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Creators/Authors contains: "Cheng, Gong"

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  1. Abstract

    Semiconductor photoconductive switches are useful and versatile emitters of terahertz (THz) radiation with a broad range of applications in THz imaging and time-domain spectroscopy. One fundamental challenge for achieving efficient ultrafast switching, however, is the relatively long carrier lifetime in most common semiconductors. To obtain picosecond ultrafast pulses, especially when coupled with waveguides/transmission lines, semiconductors are typically engineered with high defect density to reduce the carrier lifetimes, which in turn lowers the overall power output of the photoconductive switches. To overcome this fundamental trade-off, here we present a new hybrid photoconductive switch design by engineering a hot-carrier fast lane using graphene on silicon. While photoexcited carriers are generated in the silicon layer, similar to a conventional switch, the hot carriers are transferred to the graphene layer for efficient collection at the contacts. As a result, the graphene-silicon hybrid photoconductive switch emits THz fields with up to 80 times amplitude enhancement compared to its graphene-free counterpart. These results both further the understanding of ultrafast hot carrier transport in such hybrid systems and lay the groundwork toward intrinsically more powerful THz devices based on 2D-3D hybrid heterostructures.

     
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  2. Abstract

    Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging.

     
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  3. Abstract

    The development of synthetic systems that enable the sustained active self‐assembly of molecular blocks to mimic the complexity and dynamic behavior of living systems is of great interest in elucidating the origins of life, understanding the basic principles behind biological organization, and designing active materials. However, it remains a challenge to construct microsystems with dynamic behaviors and functions that are connected to molecular self‐assembly processes driven by external energy. Here, an active self‐assembly of microdroplet protocells with dynamic structure and high structural complexity through living radical polymerization under constant energy flux is reported. The active microdroplet protocells exhibit nonlinear behaviors including oscillatory growth and shrinkage. This relies on the transient stabilization of molecular assembly, which can channel the inflow of energy through noncovalent interactions of pure synthetic components. The intercommunication of microdroplet protocells through stochastic fusion leads to the formation of a variety of dynamic and higher‐order biomimetic microstructures. This work constitutes an important step toward the realization of autonomous and dynamic microsystems and active materials with life‐like properties.

     
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