scholarly journals Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers

2019 ◽  
Vol 59 (12) ◽  
pp. 5013-5025 ◽  
Author(s):  
Ghanshyam Pilania ◽  
Carl N. Iverson ◽  
Turab Lookman ◽  
Babetta L. Marrone
2020 ◽  
Vol 188 ◽  
pp. 92-100 ◽  
Author(s):  
Edesio Alcobaça ◽  
Saulo Martiello Mastelini ◽  
Tiago Botari ◽  
Bruno Almeida Pimentel ◽  
Daniel Roberto Cassar ◽  
...  

2011 ◽  
Vol 217-218 ◽  
pp. 1606-1610
Author(s):  
Dong Jiang ◽  
Xiao Ran Zhang ◽  
Yan Mei Ma ◽  
Cheng You Ma

A series of random polysulfone/polyethersulfone (PSF/PES) copolymers were synthesized by the polycondensation of 4, 4'-isopropylidendiphenol, 4, 4΄-dihyolroxy diphenyl sulfone and 4, 4'-dichlorodiphenyl sulfone in the presence of K2CO3. We obtained a series of copolymers by changing the molar ratio of 4, 4΄-dihyolroxy diphenyl sulfone and 4, 4'-isopropylidendiphenol (it was marked as the ratio of S:A). The copolymers have the similar solubility with polyethersulfone. They also have high glass transition temperatures (Tg: 199°C~229°C) and 5% weight loss temperatures (4, 4'-isopropylidendiphenol: 4, 4΄-dihyolroxy diphenyl sulfone=1:1, Td5=497°C). At the same time the elongation at break is much higher than that of PES, while the tensile strength is a little lower than that of PES.


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