Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research an...Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.展开更多
Removal of boric acid from seawater and wastewater using reverse osmosis membrane technologies is imperative and yet remains inadequately addressed by current commercial membranes.Existing research efforts performed p...Removal of boric acid from seawater and wastewater using reverse osmosis membrane technologies is imperative and yet remains inadequately addressed by current commercial membranes.Existing research efforts performed post-modification of reverse osmosis membranes to enhance boron rejection,which is usually accompanied by substantial sacrifice in water permeability.This study delves into the surface engineering of low-pressure reverse osmosis membranes,aiming to elevate boron removal efficiency while maintaining optimal salt rejection and water permeability.Membranes were modified by the self-polymerization and co-deposition of dopamine and polystyrene sulfonate at varying ratios and concentrations.The surfaces became smoother and more hydrophilic after modification.The optimum membrane exhibited a water permeability of 9.2±0.1 L·m^(-2)·h^(-1)·bar^(-1),NaCl rejection of 95.8%±0.3%,and boron rejection of 49.7%±0.1% and 99.6%±0.3% at neutral and alkaline pH,respectively.The water permeability is reduced by less than 15%,while the boron rejection is 3.7 times higher compared to the blank membrane.This research provides a promising avenue for enhancing boron removal in reverse osmosis membranes and addressing water quality concerns in the desalination process.展开更多
基金This work is supported by the National Key R&D Program of China(No.2022ZD0117501)the Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic Grant by the Agency for Science,Technology and Research(A*STAR)under grant no.A1898b0043Tsinghua University Initiative Scientific Research Program and Low Carbon En-ergy Research Funding Initiative by A*STAR under grant number A-8000182-00-00.
文摘Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.
基金the financial support by the Ministry of Education of Singapore via the Tier-1 project A-8000192-01-00.
文摘Removal of boric acid from seawater and wastewater using reverse osmosis membrane technologies is imperative and yet remains inadequately addressed by current commercial membranes.Existing research efforts performed post-modification of reverse osmosis membranes to enhance boron rejection,which is usually accompanied by substantial sacrifice in water permeability.This study delves into the surface engineering of low-pressure reverse osmosis membranes,aiming to elevate boron removal efficiency while maintaining optimal salt rejection and water permeability.Membranes were modified by the self-polymerization and co-deposition of dopamine and polystyrene sulfonate at varying ratios and concentrations.The surfaces became smoother and more hydrophilic after modification.The optimum membrane exhibited a water permeability of 9.2±0.1 L·m^(-2)·h^(-1)·bar^(-1),NaCl rejection of 95.8%±0.3%,and boron rejection of 49.7%±0.1% and 99.6%±0.3% at neutral and alkaline pH,respectively.The water permeability is reduced by less than 15%,while the boron rejection is 3.7 times higher compared to the blank membrane.This research provides a promising avenue for enhancing boron removal in reverse osmosis membranes and addressing water quality concerns in the desalination process.
