synthetic model, directly training a deep convolutional neural network (CNN) usually cannot sufficiently handle real-world blurry images. An existed generative adversarial network (GAN) can generate more detailed and realistic images, but the game between generator and discriminator is unbalancing, which leads to the training parameters not being able to converge to the ideal Nash equilibrium points. In this paper, we propose a GAN with a dual-branch discriminator using multiple sparse priors for image deblurring (DBSGAN) to overcome this limitation. By adding the multiple sparse priors into the other branch of the discriminator, the task of the discriminator is more complex. It can balance the game between the generator and the discriminator. Extensive experimental results on both synthetic and real-world blurry image datasets demonstrate the superior performance of our method over the state of the art in terms of quantitative metrics and visual quality. Especially for the GOPRO dataset, the averaged PSNR improves 1.7% over others.

Rapid Identification for the Pterocarpus Bracelet by Three-Step Infrared Spectrum Method.

In order to explore a rapid identification method for the anti-counterfeit of commercial high value collections, a three-step infrared spectrum method was used for the pterocarpus collection identification to confirm whether a commercial pterocarpus bracelet (PB) was made from the precious species of Pterocarpus santalinus (P. santalinus). In the first step, undertaken by Fourier transform infrared spectroscopy (FTIR) spectrum, the absorption peaks intensity of PB was slightly higher than that of P. santalinus only at 1594 cm-1, 1205 cm-1, 1155 cm-1 and 836 cm-1. In the next step of second derivative IR spectra (SDIR), the FTIR features of the tested samples were further amplified, and the peaks at 1600 cm-1, 1171 cm-1 and 1152 cm-1 become clearly defined in PB. Finally, by means of two-dimensional correlation infrared (2DIR) spectrum, it revealed that the response of holocellulose to thermal perturbation was stronger in P. santalinus than that in PB mainly at 977 cm-1, 1008 cm-1, 1100 cm-1, 1057 cm-1, 1190 cm-1 and 1214 cm-1, while the aromatic functional groups of PB were much more sensitive to the thermal perturbation than those of P. santalinus mainly at 1456 cm-1, 1467 cm-1, 1518 cm-1, 1558 cm-1, 1576 cm-1 and 1605 cm-1. In addition, fluorescence microscopy was used to verify the effectiveness of the above method for wood identification and the results showed good consistency. This study demonstrated that the three-step IR method could provide a rapid and effective way for the anti-counterfeit of pterocarpus collections.

Ba3 (BO3 )(CO3 )F: The First Borate Carbonate Fluoride Synthesized by the High-Temperature Solution Method.

In contrast to the well-investigated halogen-containing borates and carbonates, very few halogen-containing borate carbonate compounds have been reported. Specifically, no example of borate carbonate fluoride has been synthesized successfully until now. Herein, the planar π-conjugated units [BO3 ]3- and [CO3 ]2- and the F- ions are introduced simultaneously into one crystal structure resulting in the first borate carbonate fluoride, Ba3 (BO3 )(CO3 )F, by the high-temperature solution method in the atmosphere. Its structure features a hexagonal channel formed by the [BO3 ]3- and [CO3 ]2- units with the [F3 Ba8 ]13+ trimers filled in the channel. Various characterizations including single crystal- and powder-XRD, EDX, IR, UV-vis-NIR, and TG-DSC, together with the first principles calculation have been carried out to verify the structure and fully understand the structure-property relationships.

A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption. Recent deep techniques provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following principled constraints underlying the domain knowledge so that the reliability of their reconstruction process is questionable. In this paper, we propose a deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering efficient and reliable reconstruction. We also apply the framework to three practical tasks, i.e., complex-valued data reconstruction, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios.

Rb3BaTeB7O15: a novel [B7O16] fundamental building block in a new telluroborate with [TeO3] polyhedra.

A new telluroborate Rb3BaTeB7O15, with a new type of fundamental building block, namely [B7O16] units, has been synthesized by the high-temperature flux method, and it crystallizes in the monoclinic space group P21/n (no. 11) with a three dimensional network. To the best of our knowledge, Rb3BaTeB7O15 is the first telluroborate that is constructed only by using [TeO3] polyhedra. Meanwhile, the stereochemical activity of the [TeO3] polyhedra was demonstrated by employing theoretical calculations. The UV-vis-NIR diffuse reflectance spectrum, thermal gravimetric results, differential scanning calorimetry curves and infrared spectrum of Rb3BaTeB7O15 were characterized and analyzed. In addition, the electronic structures and birefringence were discussed by using the first-principles calculations.

A Promising Fluorooxoborate Framework with Flexibile Capability for Diverse Cations to Enhance the Second Harmonic Generation.

