Regulating the Active Sites of Metal-Phthalocyanine at the Molecular Level for Efficient Water Electrolysis: Double Deciphering of Electron-Withdrawing Groups and Bimetallic.

Implementing a molecular modulation strategy for metallic phthalocyanines (MPc) without losing the activity of the metal center and inducing a multifunction characteristic in electrocatalytic remains a challenge. Herein, a series of 2D CuCo bimetallic polymerized phthalocyanine modified with strong electron-withdrawing groups (CuCoPc-g, g = F, Cl, Br, NO2 ) for water oxidation in the alkaline electrolyte is designed and simply synthesized. The experimental results testify that the bimetallic design can perform electronic adjustment once and introduce the second active sites to get bifunctional characteristics, and then the electronic structure of the active center can be regulated by electron-withdrawing groups for a second time to achieve the optimal state. These electrons that transfer in the active center of inner metal can generate space-charged regions and the design of the polymer can stabilize active site region to maintain long-term electrolytic stability and high activity. This study precisely regulates the electronic structure of MPc at the molecular level and provides insight into the multifunctional design of polymeric macrocyclic electrocatalysts.

Deciphering the Space Charge Effect of the p-n Junction between Copper Sulfides and Molybdenum Selenides for Efficient Water Electrolysis in a Wide pH Range.

Space charge transfer is crucial for an efficient electrocatalytic process, especially for narrow-band-gap metal sulfides/selenides. Herein, we designed and synthesized a core-shell structure which is an ultrathin MoSe2 nanosheet coated CuS hollow nanoboxes (CuS@MoSe2) to form an open p-n junction structure. The space charge effect in the p-n junction region will greatly improve electron mass transfer and conduction, and also have abundant active interfaces. It was used as a bifunctional electrocatalyst for water oxidation at a wide pH range. It exhibits a low overpotential of 49 mV for the HER and 236 mV for the OER at a current density of 10 mA·cm-2 in acidic pH, 72 mV for the HER and 219 mV at 10 mA·cm-2 for the OER in alkaline pH, and 62 mV for the HER and 230 mV at 10 mA·cm-2 for the OER under neutral conditions. The experimental results and density functional theory calculations testify that the p-n junction in CuS@MoSe2 designed and synthesized has a strong space charge region with a synergistic effect. The built-in field can boost the electron transport during the electrocatalytic process and can stabilize the charged active center of the p-n junction. This will be beneficial to improve the electrocatalytic performance. This work provides the understanding of semiconductor heterojunction applications and regulating the electronic structure of active sites.

Color disease leaf image segmentation using NAMS superpixel algorithm.

BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research. OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease. METHODS: First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced. RESULTS: Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge. CONCLUSIONS: The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area.

Brain MR image segmentation using NAMS in pseudo-color.

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

The brain MRI image sparse representation based on the gradient information and the non-symmetry and anti-packing model.

Nowadays, sparse representation has been widely used in Magnetic Resonance Imaging (MRI). The commonly used sparse representation methods are based on symmetrical partition, which have not considered the complex structure of MRI image. In this paper, we proposed a sparse representation method for the brain MRI image, called GNAMlet transform, which is based on the gradient information and the non-symmetry and anti-packing model. The proposed sparse representation method can reduce the lost detail information, improving the reconstruction accuracy. The experiment results show the superiority of the proposed transform for the brain MRI image representation in comparison with some state-of-the-art sparse representation methods.