Computational Studies to Understand the Effect of Polysulfamide Designs on Structure and Properties

Plastic waste is currently generated at a rate of 400 million tons per year. The amount of plastics accumulating in the environment is growing rapidly since the degradation time for the plastics on the surface is in order of micrometres per year. Due to rising environmental concerns, there is a need for environmentally friendly and sustainable alternatives to these common commodity plastics. One such commonly used commodity plastic is polyurea which is used in many applications such as concrete coating, waterproofing and anti-corrosion on ships and a protective material. The wide range of applications for polyurea is due to the flexibility of changing the backbone chemistries. This leads to the formation of different morphologies, which are driven by the hydrogen bonding between the urea groups. However, polyurea takes a long time to degrade. Thus, there is a quest for alternate polymers that have similar structural features as polyurea but can be degraded easily, making them sustainable and environmentally more friendly. Michaudel and coworkers have recently introduced one such alternate for polyurea called polysulfamide, where the carbonyl group in polyurea is replaced with a sulfamide group in polysulfamide. They have shown that polysulfamides exhibit high thermal stability, tunable glass transition temperature and are degradable in green aqueous conditions. These attractive properties and sulfamide’s chemical structure being analogous to urea, make polysulfamides a potential replacement for polyurea. To facilitate such replacement, we need a fundamental understanding of how varying polysulfamide design impacts its structure (chain-level and self-assembled domains) and physical properties, which require synergistic molecular modeling, simulations, and experiments are needed. To complement experiments from Michaudel and coworkers, we have developed a coarse-grained (CG) model for polysulfamide to use in molecular dynamics simulations to investigate how polysulfamide design impacts the hydrogen bonding induced self-assembly of polysulfamides. This computational approach is validated by comparing simulated structures’ positional and orientational order for varying polysulfamide designs to that observed in experiments using X-ray diffraction (XRD) and infrared (IR) spectroscopy. Ongoing work is focused on extending this computational approach to a larger set of design parameters, including polydispersity in molar masses. Using the simulated and experimental data, we will train machine learning models to establish a design-structure-property relationship and accelerate the understanding of polysulfamides.