Peel and Penetration Resistance of Porous Polyethylene Terephthalate Material Produced by CO₂-Assisted Polymer Compression

Relationship between Permeability and Structure of CO2-Assisted Polymer Compression Products

Membrane filters were fabricated from polyethylene terephthalate nonwoven fabrics with an average fiber diameter of 8 μm using the CO₂-assisted polymer compression method. The filters were subjected to a liquid permeability test and structural analysis was performed using X-ray computed tomography to evaluate the tortuosity, pore size distribution, and percentage of open pores. Based on the results, filter tortuosity was proposed to be a function of porosity. Pore size estimated from the permeability test and X-ray computed tomography were in rough agreement. The ratio of open pores to all pores was as high as 98.5%, even at a porosity of 0.21. This may be due to the process of exhausting trapped high-pressure CO₂ after molding. For filter applications, a high open-pore ratio is desirable since it means that many pores are involved in the fluid flow. The CO₂-assisted polymer compression method was found to be suitable for the production of porous materials for filters.

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.

Novel Strategy for Fabricating Multilayer Porous Membranes with Varying Porosity

CO2-assisted polymer compression (CAPC) is a method for fabricating porous polymer materials in which the polymer is plasticized with CO2 and then pressed. In this work, a two-step molding method is adapted, and a porous membrane with multiple layers of varying porosity is fabricated by laminating sheets of a single starting material. A model is constructed in which the expansion owing to CO2 and compression reflected by the longitudinal elastic modulus are considered. The model is constructed based on a two-layer experiment and extended to three layers. From the model simulation, the conditions for fabricating a multilayer porous polymer membrane with three layers of varying porosity (0.6, 0.5, and 0.4) and identical thickness (0.6 mm) are calculated. Finally, a porous membrane with varying porosity is fabricated based on the simulated design.

Process Development of CO2-Assisted Polymer Compression for High Productivity: Improving Equipment and the Challenge of Numbering-Up

The CO2-assisted polymer compression method is used herein to prepare porous polymer materials by bonding laminated polymer fiber sheets using a piston in the presence of CO2. In this work, the CO2 flow line connections were moved from the pressure vessel to the piston to increase productivity, which makes the pressure vessel free-moving and the processing time of sample introduction and removal seemingly zero. In addition, a numbering-up method suitable for CO2-assisted polymer compression is proposed and verified based on the variability of the products. The variability of the product was evaluated using porosity, which is one of the most important properties of a porous material. It is found that the CO2 exhaust process, specific to this method, that uses high-pressure CO2, causes product variation, which can be successfully suppressed by optimizing the CO2 exhaust process.