Fluorooxoborates as potential deep ultraviolet (DUV) nonlinear optical (NLO) materials have exhibited diverse structures and NLO properties with metal cationic changes. Herein, the general mechanisms of metal cations on band gaps and optical properties in a series of typical fluorooxoborates have been clarified. It reveals that the framework of the emblematic 18-membered ring oxyfluorides has the flexibility of being able to contain different cations spanning from alkali to d10 metals by investigating the stability of the artificial CdB5 O7 F3 structure. Besides, introducing d10 metal cations can enhance the second harmonic generation (3.1×KH2 PO4 (KDP), d36 =0.39 pm V-1 ) and also keep a DUV spectral transparency (Eg >6.2 eV). Thus, the d10 -containing fluorooxoborate exhibits a great potential to be a new DUV optical material for nonlinear light-matter interactions.

On the Convergence of Learning-Based Iterative Methods for Nonconvex Inverse Problems.

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.

Mg(H2O)6B4O5(OH)4(H2O)3: a new hydrated borate with a short DUV cutoff edge.

A new hydrated magnesium borate, Mg(H2O)6B4O5(OH)4(H2O)3, was synthesized successfully by a facile slow evaporation method. Its structure was determined by single crystal X-ray diffraction, and it contains the functional building block (FBB) [B4O5(OH)4]. It possesses a short DUV cutoff edge (<175 nm) which benefits from the absence of dangling bonds in the crystal structure. Its properties were analyzed by IR spectroscopy and UV-vis-NIR diffuse reflectance spectroscopy. Moreover, theoretical calculations were carried out to analyze the relationship between the electronic structure and optical properties.

Fast density-peaks clustering for registration-free pediatric white matter tract analysis.

Clustering white matter (WM) tracts from diffusion tensor imaging (DTI) is primarily important for quantitative analysis on pediatric brain development. A recently developed algorithm, density peaks (DP) clustering, demonstrates great robustness to the complex structural variations of WM tracts without any prior templates. Nevertheless, the calculation of densities, the core step of DP, is time consuming especially when the number of WM fibers is huge. In this paper, we propose a fast algorithm that accelerates the density computation about 50 times over the original one. We convert the global calculation for the density as well as critical parameter in the process into local computations, and develop a binary tree structure to orderly store the neighbors for these local computations. Hence, the density computation turns out to be a direct access of the structure, rendering significantly computational saving. Performing experiments on synthetic point data and the JHU-DTI data set and comparing results of our fast DP algorithm and existing clustering methods, we can validate the efficiency and effectiveness of our fast DP algorithm. Finally, we demonstrate the application of the proposed algorithm on the analysis of pediatric WM tract development.

Transformation of the B-O Units from Corner-Sharing to Edge-Sharing Linkages in BaMBO4 (M = Ga, Al).

Two barium-containing borates BaMBO4 (M = Al, Ga) were synthesized via the solid-state method under atmospheric pressure. The 3D configurations of BaGaBO4 and BaAlBO4 are comprised of ∞2[Ba4O16]24-/∞2[Ga4O10]8-/[B2O5]4- and ∞3[Ba4O16]24-/∞2[Al4O10]8-/[B4O10]8-, respectively, of which the [B4O10]8- units possess unusual edge-sharing [BO4]5- tetrahedra. From BaGaBO4 to BaAlBO4, the B-O units are transformed from corner-sharing to edge-sharing linkages, which arises from the directional shrinkage caused by the Ba-O and M-O skeletons. The phonon spectra of these two compounds do not show imaginary frequency at any wave vectors, indicating that both of them are kinetically stable.

Deep Proximal Unrolling: Algorithmic Framework, Convergence Analysis and Applications.

Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus these approaches lack of rigorous mathematical derivations and clear interpretations. Several recent studies try to build deep models by unrolling a particular optimization model that involves task information. Unfortunately, due to the dynamic nature of network parameters, their resultant deep propagations do not possess the nice convergence property as the original optimization scheme does. In this work, we develop a generic paradigm to unroll nonconvex optimization for deep model design. Different from most existing frameworks, which just replace the iterations by network architectures, we prove in theory that the propagation generated by our proximally unrolled deep model can globally converge to the critical-point of the original optimization model. Moreover, even if the task information is only partially available (e.g., no prior regularization), we can still train a convergent deep propagations. We also extend these theoretical investigations on the more general multi-block models and thus a lot of real-world applications can be successfully handled by the proposed framework. Finally, we conduct experiments on various low-level vision tasks (i.e., non-blind deconvolution, dehazing, and low-light image enhancement) and demonstrate the superiority of our proposed framework, compared with existing state-of-the-art approaches.

K9[B4O5(OH)4]3(CO3)X·7H2O (X = Cl, Br): Syntheses, Characterizations, and Theoretical Studies of Noncentrosymmetric Halogen Borate-Carbonates with Short UV Cutoff Edges.

