This article discusses a study where researchers from China used artificial intelligence (AI) to enhance the efficiency of helium extraction through membrane-based separation. They explored the structure-performance relationships and separation mechanisms of metal-organic framework (MOF)-based membranes, identifying pore limiting diameter and void fraction as key factors for selectivity and permeability. This approach, according to lead investigator Zhengqing Zhang, unveils hidden mechanisms and insights, potentially revolutionizing material development in gas separation. The findings are published in Green Chemical Engineering and highlight the intersection of AI and material science for technological advancements. For more information, please consult the article link below:
Related topics
Topic | Replies | Views | Activity | |
---|---|---|---|---|
Machine learning discovers 'hidden-gem' materials for heat-free gas separation | 0 | 1 | August 5, 2024 | |
Rubbery organic frameworks (ROFs) toward ultrapermeable CO2-selective membranes | 0 | 1 | November 18, 2024 | |
Scientist advance simulation of metal-organic frameworks with machine learning | 0 | 23 | June 7, 2024 | |
Adaptive healing of stress-induced dynamic cracks in a metal-organic framework membrane using nanoparticles | 0 | 2 | August 5, 2024 | |
Scientists investigate in-situ growth of crown ether@UiO-66 membranes under mild conditions | 0 | 1 | July 25, 2024 |