Construction of unique NiCoP/FeNiCoP hollow heterostructured ellipsoids with modulated electronic structure for enhanced overall water splitting.

Transition metal phosphides have been demonstrated to be promising non-noble catalysts for water splitting, yet their electrocatalytic performance is impeded by unfavorable free energies of adsorbed intermediates. The achievement of nanoscale modulation in morphology and electronic states is imperative for enhancing their intrinsic electrocatalytic activity. Herein, we propose a strategy to expedite the water splitting process over NiCoP/FeNiCoP hollow ellipsoids by modulating the electronic structure and d-band center. These unique phosphorus (P) vacancies-rich ellipsoids are synthesized through an ion-exchange reaction between uniform NiCo-nanoprisms and K3[Fe(CN)6], followed by NaH2PO2-assisted phosphorization under N2 atmosphere. Various characterizations reveals that the titled catalyst possesses high specific surface area, abundant porosity, and accessible inner surfaces, all of which are beneficial for efficient mass transfer and gas diffusion. Moreover, density functional theory (DFT) calculations further confirms that the NiCoP/FeNiCoP heterojunction associated with P vacancies regulate the electronic structures of d-electrons and p-electrons of Co and P atoms, respectively, resulting in a higher desorption efficiency of adsorbed H* intermediates with a lower energy barrier for water splitting. Due to the aforementioned advantages, the resultant NiCoP/FeNiCoP hollow ellipsoids exhibit remarkably low overpotentials of 45 and 266 mV for hydrogen and oxygen evolution reaction to achieve the current densities of 10 and 50 mA cm-2, respectively. This work not only reports the synthesis of a hollow double-shell structure of NiCoP/FeNiCoP but also introduces a novel strategy for constructing a multifunctional electrocatalyst for water splitting.

Amorphous and defective Co-P-O@NC ball-in-ball hollow structure for highly efficient electrocatalytic overall water splitting.

Electrochemical water splitting using hollow and defect-rich catalysts has emerged as a promising strategy for efficient hydrogen production. However, the rational design and controllable synthesis of such catalysts with intricate morphology and composition present significant challenges. Herein, we propose a template-engaged approach to fabricate a novel ball-in-ball hollow structure of Co-P-O@N-doped carbon with abundant oxygen vacancies. The synthesis process involves the preparation of uniform cobalt-glycerate (Co-gly) polymer microspheres as precursors, followed by surface coating with ZIF-67 layer, adjustable chemical etching by phytic acid, and controllable pyrolysis at high temperature. The resulting ball-in-ball structure offers a large number of accessible active sites and high redox reaction centers, facilitating efficient charge transport, mass transfer, and gas evolution, which are beneficial for the acceleration of electrocatalytic reaction. Additionally, density functional theory (DFT) calculations indicate that the incorporation of oxygen and the presence of Co-P dangling bonds in CoP significantly enhance the adsorption of oxygenated species, leading to improved intrinsic electroactivity at the single-site level. As a sequence, the titled catalyst exhibits remarkable electrocatalytic activity and stability for water splitting in alkaline media. Notably, it only requires a low overpotential of 283 mV to achieve a current density of 10 mA cm-2 for the oxygen evolution reaction. This work may provide some new insights into the design of complex hollow structures of phosphides with abundant defects for energy conversion.

Accurate and lightweight MRI super-resolution via multi-scale bidirectional fusion attention network.

High-resolution magnetic resonance (MR) imaging has attracted much attention due to its contribution to clinical diagnoses and treatment. However, because of the interference of noise and the limitation of imaging equipment, it is expensive to generate a satisfactory image. Super-resolution (SR) is a technique that enhances an imaging system's resolution, which is effective and cost-efficient for MR imaging. In recent years, deep learning-based SR methods have made remarkable progress on natural images but not on medical images. Most existing medical images SR algorithms focus on the spatial information of a single image but ignore the temporal correlation between medical images sequence. We proposed two novel architectures for single medical image and sequential medical images, respectively. The multi-scale back-projection network (MSBPN) is constructed of several different scale back-projection units which consist of iterative up- and down-sampling layers. The multi-scale machine extracts different scale spatial information and strengthens the information fusion for a single image. Based on MSBPN, we proposed an accurate and lightweight Multi-Scale Bidirectional Fusion Attention Network(MSBFAN) that combines temporal information iteratively. That supplementary temporal information is extracted from the adjacent image sequence of the target image. The MSBFAN can effectively learn both the spatio-temporal dependencies and the iterative refinement process with only a lightweight number of parameters. Experimental results demonstrate that our MSBPN and MSBFAN are outperforming current SR methods in terms of reconstruction accuracy and parameter quantity of the model.

