Asymmetric Coordination Regulating D-Orbital Spin-Electron Filling in Single-Atom Iron Catalyst for Efficient Oxygen Reduction.

The single-atom Fe-N-C catalyst has shown great promise for the oxygen reduction reaction (ORR), yet the intrinsic activity is not satisfactory. There is a pressing need to gain a deeper understanding of the charge configuration of the Fe-N-C catalyst and to develop rational modulation strategies. Herein, we have prepared a single-atom Fe catalyst with the co-coordination of N and O (denoted as Fe-N/O-C) and adjacent defect, proposing a strategy to optimize the d-orbital spin-electron filling of Fe sites by fine-tuning the first coordination shell. The Fe-N/O-C exhibits significantly better ORR activity compared to its Fe-N-C counterpart and commercial Pt/C, with a much more positive half-wave potential (0.927 V) and higher kinetic current density. Moreover, using the Fe-N/O-C catalyst, the Zn-air battery and proton exchange membrane fuel cell achieve peak power densities of up to 490 and 1179 mW cm-2, respectively. Theoretical studies and in situ electrochemical Raman spectroscopy reveal that Fe-N/O-C undergoes charge redistribution and negative shifting of the d-band center compared to Fe-N-C, thus optimizing the adsorption free energy of ORR intermediates. This work demonstrates the feasibility of introducing an asymmetric first coordination shell for single-atom catalysts and provides a new optimization direction for their practical application.

Energy Spectrum CT Image Detection Based Dimensionality Reduction with Phase Congruency.

The image feature detection is widely used in image registration, image stitching and object recognition. The feature detection algorithm can be applied to the detection of artificial images, and can be used to detect the energy spectrum CT image. A new algorithm of phase consistency detection based on dimensionality reduction is proposed in this paper. We mainly focus on the phase congruency of the spectral CT images in the paper and try to use dimensionality reduction to integrate the information of phase congruency detected in the image. The experimental results show that the algorithm can detect the energy spectrum CT image with clear edge and contour, which is beneficial to the subsequent processing. Meanwhile, the algorithm presented is more effective in diagnosis of disease for medical professionals.