Prediction of solubility for polycyclic aromatic hydrocarbons in supercritical carbon dioxide using wavelet neural networks in quantitative structure property relationship

2004 ◽  
Vol 32 (1-3) ◽  
pp. 73-78 ◽  
Author(s):  
T. Khayamian ◽  
M. Esteki
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Supercritical Carbon Dioxide ◽  
Aromatic Hydrocarbons ◽  
Quantitative Structure ◽  
Structure Property ◽  
Wavelet Neural Networks ◽  
Structure Property Relationship ◽  
Polycyclic Aromatic

Aggregation Effects on Photophysical Properties of NBN-Doped Polycyclic Aromatic Hydrocarbons: A Theoretical Study

2021 ◽  
Author(s):  
Yi Zeng ◽  
Junfang Yang ◽  
Xiaoyan Zheng
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Theoretical Study ◽  
Photophysical Properties ◽  
Structure Property ◽  
Vital Importance ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Polycyclic Aromatic ◽  
Boron Nitrogen

To realize the precise munipulation of the optoelectrical properties of boron–nitrogen (B–N) unit-doped Polycyclic aromatic hydrocarbons (PAHs), unraveling the structure-property relationship behind is of vital importance. In this work, we...

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