Computer-Aided Polymer Design: Integrating Group Contribution and Molecular Dynamics

Tough polycyclooctene nanoporous membranes from etchable block copolymers

Porous materials with pore dimensions of the nanometer length scale are useful as nanoporous membranes. ABA triblock copolymers are convenient precursors to such nanoporous materials if the end blocks are easily degradable (e.g., polylactide or PLA), leaving nanoporous polymeric membranes (NPMs) if in thin film form. The membrane properties are dependent on midblock monomer structure, triblock copolymer composition, overall molar mass, and polymer processing conditions. Polycyclooctene (PCOE) NPMs were prepared using this method, with tunable pore sizes on the order of tens of nanometers. Solvent casting was shown to eliminate film defects and allowed achievement of superior mechanical properties over melt processing techniques, and PCOE NPMs were found to be very tough, a major advance over previously reported NPMs. Oxygen plasma etching was used to remove the surface skin layer to obtain membranes with higher surface porosity, membrane hydrophilicity, and flux of both air and water. This is a straightforward method to reliably produce highly tough NPMs with high levels of porosity and hydrophilic surface properties.

Modified Group Contribution Scheme to Predict the Glass-Transition Temperature of Homopolymers through a Limiting Property Dataset

Previous studies on glass-transition temperature (T _g) prediction mainly focus on developing diverse methods with higher regression accuracy, but very little attention has been paid to the dataset. Generally, a large range of T _g values of a specified polymer could be found in the literature but which one should be selected into a dataset merely depends on the implicit preference rather than a recognized and clear criterion. In this paper, limiting glass-transition temperature (T _g(∞)), a constant value obtained at the infinite number-average molecular weight M _n, was validated to be an adequate bridge index in the T _g prediction models. Furthermore, a new dataset containing 198 polymers was established to predict T _g(∞) using the improved group contribution method and it showed a good correlation (R ² = 0.9925, adjusted R ² = 0.9894). The method could also generate T _g-M _n curves by introducing the T _g(∞) function and provide more information to polymer scientists and engineers for material selection, product design, and synthesis.

Computational Approaches in Membrane Science and Engineering

Computational modelling and simulation form a consolidated branch in the multidisciplinary field of membrane science and technology [...]