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Updated: Feb 10, 2026

Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior
Published on: March 8, 2024
Making Metal-Organic Cages Respond to Light for Efficient Adsorptive Separation
Guoliang Liu1, Ze-Jiu Diao1, Zhou-Rui Yan1
1State Key Laboratory of Materials-Oriented Chemical Engineering, Jiangsu National Synergetic Innovation Center for Advanced Material (SICAM), College of Chemical Engineering, Nanjing Tech University, Nanjing 211816, China.
None:
Adsorptive separation, widely used in chemical and other industries, demands the regulation of adsorption capacity via temperature or pressure swings. However, energy consumption in temperature and pressure swings is high, and it is extremely desirable to develop approaches that go beyond traditional methods of modulating adsorption capacity. Inspired by nature, photoresponsive metal-organic cages (PMOCs) have been designed and attracted much attention. They are smart materials that reversibly isomerize under light, enabling on-demand, energy-efficient adsorptive separations through reversible modulation of their uptake capacity. This perspective highlights the advancement of the design and synthesis of PMOCs with different photoresponsive functional groups. Then, we focus on the discussion of several design strategies, including the hierarchical self-assembly of PMOCs into photoresponsive supramolecular frameworks, dispersion of PMOCs in host materials, and construction of PMOC-based photoresponsive porous liquids, for improving the photoresponsive regulation efficiency. Representative demonstration of controllable carbon dioxide, propylene, organic molecules, anions, and dye uptake and release is highlighted to illustrate the utility of these PMOC-based porous materials. Finally, we sorted out the problems that hinder the practical application of PMOC-based porous materials and underscore the potential acceleration in PMOC research and development through accelerating PMOC discovery, deepening mechanistic insight, and extending application scenarios.
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