Side group topological structure modified orbital and condensed state characteristics enhance the electrical anti-breakdown performance of polyolefin.

As an effective method to enhance the dielectric performance of polyolefin materials, polar side group modification has been extensively applied in the insulation and energy storage materials of electrical and electronic systems. In this work, two side groups with different topological structures were adopted, namely, vinyl acetate (VAc, aliphatic chain) and -vinyl-pyrrolidone (NVP, saturated ring), to modify polypropylene (PP) chemical grafting, and the effects of structural topology of the polar side group on the microscopic and macroscopic characteristics of PP, particularly on its electrical anti-breakdown ability, were investigated. Experimental results showed that the side group structural topology directly affected the crystallization and thermal properties of PP.

Deep learning for denoising in a Mueller matrix microscope.

The Mueller matrix microscope is a powerful tool for characterizing the microstructural features of a complex biological sample. Performance of a Mueller matrix microscope usually relies on two major specifications: measurement accuracy and acquisition time, which may conflict with each other but both contribute to the complexity and expenses of the apparatus. In this paper, we report a learning-based method to improve both specifications of a Mueller matrix microscope using a rotating polarizer and a rotating waveplate polarization state generator.