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

Synthesis and physical properties of polyhydroxyalkanoate (PHA)-based block copolymers: A review

Polyhydroxyalkanoates (PHAs) are a group of natural polyesters that are synthesised by microorganisms. In general, their thermoplasticity and (in some forms) their elasticity makes them attractive alternatives to petrochemical-derived polymers. However, the high crystallinity of some PHAs - such as poly(3-hydroxybutyrate) (P3HB) - results in brittleness and a narrow processing window for applications such as packaging. The production of copolymeric PHA materials is one approach to improving the mechanical and thermal properties of PHAs. Another solution is the manufacture of PHA-based block copolymers. The incorporation of different polymer and copolymer blocks coupled to PHA, and the resulting tailorable microstructure of these block copolymers, can result in a step-change improvement in PHA-based material properties. A range of production strategies for PHA-based block copolymers has been reported in the literature, including biological production and chemical synthesis. Biological production is typically less controllable, with products of a broad molecular weight and compositional distribution, unless finely controlled using genetically modified organisms. By contrast, chemical synthesis delivers relatively controllable block structures and narrowly defined compositions. This paper reviews current knowledge in the areas of the production and properties of PHA-based block copolymers, and highlights knowledge gaps and future potential areas of research.

Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented Graph Convolutional Neural Networks

Establishing the structure-property relationship by machine learning (ML) models is extremely valuable for accelerating the molecular design of polymers. However, existing ML models for the polymers are subject to scarcity issues of training data and fewer variations of graph structures of molecules. In addition, limited works have explored the interpretability of ML models to infer the latent knowledge in the field of polymer science that could inspire ML-assisted molecular design. In this contribution, we integrate graph convolutional neural networks (GCNs) with data augmentation strategy to predict the glass transition temperature Tg of polymers. It is demonstrated that the data-augmented GCN model outperforms the conventional models and achieves a higher accuracy for the prediction of Tg despite a small amount of training data. Furthermore, taking advantage of molecular graph representations, the data-augmented GCN model has the capability to infer the importance of atoms or substructures from the understanding of Tg, which generally agrees with the experimental findings in the field of polymer science. The inferred knowledge of the GCN model is used to advise on the design of functional polymers with specific Tg. The data-augmented GCN model possesses prominent superiorities in the establishment of structure-property relationship and also provides an efficient way for accelerating the rational design of polymer molecules.

Synthesis and Characterisation of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)-b-poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Multi-Block Copolymers Produced Using Diisocyanate Chemistry

Bacterially derived polyhydroxyalkanoates (PHAs) are attractive alternatives to commodity petroleum-derived plastics. The most common forms of the short chain length (scl-) PHAs, including poly(3-hydroxybutyrate) (P3HB) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), are currently limited in application because they are relatively stiff and brittle. The synthesis of PHA-b-PHA block copolymers could enhance the physical properties of PHAs. Therefore, this work explores the synthesis of PHBV-b-PHBV using relatively high molecular weight hydroxy-functionalised PHBV starting materials, coupled using facile diisocyanate chemistry, delivering industrially relevant high-molecular-weight block copolymeric products. A two-step synthesis approach was compared with a one-step approach, both of which resulted in successful block copolymer production. However, the two-step synthesis was shown to be less effective in building molecular weight. Both synthetic approaches were affected by additional isocyanate reactions resulting in the formation of by-products such as allophanate and likely biuret groups, which delivered partial cross-linking and higher molecular weights in the resulting multi-block products, identified for the first time as likely and significant by-products in such reactions, affecting the product performance.

Modelling across Multiple Scales to Design Biopolymer Membranes for Sustainable Gas Separations: 1—Atomistic Approach

In this work, we assessed the CO2 and CH4 sorption and transport in copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV), which showed good CO2 capture potential in our previous papers, thanks to their good solubility–selectivity, and are potential biodegradable alternatives to standard membrane-separation materials. Experimental tests were carried out on a commercial material containing 8% of 3-hydroxyvalerate (HV), while molecular modelling was used to screen the performance of the copolymers across the entire composition range by simulating structures with 0%, 8%, 60%, and 100% HV, with the aim to provide a guide for the selection of the membrane material. The polymers were simulated using molecular dynamics (MD) models and validated against experimental density, solubility parameters, and X-ray diffraction. The CO2/CH4 solubility–selectivity predicted by the Widom insertion method is in good agreement with experimental data, while the diffusivity–selectivity obtained via mean square displacement is somewhat overestimated. Overall, simulations indicate promising behaviour for the homopolymer containing 100% of HV. In part 2 of this series of papers, we will investigate the same biomaterials using a macroscopic model for polymers and compare the accuracy and performance of the two approaches.

Molecular dynamics simulations of copolymer compatibilizers for polylactide/poly(butylene succinate) blends

The effects of the molecular architecture of polylactide (PLA)-poly(butylene succinate) (PBS) copolymers on their compatibilization efficiency in immiscible PLA/PBS blends were studied using atomistic molecular-dynamics simulations. The results showed that the diblock copolymer is the most efficient at mixing with PLA and PBS homopolymers and reducing the interfacial tension of the blend. Tensile properties of the compatibilized blends were examined using uniaxial deformation simulations. Deformation behaviours of the blends at the molecular level were revealed. Both the structure (i.e. linear and branch) and the monomer sequence of the copolymers were found to affect the tensile performance. The system with a diblock copolymer showed a much greater tensile strength and elastic modulus than the other compatibilized blends. Comparing between triblock and graft copolymers, the former yielded the blends with better tensile performance than the latter. Our results suggest that copolymers with a linear structure, especially the diblock one, could be good candidates for the compatibilizer of PLA/PBS blends.

Atomistic Modeling of Hydrogen and Oxygen Solubility in Semicrystalline PA-6 and HDPE Materials

Hydrogen is a clean and sustainable energy carrier which plays a major role in the transition of the global energy market to a less fossil fuel dependent future. Polymer-based materials are crucial in the production, storage, transportation, and energy extraction of hydrogen. More insights in the hydrogen-polymers interactions are required to guide material design and product development, especially for hydrogen solubility in polymers, which is crucial in many applications. The current study aims at rationalizing the determining factors of hydrogen solubility in two relevant polymers: polyamide-6 (PA-6) and high density polyethylene (HDPE). Based on atomistic molecular dynamics simulations and experimental data, we have reached several conclusions related to hydrogen and oxygen solubility in these two polymers. The crystal phases of PA-6 and HDPE are impenetrable to hydrogen and oxygen at elevated pressures, despite the small molecular size of hydrogen and oxygen. The practical implication for gas barrier applications is that polymer crystals act as impermeable obstacles and gas migration takes place primarily in the amorphous phase. Experimental hydrogen and oxygen solubilities in PA-6 and HDPE at elevated pressures can be predicted in a semiquantitative manner by molecular simulations. The discrepancies between experimental and predicted values could be attributed to neglect of the effect of crystal regions on the amorphous polymer domains. Although hydrogen is smaller than oxygen, it has been experimentally observed that hydrogen has a lower solubility in PA-6 and HDPE than oxygen. This observation has been confirmed by molecular simulations and attributed to the more favorable energetic interactions of oxygen with PA-6 and PE than of hydrogen. These interactions dominate the solubility behavior over the distribution of the accessible volume in the polymers.

Machine learning strategies for the structure-property relationship of copolymers

Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure-property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young's modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young's modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young's modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.