Rational Design and General Synthesis of High-Entropy Metallic Ammonium Phosphate Superstructures Assembled by Nanosheets.

Three-dimensional (3D) superstructure nanomaterials with special morphologies and novel properties have attracted considerable attention in the fields of optics, catalysis, and energy storage. The introduction of high entropy into ammonium phosphate (NPO·nH2O) has not yet attracted much attention in the field of energy storage materials. Herein, we systematically synthesize a series of 3D superstructures of NPOs·nH2O ranging from unitary, binary, ternary, and quaternary to high-entropy by a simple chemical precipitation method. These materials have similar morphology, crystallinity, and synthesis routes, which eliminates the performance difference caused by the interference of physical properties. Subsequently, cobalt-nickel ammonium phosphate (CoxNiy-NPO·nH2O) powders with different cobalt-nickel molar ratios were synthesized to predict the promoting effect of mixed transition metals in supercapacitors. It is found that the CoxNiy-NPO·nH2O 3D superstructures with a Co/Ni ratio of 1:1 show the best electrochemical performance for energy storage. The aqueous device shows a high energy density of 36.18 W h kg-1 at a power density of 0.71 kW kg-1, and when the power density is 0.65 kW kg-1, the energy density of the solid-state device is 13.83 W h kg-1. The work displays a facile method for the fabrication of 3D superstructures assembled by 2D nanosheets that can be applied in energy storage.

2D MOF-assisted Pyrolysis-displacement-alloying Synthesis of High-entropy Alloy Nanoparticles Library for Efficient Electrocatalytic Hydrogen Oxidation.

Multimetallic alloy nanoparticles (NPs) have received considerable attention in various applications due to their compositional variability and exceptional properties. However, the complexity of both the general synthesis and structure-activity relationships remain the long-standing challenges in this field. Herein, we report a versatile 2D MOF-assisted pyrolysis-displacement-alloying route to successfully synthesize a series of binary, ternary and even high-entropy NPs that are uniformly dispersed on porous nitrogen-doped carbon nanosheets (PNC NSs). As a proof of utility, the obtained Co0.2 Ru0.7 Pt0.1 /PNC NSs exhibits apparent hydrogen oxidation activity and durability with a record-high mass specific kinetic current of 1.84 A mg-1 at the overpotential of 50 mV, which is approximately 11.5 times higher than that of the Pt benchmark. Both experimental and theoretical studies reveal that the addition of Pt engenders a phase transition in CoRu alloys from hexagonal close-packed (hcp) to face-centered cubic (fcc) structure. The elevated reactivity of the resulted ternary alloy can be attributed to the optimized adsorption of hydrogen intermediate and the decreased reaction barrier for water formation. This study opens a new avenue for the development of highly efficient alloy NPs with various compositions and functions.

Applications of Metal-Organic Frameworks in Water Treatment: A Review.

The ever-expanding scale of industry and agriculture has led to the gradual increase of pollutants (e.g., heavy metal ions, synthetic dyes, and antibiotics) in water resources, and the ecology and wastewater are grave problems that need to be solved urgently and has attracted widespread attention from the research community and industry in recent years. Metal-organic frameworks (MOFs) are a type of organic-inorganic hybrid material with a distinctive 3D network crystal structure. Lately, MOFs have made striking progress in the fields of adsorption, catalytic degradation, and biomedicine on account of their large specific surface and well-developed pore structure. This review summarizes the latest research achievements in the preparation of pristine MOFs, MOF composites, and MOF derivatives for various applications including the removal of heavy metal ions, organic dyes, and other harmful substances in sewage. Furthermore, the working mechanisms of utilizing adsorption, photocatalytic degradation, and membrane separation technologies are also briefly described for specific pollutants removal from sewage. It is expected that this review will provide inspiration and references for the synthesis of pristine MOFs as well as their composites and derivatives with excellent water treatment performance.

Composites of (NH2)-MIL-53(Al) and CBB as bifunctional electrocatalysts for overall electrochemical water splitting in all pH solutions.

Electrochemical water splitting is one of the most active areas of energy research, yet the benchmark electrocatalysts used for this area are based on expensive noble metals and transition metals, thus mainly reactions in alkaline solution. MOFs and halide perovskite are novel electrochemical catalysts but unstable in water basically. Here we report a study on composites of (NH2)-MIL-53(Al) MOFs and CBB halide perovskite (Cs3Bi2Br9), which exhibit obvious activity for overall electrochemical water splitting for long-term stability with little deactivation after 10 h in all pH solutions.

