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科研机构
合肥物质科学研究院 [16]
内容类型
期刊论文 [16]
发表日期
2022 [2]
2021 [2]
2020 [3]
2018 [3]
2017 [2]
2016 [2]
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Selenization governs the intrinsic activity of copper-cobalt complexes for enhanced non-radical Fenton-like oxidation toward organic contaminants
期刊论文
JOURNAL OF HAZARDOUS MATERIALS, 2022, 卷号: 435
作者:
Hong, Peidong
;
Zhang, Kaisheng
;
He, Junyong
;
Li, Yulian
;
Wu, Zijian
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2022/12/23
CoCuSe
Fenton-like
Non-radical degradation
Reaction mechanism
CTC
A new method for detecting mixed bacteria based on multi-wavelength transmission spectroscopy technology
期刊论文
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2022, 卷号: 270
作者:
Feng, Chun
;
Zhao, Nanjing
;
Yin, Gaofang
;
Gan, Tingting
;
Yang, Ruifang
收藏
  |  
浏览/下载:79/0
  |  
提交时间:2022/03/21
Spectral separation
Bacteria
Monte carlo
Principal component analysis
Backpropagation neural networks
Multi-wavelength transmission spectrum
Line identification of extreme ultraviolet (EUV) spectra from low-Z impurity ions in EAST tokamak plasmas
期刊论文
Plasma Science and Technology, 2021, 卷号: 23
作者:
LI,Lei
;
ZHANG,Ling
;
XU,Zong
;
MORITA,Shigeru
;
CHENG,Yunxin
收藏
  |  
浏览/下载:56/0
  |  
提交时间:2021/06/21
line identification
EUV spectroscopy
EUV spectra
impurity line emissions
tokamak plasmas
Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria
期刊论文
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2021, 卷号: 251
作者:
Feng, Chun
;
Zhao, Nanjing
;
Yin, Gaofang
;
Gan, Tingting
;
Yang, Ruifang
收藏
  |  
浏览/下载:109/0
  |  
提交时间:2021/04/26
Bacterial species
Identification
Artificial neural networks
Multi-wavelength transmission spectrum
Generalized regression neural network
Back propagation neural network
Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine
期刊论文
MOLECULES, 2020, 卷号: 25
作者:
Chen, Yunan
;
Yang, Ruifang
;
Zhao, Nanjing
;
Zhu, Wei
;
Chen, Xiaowei
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2020/12/28
concentration-emission matrix
PCA
genic algorithm
SVM
oil species recognition
Generic Approach to Boost the Sensitivity of Metal Oxide Sensors by Decoupling the Surface Charge Exchange and Resistance Reading Process
期刊论文
ACS APPLIED MATERIALS & INTERFACES, 2020, 卷号: 12
作者:
Dai, Tiantian
;
Meng, Gang
;
Deng, Zanhong
;
Chen, Ying
;
Liu, Hongyu
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2020/10/21
pulsed temperature modulation
micro-electromechanical systems sensor
resistance reading
sensitivity-enhancement
metal oxide semiconductor
Development of vacuum ultraviolet spectroscopy for measuring edge impurity emission in the EAST tokamak
期刊论文
PLASMA SCIENCE & TECHNOLOGY, 2020, 卷号: 22
作者:
Zhang Hongming
;
Lyu Bo
;
He Liang
;
Shen Yongcai
;
Chen Jun
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2020/11/26
EAST tokamak
impurity tungsten
vacuum ultraviolet spectroscopy
edge impurity emission
plasma diagnostics
Mechanistic understanding of polychlorinated biphenyls degradation by peroxymonosulfate activated with CuFe2O4 nanoparticles: Key role of superoxide radicals
期刊论文
CHEMICAL ENGINEERING JOURNAL, 2018, 卷号: 348, 期号: 无, 页码: 526-534
作者:
Qin, Wenxiu
;
Fang, Guodong
;
Wang, Yujun
;
Zhou, Dongmei
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2019/08/23
Peroxymonosulfate
Nano-CuFe2O4
Activation
PCBs
Reactive oxygen species
Multi-level learning features for automatic classification of field crop pests
期刊论文
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 卷号: 152, 期号: 无, 页码: 233-241
作者:
Xie, Chengjun
;
Wang, Rujing
;
Zhang, Jie
;
Chen, Peng
;
Dong, Wei
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/11
Pest classification
Unsupervised feature learning
Dictionary learning
Feature encoding
Microfluidic dielectrophoresis device for trapping, counting and detecting Shewanella oneidensis at the cell level
期刊论文
BIOSENSORS & BIOELECTRONICS, 2018, 卷号: 99, 期号: 无, 页码: 416-423
作者:
Chen, Xiangyu
;
Liang, Zhiting
;
Li, Daobo
;
Xiong, Ying
;
Xiong, Penghui
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2018/08/17
Microfluidic Chip
Dielectrophoresis
Shewanella Oneidensis
Fluorescence
Cell Level
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