Ulrich Wiesner, the Spencer T. Olin Professor of Materials Science and Engineering at Cornell University, along with co-authors Lilly Tsaur and Fernando A. Escobedo, has demonstrated a novel ultrafiltration (UF) membrane fabrication technique capable of sorting molecules by chemical affinity. Published November 13 in Nature Communications, the Cornell Engineering team utilized chemically distinct block copolymer micelles—self-assembling polymer spheres—and applied machine learning segmentation to identify patterns within the membrane’s porous structure. This approach enables control over pore surface chemistry, offering a pathway to create UF membranes—previously limited to size-based separation—with customizable selectivity for complex mixtures like antibodies, potentially revolutionizing industrial filtration processes.
Chemically Diverse Membranes Created with Block Copolymers
Researchers at Cornell have developed a novel approach to ultrafiltration (UF) membrane creation, moving beyond size-based separation to chemical affinity. By blending distinct block copolymer micelles – nanoscale polymer spheres – they’ve created porous films with chemically diverse pore surfaces. This is achieved through controlled self-assembly, leveraging neutral and repulsive interactions between micelles. Initial results demonstrate the ability to incorporate up to three different block copolymers, tailoring pore chemistry for specific molecular separations – a critical advancement for biopharmaceutical manufacturing and beyond.
The key to this innovation lies in identifying micelle distribution within the membrane’s separation layer. Due to imaging limitations, the team employed machine learning to analyze subtle pore patterns revealed by scanning electron microscopy. This allowed them to map the location of each copolymer type. Complementary molecular simulations, utilizing coarse-grained modeling, further illuminated the self-assembly rules governing micelle organization, despite the system’s complexity and far-from-equilibrium conditions.
This research builds upon prior work in block copolymer self-assembly, previously commercialized by Terapore Technologies. The potential impact is significant: existing UF manufacturing processes can be adapted simply by changing the “recipe” of block copolymers used. This promises a paradigm shift in filtration, opening possibilities for affinity separations, smart coatings, and sensitive biosensors. The work was supported by the National Science Foundation and utilized Cornell’s materials research facilities.
Machine Learning Identifies Micelle Chemistry Patterns
Cornell researchers have demonstrated a pathway to ultrafiltration (UF) membranes capable of separating molecules based on chemical affinity, not just size. Published in Nature Communications, the team blended block copolymer micelles – nanoscale polymer spheres – during membrane fabrication. Crucially, machine learning was employed to analyze scanning electron microscopy images of the resulting pore structures. This allowed identification of subtle pattern differences revealing where each micelle type assembled, a task impossible with imaging alone, and revealed control over pore chemistry.
The innovation addresses a key limitation of current UF technology, which struggles to differentiate molecules of similar size but differing chemical structure—critical in biopharmaceutical manufacturing. By combining up to three distinct block copolymers, researchers controlled micelle self-assembly and pore chemistry. Molecular simulations, utilizing coarse-grained models, supported the experimental findings and explained the micelle organization. This approach builds on prior work at Cornell, leading to the startup Terapore Technologies.
This method offers a cost-effective route to chemically diverse membrane surfaces without expensive post-fabrication processing. Essentially, manufacturers can alter the “recipe” of existing UF processes to achieve affinity-based separations. Beyond filtration, the ability to program pore surface chemistry unlocks potential in smart coatings and biosensors. The research, supported by the NSF, represents a paradigm shift, moving UF beyond simple size exclusion towards functional separation based on molecular recognition.
New Approach Revolutionizes Ultrafiltration Technology
A new ultrafiltration (UF) technique developed at Cornell University promises a revolution in membrane technology, moving beyond size-based separation to chemical affinity. Researchers successfully created porous membranes by blending block copolymer micelles – nanoscale polymer spheres – during fabrication. This innovative approach allows for the creation of pores with diverse chemistries, enabling the separation of molecules with identical size and weight but differing chemical structures – a long-standing challenge in fields like biopharmaceutical manufacturing.
The key to this breakthrough lies in controlling micelle self-assembly. By combining up to three distinct block copolymers, the team demonstrated how neutral and repulsive interactions dictate the arrangement of different chemistries within the membrane’s surface pores. Identifying these arrangements proved difficult, requiring hundreds of scanning electron microscopy images and machine learning algorithms to map micelle locations. Molecular simulations, utilizing coarse-grained models, further illuminated the governing rules of this complex self-organization process.
This method offers a significant advantage over current post-fabrication chemical modification techniques, which are prohibitively expensive for industrial scale-up. Leveraging existing scalable block copolymer processes—already utilized by startup Terapore Technologies—companies can simply adjust the “recipe” to create membranes with chemically diverse pores. This promises a paradigm shift in UF operations, opening new possibilities for filtration, smart coatings, and highly sensitive biosensors.