Two mixed halogen borate-carbonates K9[B4O5(OH)4]3(CO3)X·7H2O (X = Cl, Br) have been successfully synthesized with the hydrothermal method. They crystallize in the space group P6̅2 c, and their structures feature 3D networks consisting of the K9O30Cl polyhedra. The isolated [B4O9]6- groups and [BO3]3- triangles are filled in the space of 3D networks. K9[B4O5(OH)4]3(CO3)X·7H2O (X = Cl, Br) show the largest band gaps (6.34 and 5.65 eV, respectively) among the borate-carbonate systems. In addition, we investigated the main-group-element borate halides based on the Inorganic Crystal Structure Database, compared the structures, and summarized which structures are beneficial to the formation of isomorphic compounds. Herein, the syntheses, structures, first-principle calculations, and optical properties were reported in the work.

Ce(IO3 )2 F2 ⋠H2 O: The First Rare-Earth-Metal Iodate Fluoride with Large Second Harmonic Generation Response.

The first rare earth metal iodate fluoride, Ce(IO3 )2 F2 ⋅H2 O, was synthesized by a hydrothermal method. In the structure, CeO5 F4 polyhedra connect with isolated IO3 groups to form 1D infinite 1 ∞ [Ce(IO3 )2 F2 ] chains, which interconnect with each other by weak hydrogen bonds to construct the whole structure. Ce(IO3 )2 F2 ⋅H2 O produces a large second harmonic generation response, which is three times that of potassium dihydrogenphosphate. Theoretical calculations with DFT and dipole moments were performed to illustrate the relationships between the structure and the properties. The results show that Ce(IO3 )2 F2 ⋅H2 O is a new iodate fluoride with novel structure and potential applications in nonlinear optics.

Designing a stable feedback control system for blind image deconvolution.

Blind image deconvolution is one of the main low-level vision problems with wide applications. Many previous works manually design regularization to simultaneously estimate the latent sharp image and the blur kernel under maximum a posterior framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we present a novel perspective, using a stable feedback control system, to simulate the latent sharp image propagation. The controller of our system consists of regularization and guidance, which decide the sparsity and sharp features of latent image, respectively. Furthermore, the formational model of blind image is introduced into the feedback process to avoid the image restoration deviating from the stable point. The stability analysis of the system indicates the latent image propagation in blind deconvolution task can be efficiently estimated and controlled by cues and priors. Thus the kernel estimation used for image restoration becomes more precision. Experimental results show that our system is effective on image propagation, and can perform favorably against the state-of-the-art blind image deconvolution methods on different benchmark image sets and special blurred images.

Intestinal microbiota impact sepsis associated encephalopathy via the vagus nerve.

OBJECTIVE: The pathogenesis of sepsis associated encephalopathy (SAE) remains poorly understood. Vagus nerve plays an important role in gut-microbiota-brain axis. This study aimed to investigate whether vague nerve is a key mediator of the impact of intestinal microbiota on SAE. METHODS: Male rats were randomly divided into four groups (n=20): SHAM (SH) group, lipopolysaccharide (LPS) group, fecal microbiota transplantation (FMT) +LPS group, and vagotomy (VGX)+LPS+FMT group. The left cervical vagotomy was performed 30min before LPS administration in LPS+FMT+VGX group. LPS+ FMT and LPS+FMT+VGX groups received nasogastric infusion of feces from healthy donor three times a day. Fecal samples were collected every two days to monitor changes in microbiota composition by 16S rDNA analysis. Brain function was evaluated by behavioral tests and EEG. The levels of tumor necrosis factor alpha (TNF-α), interleukin (IL)-1ß, IL-6, IL-10 in brain cortex were detected by ELISA. The expression of Iba-1 in brain cortex was assessed by immunohistochemistry and Western blot analysis. RESULTS: Significant modification of microbiota composition, characterized by a profound increase of commensals in the Firmicutes phylum and depletion of opportunistic organisms in the Proteobacteria phylum, was observed in FMT groups compared to LPS group. Furthermore, we identified a reconstituted bacterial community enriched in Firmicutes and depleted of Proteobacteria. In both FMT groups the diversity of the fecal microbiota and the microbiota composition were similar to SH group. LPS mice treated with FMT demonstrated a better spatial memory and less EEG abnormalities, significantly attenuated levels of IL-1ß, IL-6, TNF-α, and decreased number of Iba-1 positive microglia in the cortex, but these beneficial effects of FMT were reversed by VGX. CONCLUSIONS: FMT can change intestinal microbiota in sepsis patients, and vagus nerve is a key mediator between intestinal microbiota and SAE. These findings suggest that FMT and vagus nerve are potential therapy targets for treating SAE.