Center-environment feature models for materials image segmentation based on machine learning.

Materials properties depend not only on their compositions but also their microstructures under various processing conditions. So far, the analyses of complex microstructure images rely mostly on human experience, lack of automatic quantitative characterization methods. Machine learning provides an emerging vital tool to identify various complex materials phases in an intelligent manner. In this work, we propose a "center-environment segmentation" (CES) feature model for image segmentation based on machine learning method with environment features and the annotation input of domain knowledge. The CES model introduces the information of neighbourhood as the features of a given pixel, reflecting the relationships between the studied pixel and its surrounding environment. Then, an iterative integrated machine learning method is adopted to train and correct the image segmentation model. The CES model was successfully applied to segment seven different material images with complex texture ranging from steels to woods. The overall performance of the CES method in determining boundary contours is better than many conventional methods in the case study of the segmentation of steel image. This work shows that the iterative introduction of domain knowledge and environment features improve the accuracy of machine learning based image segmentation for various complex materials microstructures.

Construction of Fe-doped NiS-NiS2 Heterostructured Microspheres Via Etching Prussian Blue Analogues for Efficient Water-Urea Splitting.

Developing efficient and robust non-precious-metal-based catalysts to accelerate electrocatalytic reaction kinetics is crucial for electrochemical water-urea splitting. Herein, Fe-doped NiS-NiS2 heterostructured microspheres, an electrocatalyst, are synthesized via etching Prussian blue analogues following a controlled annealing treatment. The resulting microspheres are constructed by mesoporous nanoplates, granting the virtues of large surface areas, high structural void porosity, and accessible inner surface. These advantages not only provide more redox reaction centers but also strengthen structural robustness and effectively facilitate the mass diffusion and charge transport. Density functional theory simulations validate that the Fe-doping improves the conductivity of nickel sulfides, whereas the NiS-NiS2 heterojunctions induce interface charge rearrangement for optimizing the adsorption free energy of intermediates, resulting in a low overpotential and high electrocatalytic activity. Specifically, an ultralow overpotential of 270 mV at 50 mA cm-2 for the oxygen evolution reaction (OER) is achieved. After adding 0.33 M urea into 1 M KOH, Fe-doped NiS-NiS2 obtains a strikingly reduced urea oxidation reaction potential of 1.36 V to reach 50 mA cm-2 , around 140 mV less than OER. This work provides insights into the synergistic modulation of electrocatalytic activity of non-noble catalysts for applications in energy conversion systems.

Encapsulating Fe2 O3 Nanotubes into Carbon-Coated Co9 S8 Nanocages Derived from a MOFs-Directed Strategy for Efficient Oxygen Evolution Reactions and Li-Ions Storage.

The development of high-efficiency, robust, and available electrode materials for oxygen evolution reaction (OER) and lithium-ion batteries (LIBs) is critical for clean and sustainable energy system but remains challenging. Herein, a unique yolk-shell structure of Fe2 O3 nanotube@hollow Co9 S8 nanocage@C is rationally prepared. In a prearranged sequence, the fabrication of Fe2 O3 nanotubes is followed by coating of zeolitic imidazolate framework (ZIF-67) layer, chemical etching of ZIF-67 by thioacetamide, and eventual annealing treatment. Benefiting from the hollow structures of Fe2 O3 nanotubes and Co9 S8 nanocages, the conductivity of carbon coating and the synergy effects between different components, the titled sample possesses abundant accessible active sites, favorable electron transfer rate, and exceptional reaction kinetics in the electrocatalysis. As a result, excellent electrocatalytic activity for alkaline OER is achieved, which delivers a low overpotential of 205 mV at the current density of 10 mA cm-2 along with the Tafel slope of 55 mV dec-1 . Moreover, this material exhibits excellent high-rate capability and excellent cycle life when employed as anode material of LIBs. This work provides a novel approach for the design and the construction of multifunctional electrode materials for energy conversion and storage.

Exemplar-based image completion using image depth information.

Image completion techniques are required to complete missing regions in digital images. A key challenge for image completion is keeping consistency of image structures without ambiguity and visual artifacts. We propose a novel method for image completion using image depth cue. Our method includes three major features. First, we compute the image gradient to improve image completion when searching for the most similar patches. Second, using image depth, we guide image completion by means of appropriate scale transformation. Third, we propose a global optimization patch-based method having gradient and depth features for image completion. Experiments demonstrate that our approach is a potentially superior method for completing missing regions.