Novel Strategy for Fabricating Multilayer Porous Membranes with Varying Porosity

Effect of Crystallinity on Young’s Modulus of Porous Materials Composed of Polyethylene Terephthalate Fibers in the Presence of Carbon Dioxide

Carbon dioxide (CO₂)-assisted polymer compression method is used for plasticizing polymers with subcritical CO₂ and then crimping the polymer fibers. Given that this method is based on crimping after plasticization by CO₂, it is very important to know the degree of plasticization. In this study, heat treatment was gently applied on raw material fibers to obtain fibers with different degrees of crystallinity without changing the shape of the fibers. Simultaneously, two types of sheets were placed in a pressure vessel to compare the degree of compression and the degree of hardness. Furthermore, a model was used to derive the relative Young’s modulus of porous materials composed of polymer fibers with different degrees of crystallinity. In the model, the amount of strain was calculated according to the Young’s modulus as a function of porosity and reflected in compression. Young’s modulus of porous polymers in the presence of CO₂ has been shown to vary significantly with slight differences in crystallinity, indicating that extremely low crystallinity is significant for plasticizing the polymer by CO₂.

New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process

It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO2-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method.

Correlation between the Porosity and Permeability of a Polymer Filter Fabricated via CO2-Assisted Polymer Compression

A porous filter was fabricated by plasticizing polymer fibers with CO2, followed by pressing and adhering; then, its gas permeability, a basic physical property of filters, was measured using N2. The as-obtained filter was well compressed and expected to approximate a sintered porous material. Therefore, the fabricated filter was analyzed by applying the Darcy law, and the correlation between its gas permeability and porosity was clarified. The gas permeability decreased owing to both pore size and porosity reduction upon increasing the degree of compression, which is a feature of the CO2-assisted polymer compression method. In particular, without any contradiction of pore size data previously reported, the gas permeability was clearly determined by the filter porosity and pore size. This study can serve as a guide for designing filters via CO2-assisted polymer compression.