The semimetal Bi has received increasing interest as an alternative to noble metals for use in plasmonic photocatalysis. To enhance the photocatalytic efficiency of metallic Bi, Bi microspheres modified by SiO2 nanopa...The semimetal Bi has received increasing interest as an alternative to noble metals for use in plasmonic photocatalysis. To enhance the photocatalytic efficiency of metallic Bi, Bi microspheres modified by SiO2 nanoparticles were fabricated by a facile method. Bi-O-Si bonds were formed between Bi and SiO2, and acted as a transportation channel for hot electrons. The SiO2@Bi microspheres exhibited an enhanced plasmon-mediated photocatalytic activity for the removal of NO in air under 280 nm light irradiation, as a result of the enlarged specific surface areas and the promotion of electron transfer via the Bi-O-Si bonds. The reaction mechanism of photocatalytic oxidation of NO by SiO2@Bi was revealed with electron spin resonance and in situ diffuse reflectance infrared Fourier transform spectroscopy experiments, and involved the chain reaction NO -> NO2 -> NO3- with center dot OH and center dot O-2(-) radicals as the main reactive species. The present work could provide new insights into the in-depth mechanistic understanding of Bi plasmonic photocatalysis and the design of high-performance Bi-based photocatalysts. (C) 2017, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.展开更多
在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异...在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异常值检测能力。本研究采用遥感海浪有效波高数据,构建双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)模型对有效波高进行预测,结合阈值方法进行异常检测,与3σ准则法、孤立森林模型法、 LSTM模型法以及VAE-LSTM模型法进行异常检测精度比较,探究基于Bi-LSTM的质量控制模型在遥感海浪数据异常值检测方面的能力。试验结果表明,Bi-LSTM质量控制模型具有良好的异常值检测效果,其精准率、召回率、 F1分数和运行时间分别为91%、 93%、 92和3.35 s,综合评价效果最佳,可有效对遥感海浪数据进行质量控制。展开更多
基金supported by the National Natural Science Foundation of China(21501016,51478070,21406022,21676037)the National Key R&D Project(2016YFC0204702)+4 种基金the Innovative Research Team of Chongqing(CXTDG201602014)the Natural Science Foundation of Chongqing(cstc2016jcyjA 0481,cstc2015jcyjA 0061)the Science and Technology Project of Chongqing Education Commission(KJ1600625,KJ1500637)the Application and Basic Science Project of Ministry of Transport of People's Republic of China(2015319814100)the Innovative Research Project from CTBU(yjscxx2016-060-36)~~
文摘The semimetal Bi has received increasing interest as an alternative to noble metals for use in plasmonic photocatalysis. To enhance the photocatalytic efficiency of metallic Bi, Bi microspheres modified by SiO2 nanoparticles were fabricated by a facile method. Bi-O-Si bonds were formed between Bi and SiO2, and acted as a transportation channel for hot electrons. The SiO2@Bi microspheres exhibited an enhanced plasmon-mediated photocatalytic activity for the removal of NO in air under 280 nm light irradiation, as a result of the enlarged specific surface areas and the promotion of electron transfer via the Bi-O-Si bonds. The reaction mechanism of photocatalytic oxidation of NO by SiO2@Bi was revealed with electron spin resonance and in situ diffuse reflectance infrared Fourier transform spectroscopy experiments, and involved the chain reaction NO -> NO2 -> NO3- with center dot OH and center dot O-2(-) radicals as the main reactive species. The present work could provide new insights into the in-depth mechanistic understanding of Bi plasmonic photocatalysis and the design of high-performance Bi-based photocatalysts. (C) 2017, Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
文摘在遥感海浪数据质量控制研究中,由于数据的复杂与不规则性,传统质量控制方法对海浪数据单点异常值的检测具有一定局限性。深度学习具有强大的特征学习能力,在解决非线性复杂问题方面具有一定优势,将其应用在数据质量控制领域可以提高异常值检测能力。本研究采用遥感海浪有效波高数据,构建双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)模型对有效波高进行预测,结合阈值方法进行异常检测,与3σ准则法、孤立森林模型法、 LSTM模型法以及VAE-LSTM模型法进行异常检测精度比较,探究基于Bi-LSTM的质量控制模型在遥感海浪数据异常值检测方面的能力。试验结果表明,Bi-LSTM质量控制模型具有良好的异常值检测效果,其精准率、召回率、 F1分数和运行时间分别为91%、 93%、 92和3.35 s,综合评价效果最佳,可有效对遥感海浪数据进行质量控制。
