3D printing of hierarchically porous MOF monoliths using silica nanocages as a hybrid binder for CO2 capture
Source abstract
Abstract
Metal-organic frameworks (MOFs), such as HKUST-1, are highly promising for carbon capture and storage due to their exceptional porosity and tunable properties. However, integrating MOFs into scalable, hierarchical structures, especially via 3D printing, has been challenging. Traditional 3D printing methods often rely on polymeric binders that block pore accessibility and require high-temperature processing, which degrades MOFs and limits their functionality. This work introduces a novel approach using digital light processing (DLP) 3D printing to fabricate MOF-containing monoliths with multiscale porosity. By replacing polymeric binders with methacrylate-functionalized silica nanocages, printable MOF/SiO 2 inks were obtained while preserving the microporosity and functionality of HKUST-1. Optimizing ink formulations enabled the printing of complex geometries, such as Gyroid structures, with high MOF content and high CO 2 affinity. The resulting MOF@SiO 2 composite materials have a hierarchical structure that combines the readily available micropores of the MOF with the mesopores of the silica scaffold and the macropores resulting from the geometrical design. This architecture ensures high accessibility to active sites, making the material ideal for flow-based application. Additionally, the method allows for post-printing functionalization, such as an additional in situ MOF growth, to further enhance porosity and sorption performance. This versatile approach overcomes the limitations for the printing of MOF-based materials, offering a transformative tool for designing advanced porous monolithic reactors.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Magnesium Oxide Properties and Applications · Polymer Foaming and Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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