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Structure Control Using Bioderived Solvents in Electrochemical Metal-Organic Framework Synthesis

Meha Bhindi, Liam Massengo, James Hammerton, Matthew J. Derry, Stephen D. Worrall

2023Applied Sciences18 citationsDOIOpen Access PDF

Abstract

Electrochemical synthesis of metal-organic frameworks (MOFs) has proven to possess many environmental advantages over traditional synthesis methods such as reduced energy use and shorter reaction times. However, the use of toxic, fossil fuel derived solvents such as N,N-dimethylformamide (DMF) presents a challenge to the environmental credentials of this method that has yet to be dealt with. Here, we investigate bioderived solvents, CyreneTM and γ-valerolactone (GVL), as an alternative for the synthesis of a range of MOFs via the anodic deposition method. The obtained MOF materials are characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM) to confirm their identities and morphologies and for comparison with MOFs synthesized using the traditional DMF-based solvent systems. When using CyreneTM and GVL solvents, crystalline MOF materials were obtained of comparable quality to those afforded using DMF. However, in several cases, using CyreneTM or GVL led to the formation of less stable, higher porosity MOF structures than those obtained using DMF, indicating that the larger bio solvent molecules may also play a templating role during the synthesis. This study successfully demonstrates the first-time electrochemical synthesis of MOFs has been performed using bio solvents and has highlighted that the use of bio solvents can provide a route to obtaining lower density, higher porosity MOF phases than those obtained using traditional solvents.

Topics & Concepts

ElectrochemistryPorosityMetal-organic frameworkSolventMaterials scienceDimethylformamideChemical engineeringMetalNanotechnologyOrganic chemistryChemistryElectrodePhysical chemistryMetallurgyAdsorptionEngineeringComposite materialMetal-Organic Frameworks: Synthesis and ApplicationsCorrosion Behavior and InhibitionMachine Learning in Materials Science