scholarly journals Decomposition of Nitrous Oxide over Cu/TiO2 Catalysts: The Effect of Cu Loading, TiO2 Structure, and Reaction Conditions

2019 ◽  
Vol 1 (2) ◽  
pp. 99
Author(s):  
K. Yanagida ◽  
W. Kurniawan ◽  
C. Salim ◽  
H. Hinode
Keyword(s):  
Nitrous Oxide ◽  
Catalytic Activity ◽  
Water Vapor ◽  
N2o Decomposition ◽  
Rutile Structure ◽  
Influence Of Water ◽  
Tio2 Support ◽  
Cu Catalyst ◽  
The Relationship ◽  
Decomposition Of Nitrous Oxide

Decomposition of nitrous oxide (N2O) over titania (TiO2) supported copper (Cu) catalyst was investigated with the existence of oxygen and water vapor. The catalytic activity of TiO2 was promoted by copper loading. It was found that there are optimum levels of copper loading on TiO2, and these values are correlated to the specific surface area of TiO2 support being used. The relationship between the catalytic activity for decomposition of N2O and the crystal structure of TiO2 was also investigated. The result revealed that Cu/TiO2 catalysts with the rutile structure has a higher activity toward N2O decomposition than those with the anatase structure. In this research, Cu(5wt%)/TiO2 prepared from TiO2 JRC-TIO-4 (reference catalyst provided by Catalysis Society of Japan) which was mainly constituted of rutile showed the highest activity for N2O decomposition and it could decompose N2O completely at 650℃. The catalytic activity was inhibited by the existence of oxygen. However, there was no influence of water vapor to the catalytic activity of Cu/TiO2 for N2O decomposition. 

Direct N2O decomposition over Yb2O3-CuO catalysts with C-type cubic structure

2020 ◽  
Vol 13 (08) ◽  
pp. 2050040
Author(s):  
Chang-Min Cho ◽  
Naoyoshi Nunotani ◽  
Nobuhito Imanaka
Keyword(s):  
Nitrous Oxide ◽  
Catalytic Activity ◽  
Water Vapor ◽  
N2o Decomposition ◽  
Redox Properties ◽  
High Durability ◽  
Oxygen Gas ◽  
Cubic Structure ◽  
Oxide Ion

Novel Yb2O3-CuO catalysts with C-type cubic structure were synthesized for direct nitrous oxide (N2O) decomposition. By introducing Cu[Formula: see text] ions into Yb2O3 lattice, the enhancement in the catalytic activity was recognized owing to the improvement of redox properties and the generation of oxide ion vacancies. Among the catalysts prepared, the highest activity was obtained for the (Yb[Formula: see text]Cu[Formula: see text]O[Formula: see text] catalyst, and the complete N2O decomposition was achieved at the temperature as low as 400[Formula: see text]C. In addition, the catalyst showed high durability for co-existing of oxygen gas and water vapor.

scholarly journals Low-Temperature Synthesis and Catalytic Activity of Cobalt Ferrite in Nitrous Oxide (N2O) Decomposition Reaction

Catalysts ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 889
Author(s):  
Kristina Denisova ◽  
Alexander A. Ilyin ◽  
Ruslan Rumyantsev ◽  
Julia Sakharova ◽  
Alexander P. Ilyin ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Catalytic Activity ◽  
Low Temperature ◽  
Cobalt Ferrite ◽  
Mechanochemical Activation ◽  
Solid Phase ◽  
N2o Decomposition ◽  
Nitrogen Adsorption ◽  
Decomposition Reaction ◽  
Nitrous Oxide Decomposition

Cobalt ferrite (CoFe2O4) nanoparticles were synthesized and investigated as a catalyst in the reaction of nitrous oxide (N2O) decomposition. Cobalt ferrite was synthesized by solid–phase interaction at 1100 °C and by preliminary mechanochemical activation in a roller-ring vibrating mill at 400 °C. The nanoparticles were characterized by X-ray diffraction (XRD), synchronous thermal analysis (TG and DSC) and scanning electron microscopy (SEM). A low-temperature nitrogen adsorption/desorption test was used to evaluate the catalytic activity of the cobalt ferrite nanoparticles. Correlations between the structure and catalytic properties of the catalysts are reported. The highest catalytic activity of CoFe2O4 in the reaction of nitrous oxide decomposition was 98.1% at 475 °C for cobalt ferrite obtained by mechanochemical activation.

The catalytic activity of metallic and deposited oxide catalysts in the decomposition of nitrous oxide

2007 ◽  
Vol 81 (6) ◽  
pp. 895-900 ◽  
Author(s):  
T. P. Gaidei ◽  
A. I. Kokorin ◽  
N. Pillet ◽  
M. E. Srukova ◽  
E. S. Khaustova ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Catalytic Activity ◽  
Oxide Catalysts ◽  
Decomposition Of Nitrous Oxide

Concomitant Polymorphs: An Alternative to Modulate Oxygen Evolution Performance of Mononuclear Nickel Complex

2021 ◽  
Author(s):  
Chen Hu ◽  
Qing-Qing Huang ◽  
Haibing Xu ◽  
Yuexing Zhang ◽  
Xu Peng ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Oxygen Evolution ◽  
Nickel Complex ◽  
Crystal Packing ◽  
The Relationship

The availability of polymorphs of metallic complexes provides an opportunity to reveal the relationship between crystal packing and catalytic activity. Herein, we immobilize two stable concomitant polymorphs (green NiL2-G and...

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

Sign in / Sign up

Export Citation Format

Share Document