Utilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation
Lena Pilz, Carsten Natzeck, Jonas Wohlgemuth, Nina Scheuermann, Simon Spiegel, Simon Oßwald, Alexander Knebel, Stefan Bräse, Christof Wöll, Manuel Tsotsalas, Nicholaus Prasetya
Abstract
In this study, machine learning has been used to assist the fabrication of high-quality SURMOFs that are then further used as a template to fabricate polymer-based SURGEL membranes for gas separation.
Topics & Concepts
MembraneGas separationFabricationPolymerMaterials scienceSeparation (statistics)Quality (philosophy)Process engineeringComputer scienceNanotechnologyChemical engineeringArtificial intelligenceEngineeringChemistryMachine learningComposite materialPhysicsMedicineBiochemistryPathologyQuantum mechanicsAlternative medicineMembrane Separation and Gas TransportMetal-Organic Frameworks: Synthesis and ApplicationsMembrane Separation Technologies