Characterizing Grain Boundary Effects on Mg²⁺ Conduction in Metal-Organic Frameworks

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Next-generation materials for fast ion conduction are crucial for battery technology.
• Metal-organic frameworks (MOFs) show promise for ion conduction due to their structural diversity.
• Understanding grain boundary effects is essential for accurate prediction of ion conductivity in polycrystalline materials.

Purpose of the Study:

• To develop and validate a computational approach for modeling ion transport at grain boundaries in MOFs.
• To predict the contribution of grain boundaries to overall ion conductivity.
• To investigate Mg2+ conduction in Mg-MOF-74 thin films.

Main Methods:

• Computational modeling of ion transport at grain boundaries.
• Experimental guidance for structural characterization of MOF grain boundary interfaces.
• Development of a simplified MOF nanocrystal model combining bulk and grain boundary transport.
• Prediction of Mg2+ conductivity in Mg-MOF-74 thin films.

Main Results:

• The developed model accurately predicts Mg2+ conductivity in Mg-MOF-74 films within chemical accuracy.
• Strong Mg2+ binding at grain boundaries significantly inhibits conduction.
• Grain boundary effects can reduce ion conductivity by 2-3 orders of magnitude.
• Only specific grain boundary alignments facilitate fast Mg2+ transport.

Conclusions:

• The computation-aided platform provides molecular-level insights into grain boundary effects on ion conductivity.
• This approach enables quantitative prediction of ion conductivity in MOF-based conductors.
• The synergistic use of computation and experimental measurements can characterize MOF grain boundary composition.