Rational design of hierarchically structured electrocatalysts is particularly important for electrocatalytic oxygen reduction reaction (ORR). Here, ZIF-67 crystals are stringed on core-shell Ag@C nanocables using a coordinationmodulated process. Upon pyrolysis, Ag@C strings of Co nanoparticles embedded with three-dimensional porous carbon with beads-on-string hierarchical structures are developed. Due to the advantages of the rich electrochemical active sites of Co-based "beads" and the efficient electron transfer pathways via Ag@C "strings," the resultant NH 3

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Accurate and meticulous measurement is an important prerequisite to obtain the real surface information of samples in atomic force microscopy (AFM). A severe problem is the frequent occurrence of measurement errors, which are mainly caused by the nonlinearity of the probe driver, the temperature drift of the system and the tip characteristics. The measurement errors caused by probe tip are the main source of errors in AFM nanoscale measurements. The shape and state of AFM tip will distort the AFM image from the actual sample morphology. If the information about the probe is known, the measurement error caused by the probe tip can be greatly reduced. In order to obtain accurate AFM images, a new method based on geometric measurement model and blind tip reconstruction is proposed to eliminate tip-sample convolution in the measurement of grating samples. The static and dynamic characteristics of the AFM tip are described by four parameters: cone angle, curvature radius, scanning inclination angle and mounting inclination angle. Finally, the feasibility and effectiveness of the new calibration method are verified by evaluating the image reconstruction quality. In conclusion, the proposed method can effectively reconstruct accurate AFM images of the grating.

Atomic force microscope (AFM) is a powerful nanoscale instrument, which can obtain the true surface morphology of samples. There are strict requirements for the detailed features in AFM images, so it is necessary to mine the deep information in the images. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-quality images. For most cases, the original images of AFM are with low resolution. In order to get the more detailed texture and feature information as much as possible, a super-resolution convolutional neural network algorithm based on enhanced data set is proposed in AFM imaging. By learning the mapping relationship between low-resolution images and high-resolution images from the image database, the high-resolution image is finally obtained. Aiming at the problem of long scanning time and too small training database of AFM image, adaptive histogram equalization is used to expand and enhance the training set of AFM images. Compared with the traditional super-resolution methods, the subjective and objective evaluation of the reconstructed image verifies the feasibility of the proposed algorithm.