Resonance tuning effect using nanoparticle cluster configuration in the infrared regime

2020 ◽  
Vol 49 (3) ◽  
pp. 311-314
Author(s):  
Manh Cuong Tran ◽  
Van Hai Pham ◽  
Dac Tuyen Le ◽  
Dinh Lam Vu ◽  
Huy Hoang Luc
Nanophotonics ◽  
2020 ◽  
Vol 9 (14) ◽  
pp. 4233-4252
Author(s):  
Yael Gutiérrez ◽  
Pablo García-Fernández ◽  
Javier Junquera ◽  
April S. Brown ◽  
Fernando Moreno ◽  
...  

AbstractReconfigurable plasmonics is driving an extensive quest for active materials that can support a controllable modulation of their optical properties for dynamically tunable plasmonic structures. Here, polymorphic gallium (Ga) is demonstrated to be a very promising candidate for adaptive plasmonics and reconfigurable photonics applications. The Ga sp-metal is widely known as a liquid metal at room temperature. In addition to the many other compelling attributes of nanostructured Ga, including minimal oxidation and biocompatibility, its six phases have varying degrees of metallic character, providing a wide gamut of electrical conductivity and optical behavior tunability. Here, the dielectric function of the several Ga phases is introduced and correlated with their respective electronic structures. The key conditions for optimal optical modulation and switching for each Ga phase are evaluated. Additionally, we provide a comparison of Ga with other more common phase-change materials, showing better performance of Ga at optical frequencies. Furthermore, we first report, to the best of our knowledge, the optical properties of liquid Ga in the terahertz (THz) range showing its broad plasmonic tunability from ultraviolet to visible-infrared and down to the THz regime. Finally, we provide both computational and experimental evidence of extension of Ga polymorphism to bidimensional two-dimensional (2D) gallenene, paving the way to new bidimensional reconfigurable plasmonic platforms.


2021 ◽  
Vol 2021 (6) ◽  
Author(s):  
Song He ◽  
Zhenjie Li ◽  
Qinglin Yang

Abstract We study cluster algebras for some all-loop Feynman integrals, including box-ladder, penta-box-ladder, and double-penta-ladder integrals. In addition to the well-known box ladder whose symbol alphabet is $$ {D}_2\simeq {A}_1^2 $$ D 2 ≃ A 1 2 , we show that penta-box ladder has an alphabet of D3 ≃ A3 and provide strong evidence that the alphabet of seven-point double-penta ladders can be identified with a D4 cluster algebra. We relate the symbol letters to the u variables of cluster configuration space, which provide a gauge-invariant description of the cluster algebra, and we find various sub-algebras associated with limits of the integrals. We comment on constraints similar to extended-Steinmann relations or cluster adjacency conditions on cluster function spaces. Our study of the symbol and alphabet is based on the recently proposed Wilson-loop d log representation, which allows us to predict higher-loop alphabet recursively; by applying it to certain eight-point and nine-point double-penta ladders, we also find D5 and D6 cluster functions respectively.


2020 ◽  
Vol 32 (51) ◽  
pp. 2002208
Author(s):  
Hyun‐Cheol Song ◽  
Sun‐Woo Kim ◽  
Hyun Soo Kim ◽  
Dong‐Gyu Lee ◽  
Chong‐Yun Kang ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.


2014 ◽  
Vol 25 (23) ◽  
pp. 235303 ◽  
Author(s):  
Manohar Chirumamilla ◽  
Anisha Gopalakrishnan ◽  
Andrea Toma ◽  
Remo Proietti Zaccaria ◽  
Roman Krahne

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