Research Progress in Energy Based on Polyphosphazene Materials in the Past Ten Years

A polyphosphazene elastomer containing 2,2,2-trifluoroethoxy groups as a dielectric in electrically responsive soft actuators

The adaptive structure and excellent actuation of dielectric elastomer actuators (DEAs) make them promising candidates for soft robotics, haptic interfaces and artificial muscles. A wide variety of elastomers have been synthesised and investigated as dielectrics. Inorganic polymers such as polysiloxanes and polyphosphazenes have a low glass transition temperature. While polydimethylsiloxane (PDMS) has made its way into DEAs, the latter has received little attention in this field. Here, we present a dielectric elastomer based on polyphosphazene modified with 2,2,2,-trifluoroethoxy groups as the dielectric, which exhibits a dielectric permittivity two times higher than polydimethylsiloxanes (PDMS), excellent elasticity and a high dielectric breakdown field. These properties enable fast, reliable actuation and higher electrostatic forces than conventional PDMS. The actuators can withstand repeated actuation cycles and are suitable for long-term reliability applications.

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes. Several polymers of interest were identified. The results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.