Structure–property relationships for styrene crosslinked polyesters. II. Glass transition temperature

1992 ◽  
Vol 44 (4) ◽  
pp. 653-661 ◽  
Author(s):  
V. Bellenger ◽  
B. Mortaigne ◽  
J. Verdu
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Structure Property ◽  
Structure Property Relationships

Structure-Property Relationships in Elastomer-Modified Terpolymer Systems

1971 ◽  
Vol 44 (1) ◽  
pp. 62-70
Author(s):  
E. M. Hagerman
Keyword(s):  
Molecular Weight ◽  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Impact Resistance ◽  
Acrylonitrile Butadiene Styrene ◽  
Matrix Composition ◽  
Structure Property ◽  
Constant Matrix ◽  
Izod Impact

Abstract A number of terpolymers, incorporating as the elastomer phase polybutadiene, polyisoprene, poly-2,3-dimethylbutadiene, poly(butadiene-co-styrene), and poly(butadiene-co-2-methyl-5-vinylpyridine), were studied. Matrices were composed of poly(styrene-co-aerylonitrile) (SAN), poly(α-methylstyrene-eo-acrylonitrile), and poly(styrene-co-acenaphthylene). At constant elastomer content and elastomer molecular weight in systems employing a SAN matrix, Izod impact resistance was found to vary inversely with rising elastomer-glass transition temperature. In systems of various matrix composition, using a polybutadiene elastomer, heat deflection temperatures were found to vary directly and impact resistance inversely with rising matrix-glass transition temperature. In acrylonitrile-butadiene-styrene (ABS), systems of constant matrix composition and elastomer content, varying the elastomer molecular weight from 0.6 to 2.6×105 resulted in increasing the Izod impact resistance from 0.67 to 12.8 ft-1b/in. of notch.

scholarly journals Predicting Polymer’s Glass Transition Temperature by A Chemical Language Processing Model

2021 ◽  
Author(s):  
Guang Chen ◽  
Lei Tao ◽  
Ying Li
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Language Processing ◽  
High Throughput Screening ◽  
Molecular Descriptors ◽  
Feature Representation ◽  
Structure Property ◽  
Repeat Units ◽  
Chemical Language

We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of polymer’s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient and simple. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point `*’. Results show that the trained model demonstrates reasonable prediction accuracy on unseen polymer’s Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer’s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer’s properties.

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