Composition and Configuration Dependence of Glass-Transition Temperature in Binary Copolymers and Blends of Polyhydroxyalkanoate Biopolymers

Abstract

Polyhydroxyalkanoates (PHAs), a promising class of biomaterials, have gained considerable attention to replace petroleum-based plastics owing to their excellent biocompatibility and biodegradability. Homopolymers of PHA suffer from poor tunability in thermal and mechanical properties. Going from homopolymers to copolymers, the design space can be substantially enhanced by combining two or more monomers in different compositions (i.e., relative ratios of the different monomers) and configurations (i.e., relative positions of the different monomers in the polymer backbone) leading to a substantially large chemical space where application-specific optimization for the targeted functionality can be performed. However, this composition and configuration dependence of properties in the vast PHA copolymer chemical space remains largely unexplored. In this contribution, further building on our past work with PHA homopolymers, we systematically explore these chemical trends for glass-transition temperature (T g) in PHA copolymers and blends. Our molecular dynamics simulations, utilizing a previously validated force field for PHAs, suggest that these trends are largely governed not only by the homopolymer T g values but also configuration-dependent interchain interactions in the copolymer system. In particular, our results indicate that the configuration-dependent variation in the target property at a fixed composition can be significant in the presence of hydrogen-bond-forming monomers. These qualitative observations are further rationalized by quantitatively analyzing various closely related atomic level descriptors of copolymers and blends such as monomer mobility, number of hydrogen bonds, and pair correlation functions. The findings presented in this work help to develop a deeper atomistic-level understanding of thermomechanical behavior of PHA-based copolymers and can potentially guide the rational design of biopolymers as environmental-friendly functional materials.

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

“…The chemical design space can be substantially increased going from homopolymers to copolymers by combining two or three monomers with different compositions and configurations. In our previous work [30], we have systematically explored the compositionand configuration-dependence of T g in binary copolymers and blends of PHAs. Predictions from simulations were well in agreement with the estimates obtained using the analytical Fox Equation [67].…”

“…Molecular dynamics (MD) simulations using physics-based models have been proven highly efficient in predicting a range of polymer properties [29][30][31][32]. MD simulations were used in studying the miscibility of PHA and polylactide (PLA) polymers [33] and have also been employed in studying the mechanical properties of linear polymers [34], branched polymers [35], crosslinking polymers [36], and even the properties of the graphene-polymer interface [37,38].…”

“…These parameters were employed to represent the underlying potential energy surface for PHA molecules. We employed MD simulations to predict the glass transition temperature (T g ) of PHA homopolymers [29] and also systematically explored the compositional-and configurational-dependence of T g in binary copolymers and blends of PHA polymers [30]. In this contribution, we extend our past work to study the mechanical properties of PHA-based polymers.…”

Bioplastic Design using Multitask Deep Neural Networks

“…3,6,7 Diverse chemistries harbored in PHAs span a large property space with ample opportunities to design mechanical and thermal properties such as the Young's modulus (E), tensile strength (σ), elongation ( ), glass transition temperature (T g ), melting temperature (T m ), and degradation temperature (T d ). 3,[8][9][10][11][12][13] A large search space is created by combining 540 polyhydroxyalkanoates (PHAs) and 13 conventional polymers to copolymers. Property predictors and property requirements of commonly used polymers allow us to identify bioplastic candidates within the search space.…”

Bioplastic Design using Multitask Deep Neural Networks

“…3,6,7 Diverse chemistries harbored in PHAs span a large property space with ample opportunities to design mechanical and thermal properties such as the Young's modulus (E), tensile strength (σ), elongation (ǫ), glass transition temperature (T g ), melting temperature (T m ), and degradation temperature (T d ). 3,[8][9][10][11][12][13] A large search space is created by combining 540 polyhydroxyalkanoates (PHAs) and 13 conventional polymers to copolymers. Property predictors and property requirements of commonly used polymers allow us to identify bioplastic candidates within the search space.…”