Browsing by Author "Shih, Wei-Chuan"

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    Nanoporous Metals: From Plasmonic Properties to Applications in Enhanced Spectroscopy and Photocatalysis
    (American Chemical Society, 2021) Koya, Alemayehu Nana; Zhu, Xiangchao; Ohannesian, Nareg; Yanik, A. Ali; Alabastri, Alessandro; Proietti Zaccaria, Remo; Krahne, Roman; Shih, Wei-Chuan; Garoli, Denis
    The field of plasmonics is capable of enabling interesting applications in different wavelength ranges, spanning from the ultraviolet up to the infrared. The choice of plasmonic material and how the material is nanostructured has significant implications for ultimate performance of any plasmonic device. Artificially designed nanoporous metals (NPMs) have interesting material properties including large specific surface area, distinctive optical properties, high electrical conductivity, and reduced stiffness, implying their potentials for many applications. This paper reviews the wide range of available nanoporous metals (such as Au, Ag, Cu, Al, Mg, and Pt), mainly focusing on their properties as plasmonic materials. While extensive reports on the use and characterization of NPMs exist, a detailed discussion on their connection with surface plasmons and enhanced spectroscopies as well as photocatalysis is missing. Here, we report on different metals investigated, from the most used nanoporous gold to mixed metal compounds, and discuss each of these plasmonic materials’ suitability for a range of structural design and applications. Finally, we discuss the potentials and limitations of the traditional and alternative plasmonic materials for applications in enhanced spectroscopy and photocatalysis.
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    Oil Spill Sensor using Multispectral Infrared Imaging via L1 Minimization
    (2010-11) Li, Yingying; Shih, Wei-Chuan; Han, Zhu; Yin, Wotao
    Early detection of oil spill events is the key to environmental protection and disaster management. Current technology lacks the sensitivity and specificity in detecting the early onset of a small-scale oil spill event. Based on an infrared oil-water contrast model recently developed, we propose a novel nonscanning computational infrared sensor that has the potential to achieve unprecedented detection sensitivity. Such a system can be very low-cost and robust for automated outdoor operations, leading to massive offshore deployment. Taking advantage of the characteristic oil thickness multispectral signatures, we have streamlined an algorithm that incorporates 3D image reconstruction and classification in a single inversion step capitalizing on the benefits of L1 minimization.