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Multilength Scale Hierarchy in Metal‐Organic Frameworks: Synthesis, Characterization and the Impact on Applications

Min Ying Tsang, Anna А. Sinelshchikova, Orysia Zaremba, Flora Schöfbeck, Alejandra Durán Balsa, Michael R. Reithofer, Stefan Wuttke, Jia Min Chin

2023Advanced Functional Materials31 citationsDOIOpen Access PDF

Abstract

Abstract Evolutionary selection in nature has led to hierarchical structuring as a fundamental optimization strategy for biological structures, maximizing functional performance while minimizing resource usage. Precise hierarchical organization of natural materials over a wide range of length scales gives rise to unique synergistic properties that could not be achieved by single components. Despite the clear advantages offered by hierarchically structuring matter, mastering hierarchical control based on the current synthetic toolbox is still a challenge. In this review, some recent advancements in the fabrication of hierarchical metal organic framework (MOF) materials are highlighted and the advantages that arise due to different kinds of MOF hierarchy are critically analyzed. The special focus of the review lies in highlighting the applications where MOF hierarchical materials can be most impactful and describing characterization techniques currently at the disposal of scientists for the precise characterization of MOF hierarchical structures across all length scales. Finally, the intent is to inspire reticular chemists to master hierarchical control of MOF materials so as to fully utilize the advantages MOFs offer for various applications.

Topics & Concepts

StructuringHierarchyCharacterization (materials science)Metal-organic frameworkNanotechnologyComputer scienceHierarchical organizationScale (ratio)Biochemical engineeringSelection (genetic algorithm)ToolboxMaterials scienceSystems engineeringEngineeringArtificial intelligencePhysicsOrganic chemistryChemistryEconomicsQuantum mechanicsMarket economyManagementProgramming languageAdsorptionFinanceMetal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials ScienceX-ray Diffraction in Crystallography
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