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Improvement on the electrical property of conductive polymer composites is dependent on the controllable dispersion of conductive additives in polymer matrices to form a conductive network. Here we show a segregated electrically conductive network is assembled in poly­(l-lactide)/poly­(ε-caprolactone)/multiwall carbon nanotubes (PLLA/PCL/MWCNTs) composites. First, the MWCNTs were dispersed in PCL to obtain the PCL/MWCNTs phase. Second, the PLLA particles were well coated with PCL/MWCNTs phase at 100 °C, which is between melting temperature of PLLA and PCL. Finally, the coated PLLA particles were compressed above the melting temperature of PLLA to form PCL/MWCNTs segregated structures. The morphological observation showed MWCNTs successful location in the continuous PCL phase, resulting in an ultralow percolation threshold of 0.0085 vol % MWCNTs. To our best knowledge, it is the lowest percolation threshold in PLLA- or PCL-based conductive composites at present. The composites with the segregated structure with only 0.05 wt % of MWCNTs loading achieved high electrical conductivity of 3.84 × 10–4 S/m. Furthermore, the composites with the segregated structure not only showed 10% higher Young’s modulus than that of the correspondingly conventional composites but also maintained high elongation at break and tensile strength. The samples with the segregated structure also show higher complex viscosity and lower crystallinity than that of the conventional composites because of the continuous PCL/MWCNTs network and the confined effects by this network.

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Adaptive optics techniques have been developed over the past half century and routinely used in large ground-based telescopes for more than 30 years. Although this technique has already been used in various applications, the basic setup and methods have not changed over the past 40 years. In recent years, with the rapid development of artificial intelligence, adaptive optics will be boosted dramatically. In this paper, the recent advances on almost all aspects of adaptive optics based on machine learning are summarized. The state-of-the-art performance of intelligent adaptive optics are reviewed. The potential advantages and deficiencies of intelligent adaptive optics are also discussed.