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Lifeng Cui,Qiulin Zhang,Chaochuang Yin,Shifei Kang,Zhigang Ge,Qineng Xia,Yangang Wang,Xi Li 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2019 NANO Vol.14 No.3
Water pollution caused by intensive use of organic dyes has become an increasingly serious problem recently. Green and efficient processes are desperately needed to remove persistent organic pollutants from waste waters. Herein, Ag nanoparticles loaded ZnO hollow microspheres were synthesized through a simple solvothermal method and used as a photocatalyst for dye degradation. The calculated band gap of Ag/ZnO — 5% (2.97 eV) is much narrower than that of pure ZnO (3.37 eV). The obtained Ag/ZnO samples show a remarkable photocatalytic activity in photodegradation of Rhodamine B (RhB) under simulated sunlight irradiation. The degradation efficiency of RhB for Ag/ZnO — 5% is 98.8% after 100 min irradiation while only 52.8% degradation rate is obtained over pure ZnO. The enhancement is attributed to the exposed active ZnO (001) plane and the surface plasmon resonance (SPR) effect of Ag nanoparticles that promote the separation of photogeneated electrons and holes.
Wang, Qiuhua,Kang, Mingyang,Yuan, Lifeng,Wang, Yunlu,Miao, Gongxun,Choo, Kim-Kwang Raymond Korean Society for Internet Information 2021 KSII Transactions on Internet and Information Syst Vol.15 No.7
Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.
( Qiuhua Wang ),( Mingyang Kang ),( Lifeng Yuan ),( Yunlu Wang ),( Gongxun Miao ),( Kim-kwang Raymond Choo ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.
Mun Seok Gyu,최형우,이종민,임재현,하장호,Kang Min-Jung,Kim Eun-Joong,Kang Lifeng,정봉근 나노기술연구협의회 2020 Nano Convergence Vol.7 No.10
We developed the microfluidic co-culture platform to study photothermal therapy applications. We conjugated folic acid (FA) to target breast cancer cells using reduced graphene oxide (rGO)-based functional nanomaterials. To characterize the structure of rGO-based nanomaterials, we analyzed the molecular spectrum using UV–visible and Fourier-transform infrared spectroscopy (FT-IR). We demonstrated the effect of rGO-FA-based nanomaterials on photothermal therapy of breast cancer cells in the microfluidic co-culture platform. From the microfluidic co-culture platform with breast cancer cells and human umbilical vein endothelial cells (HUVECs), we observed that the viability of breast cancer cells treated with rGO-FA-based functional nanomaterials was significantly decreased after near-infrared (NIR) laser irradiation. Therefore, this microfluidic co-culture platform could be a potentially powerful tool for studying cancer cell targeting and photothermal therapy.