(380g) Next Generation Macroporous Polymer Membrane with Low Channeling for Improved Selectivity

Microfiltration (MF) and ultrafiltration (UF) are two pressure-driven membrane technologies with applications in biotechnology, water, and wastewater treatment, chemical and petroleum, and food and beverage industries. Depending on the application, membranes used in these industries have a mean pore size rating ranging from 0.01- 10 µm and a vast majority of these microporous membranes are polymeric. Morphological characteristics of these microporous membranes such as porosity, pore size distribution (PSD) and pore connectivity are critical because they affect their performance (selectivity, capacity, recovery, and fouling propensity)¹. Polymer MF/UF membranes are synthesized by phase inversion (PI) process. In this synthesis technique, the temporal and spatial fluctuations in process conditions during the precipitation stage lead to poor control over the morphological structure. The resulting PSD is often log-normal and leads to poor selectivity due to large variation in pore size with subsequent channeling².

In this work, we studied the fluid flow and particle filtration behavior through commercial microfiltration and ultrafiltration membranes (PES, PVDF and regenerated cellulose) of different pore sizes. The 3D microstructures of these membranes were obtained through either reconstruction from a set of cross-sectional images obtained from focused ion bean scanning electron (FIB – SEM) microscopy or through data science-based 3D reconstruction from a single 2D cross sectional SEM image using the method described by Chamani et al.³ Image segmentation based pore-throat modeling⁴ was used to find the pore space characteristics such as equivalent pore diameter, pore throat diameter and coordination number. The pore throat diameter calculated from this method was in good agreement with the rated pore size of the membranes. In-silico filtration experiments were conducted using a computational fluid dynamics package computational fluid mechanics program (Filtration module, GeoDict, Math2Market GmbH, Kaiserslautern, Germany). To identify and quantify channeling, the regions in the pore space corresponding to velocity outliers were identified and connectivity parameters⁵ were calculated in the directions parallel and perpendicular to flow. To the best of our knowledge, such analysis has not been applied to study the transport characteristics of macoporous polymer membranes. Fast channels spanning as long as 6 times the pore size of the membranes could be observed in the pore space and a major fraction of the fluid and particles were transported through these few channels. This results in low surface area utilization which could be detrimental to selectivity in applications such as affinity-based membrane filtration.

To find microstructures that have controlled pore space characteristics with improved connectivity to minimize channeling, In-silico filtration experiments revealed that the flow through a new class of membranes called ‘fused microsphere membranes (FMM)’ was more uniform with reduced channeling. FMMs are currently being synthesized in our lab by a bottom-up approach⁶ using amine modified polystyrene nanoparticles crosslinked with glutaraldehyde and with a commercial polymer membrane as the support. Additionally, the pore size of these membranes can be tuned by controlling the nanoparticle diameter and dispersity. These membranes will be tested, and their performance will be compared with commercial polymer membranes.

References

(1) Belfort, G. Membrane Filtration with Liquids: A Global Approach with Prior Successes, New Developments and Unresolved Challenges. Angew. Chem. Int. Ed. 2019, 58 (7), 1892–1902. https://doi.org/10.1002/anie.201809548.

(2) Sorci, M.; Woodcock, C. C.; Andersen, D. J.; Behzad, A. R.; Nunes, S.; Plawsky, J.; Belfort, G. “Linking Microstructure of Membranes and Performance.” J. Membr. Sci. 2020, 594, 117419. https://doi.org/10.1016/j.memsci.2019.117419.

(3) Chamani, H.; Rabbani, A.; Russell, K. P.; Zydney, A. L.; Gomez, E. D.; Hattrick-Simpers, J.; Werber, J. R. Data-Science-Based Reconstruction of 3-D Membrane Pore Structure Using a Single 2-D Micrograph. J. Membr. Sci. 2023, 678, 121673. https://doi.org/10.1016/j.memsci.2023.121673.

(4) Rabbani, A.; Jamshidi, S.; Salehi, S. An Automated Simple Algorithm for Realistic Pore Network Extraction from Micro-Tomography Images. J. Pet. Sci. Eng. 2014, 123, 164–171. https://doi.org/10.1016/j.petrol.2014.08.020.

(5) Siena, M.; Iliev, O.; Prill, T.; Riva, M.; Guadagnini, A. Identification of Channeling in Pore-Scale Flows. Geophys. Res. Lett. 2019, 46 (6), 3270–3278. https://doi.org/10.1029/2018GL081697.

(6) Marchetti, P.; Mechelhoff, M.; Livingston, A. G. Tunable-Porosity Membranes From Discrete Nanoparticles. Sci. Rep. 2015, 5 (1), 17353. https://doi.org/10.1038/srep17353.