Neural Network Based Temperature-Dependent Quantitative Structure Property Relations (QSPRs) for Predicting Vapor Pressure of Hydrocarbons

2001 ◽  
Vol 41 (2) ◽  
pp. 463-477 ◽  
Author(s):  
Denise Yaffe ◽  
Yoram Cohen
Keyword(s):  
Neural Network ◽  
Vapor Pressure ◽  
Quantitative Structure ◽  
Structure Property ◽  
Temperature Dependent ◽  
Property Relations

Micro-Raman Study of Self-Assembled Nanostructures: (1−x)PZN:xPT Solid Solution

1999 ◽  
Vol 581 ◽  
Author(s):  
S. Gupta ◽  
R. S. Katiyar ◽  
R. Guo ◽  
A. S. Bhalla
Keyword(s):  
Lead Titanate ◽  
Relaxor Ferroelectrics ◽  
Structure Property ◽  
Temperature Dependent ◽  
Property Relations ◽  
Polarization Mechanism ◽  
Raman Study ◽  
Self Assembled ◽  
Polarized Raman ◽  
Temperature Dependent Study

ABSTRACTRelaxor ferroelectrics are one of the important classes of self-assembled nanostructure composite materials. Interesting features associated with the nanoregions give rise to the most interesting device related characteristics and unusual properties in these materials. Besides, they possess the largest property coefficients by themselves or when modified with lead titanate (PT). In this report, a detailed temperature dependent study has been carried out on (1-x)PZN-xPT relaxors with compositions x = 0.05 and 0.085 using polarized Raman scattering under optical and E-field variables and inferred the structure-property relations in order to obtain information to characterize the material for matching the application criteria. In addition, phase transitions associated with the relaxors have also been investigated to understand the polarization mechanism(s) for the unpoled and poled specimens.

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