Synergistic Mechanism of Sub-Nanometric Ru Clusters Anchored on Tungsten Oxide Nanowires for High-Efficient Bifunctional Hydrogen Electrocatalysis.

The construction of strong interactions and synergistic effects between small metal clusters and supports offers a great opportunity to achieve high-performance and cost-effective heterogeneous catalysis, however, studies on its applications in electrocatalysis are still insufficient. Herein, it is reported that W18 O49 nanowires supported sub-nanometric Ru clusters (denoted as Ru SNC/W18 O49 NWs) constitute an efficient bifunctional electrocatalyst for hydrogen evolution/oxidation reactions (HER and HOR) under acidic condition. Microstructural analyses, X-ray absorption spectroscopy, and density functional theory (DFT) calculations reveal that the Ru SNCs with an average RuRu coordination number of 4.9 are anchored to the W18 O49 NWs via RuOW bonds at the interface. The strong metal-support interaction leads to the electron-deficient state of Ru SNCs, which enables a modulated RuH strength. Furthermore, the unique proton transport capability of the W18 O49 also provides a potential migration channel for the reaction intermediates. These components collectively enable the remarkable performance of Ru SNC/W18 O49 NWs for hydrogen electrocatalysis with 2.5 times of exchange current density than that of carbon-supported Ru nanoparticles, and even rival the state-of-the-art Pt catalyst. This work provides a new prospect for the development of supported sub-nanometric metal clusters for efficient electrocatalysis.

Longitudinal in Utero Analysis of Engrailed-1 Knockout Mouse Embryonic Phenotypes Using High-Frequency Ultrasound.

Large-scale international efforts to generate and analyze loss-of-function mutations in each of the approximately 20,000 protein-encoding gene mutations are ongoing using the "knockout" mouse as a model organism. Because one-third of gene knockouts are expected to result in embryonic lethality, it is important to develop non-invasive in utero imaging methods to detect and monitor mutant phenotypes in mouse embryos. We describe the utility of 3-D high-frequency (40-MHz) ultrasound (HFU) for longitudinal in utero imaging of mouse embryos between embryonic days (E) 11.5 and E14.5, which represent critical stages of brain and organ development. Engrailed-1 knockout (En1-ko) mouse embryos and their normal control littermates were imaged with HFU in 3-D, enabling visualization of morphological phenotypes in the developing brains, limbs and heads of the En1-ko embryos. Recently developed deep learning approaches were used to automatically segment the embryonic brain ventricles and bodies from the 3-D HFU images, allowing quantitative volumetric analyses of the En1-ko brain phenotypes. Taken together, these results show great promise for the application of longitudinal 3-D HFU to analyze knockout mouse embryos in utero.

Integrated 3d flow-based multi-atlas brain structure segmentation.

MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method's potential for general applicability in various brain structures and settings.

A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos.

Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36-0.09 s/volume ≈ 1000× faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation.

DEEP MOUSE: AN END-TO-END AUTO-CONTEXT REFINEMENT FRAMEWORK FOR BRAIN VENTRICLE & BODY SEGMENTATION IN EMBRYONIC MICE ULTRASOUND VOLUMES.

The segmentation of the brain ventricle (BV) and body in embryonic mice high-frequency ultrasound (HFU) volumes can provide useful information for biological researchers. However, manual segmentation of the BV and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.

AUTOMATIC BODY LOCALIZATION AND BRAIN VENTRICLE SEGMENTATION IN 3D HIGH FREQUENCY ULTRASOUND IMAGES OF MOUSE EMBRYOS.

This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.

DEEP BV: A FULLY AUTOMATED SYSTEM FOR BRAIN VENTRICLE LOCALIZATION AND SEGMENTATION IN 3D ULTRASOUND IMAGES OF EMBRYONIC MICE.

Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from HFU volumes is tedious, time-consuming, and requires specialized expertise. In this paper, we propose a novel deep learning based BV segmentation system for whole-body HFU images of mouse embryos. Our fully automated system consists of two modules: localization and segmentation. It first applies a volumetric convolutional neural network on a 3D sliding window over the entire volume to identify a 3D bounding box containing the entire BV. It then employs a fully convolutional network to segment the detected bounding box into BV and background. The system achieves a Dice Similarity Coefficient (DSC) of 0.8956 for BV segmentation on an unseen 111 HFU volume test set surpassing the previous state-of-the-art method (DSC of 0.7119) by a margin of 25%.