The Optimization of Dispersion and Application Techniques for Nanocarbon-Doped Mixed Matrix Gas Separation Membranes

In this work, supported cellulose acetate (CA) mixed matrix membranes (MMMs) were prepared and studied concerning their gas separation behaviors. The dispersion of carbon nanotube fillers were studied as a factor of polymer and filler concentrations using the mixing methods of the rotor–stator system (RS) and the three-roll-mill system (TRM). Compared to the dispersion quality achieved by RS, samples prepared using the TRM seem to have slightly bigger, but fewer and more homogenously distributed, agglomerates. The green γ-butyrolactone (GBL) was chosen as a polyimide (PI) polymer-solvent, whereas diacetone alcohol (DAA) was used for preparing the CA solutions. The coating of the thin CA separation layer was applied using a spin coater. For coating on the PP carriers, a short parameter study was conducted regarding the plasma treatment to affect the wettability, the coating speed, and the volume of dispersion that was applied to the carrier. As predicted by the parameter study, the amount of dispersion that remained on the carriers decreased with an increasing rotational speed during the spin coating process. The dry separation layer thickness was varied between about 1.4 and 4.7 μm. Electrically conductive additives in a non-conductive matrix showed a steeply increasing electrical conductivity after passing the so-called percolation threshold. This was used to evaluate the agglomeration behavior in suspension and in the applied layer. Gas permeation tests were performed using a constant volume apparatus at feed pressures of 5, 10, and 15 bar. The highest calculated CO2/N2 selectivity (ideal), 21, was achieved for the CA membrane and corresponded to a CO2 permeability of 49.6 Barrer.

Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. We interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. We interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively, orders of magnitude higher than currently available polymeric membranes. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

Preparation of Hollow Fiber Membranes Based On Poly(4-methyl-1-pentene) for Gas Separation

New hollow fiber gas separation membranes with a non-porous selective layer based on poly(4-methyl-1-pentene) (PMP) granules have been obtained using the solution-free melt spinning process. The influence of the preparation conditions on the geometry of the obtained samples was studied. It was found that a spin head temperature of 280 °C and a specific mass throughput of 103 g mm−2 h−1 are optimal to obtain defect-free, thin-walled hollow fibers in a stable melt spinning process, using the given spinneret geometry and a winding speed of 25 m/min. The gas permeability and separation properties of new fibers were studied using CO2/N2 and CO2/CH4 mixtures, and it was found that the level of gas selectivity characteristic of homogeneous polymer films can be achieved. The features of the gas mixture components permeability below and above the PMP glass transition temperature have been experimentally studied in the range of CO2 concentrations from 10 to 90% vol. The temperature dependences of the permeability of the CO2/CH4/N2 mixture through the obtained HF based on PMP have been investigated, and the values of the apparent activation energies of the permeability have been calculated, which make it possible to predict the properties of membrane modules based on the obtained membranes in a wide temperature range.

Cross-Linking and Evaluation of the Thermo-Mechanical Behavior of Epoxy Based Poly(ionic Liquid) Thermosets

Poly(ionic liquids) (PILs) and ionenes are polymers containing ionic groups in their repeating units. The unique properties of these polymers render them as interesting candidates for a variety of applications, such as gas separation membranes and polyelectrolytes. Due to the vast number of possible structures, numerous synthesis protocols to produce monomers with different functional groups for task-specific PILs are reported in literature. A difunctional epoxy-IL resin was synthesized and cured with multifunctional amine and anhydride hardeners and the thermal and thermomechanical properties of the networks were assessed via differential scanning calorimetry and dynamic mechanical analysis. By the selection of suitable hardeners, the glass transition onset temperature (Tg,onset) of the resulting networks was varied between 18 °C and 99 °C. Copolymerization of epoxy-IL with diglycidyl ether of bisphenol A (DGEBA) led to a further increase of the Tg,onset. The results demonstrate the potential of epoxy chemistry for tailorable PIL networks, where the hardener takes the place of the ligands without requiring an additional synthesis step and can be chosen from a broad range of commercially available compounds.