Investigation of photocatalytic activity of Ag-rutile heterojunctions

Micro & nano letters

article

Xiaodong Zhu¹  Hui Liu¹  Juan Wang¹  Hualong Dai¹  Yu Bai³  Wei Feng¹  Shihu Han⁴ 

[1] College of Mechanical Engineering, Chengdu University;College of Materials and Chemistry & Chemical Engineering, Chengdu University of Technology;Zhongchao Great Wall Precious Metals Co. Ltd.;School of Materials Sciences and Engineering, Southwest University of Science and Technology

关键词: photodissociation;    titanium compounds;    silver;    adsorption;    ultraviolet spectra;    visible spectra;    nanofabrication;    X-ray diffraction;    sol-gel processing;    photocatalysis;    photocatalysts;    nanocomposites;    pure rutile;    sol–gel route;    composite photocatalyst activity;    rutile surface;    photocatalytic activity;    silver-rutile heterojunctions;    silver-rutile nanomaterials;    excessive silver particles;    electron-hole recombination;    light source absorption;    RhB molecule adsorption;    silver rutile;    Ag;    RhB;   

DOI  :  10.1049/mnl.2020.0253

学科分类：计算机科学（综合）

来源: Wiley

【 摘 要 】

Detection of anomaly and attack identification is some of the major concern in IoT domain in recent days. With the exponential use of IoT based infrastructure in every domain, threats and anomalies are amplifying adequately. Attacks such as malicious operations, spying, service denial etc. are the main cause for failure in IoT system. So, developing an efficient model to identify and decipher such complex problem is always been a challenging task. Rather some of the machine learning based models is developed to solve such problem, but due to highly nonlinear nature of the data, such methods seem to be failed to prove the efficacy. With the combination of several models, ensemble learning helps to enhance the performance of machine learning methods. As compared to any single method, the ensemble learning based models are highly predictable for large dimensional data. In this paper, an adaptive boosting based model has been proposed to identify the anomaly in IoT based environment. The performance of the proposed method is compared with several other competitive machine learning based methods and found to be superior with all the considered metrics.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND