MOFs to Enhance Green NH₃ Synthesis in Plasma Reactors: Hierarchical Computational Screening Enhanced by Iterative...

Area of Science:

• Materials Science and Engineering
• Chemical Engineering
• Computational Chemistry

Background:

• Plasma reactors offer a promising route for decarbonizing ammonia (NH3) production.
• Current NH3 energy yields in plasma reactors require improvement for widespread adoption.
• Protecting NH3 from plasma-induced destruction using porous materials is an emerging strategy.

Purpose of the Study:

• To computationally screen a large database of metal-organic frameworks (MOFs) for NH3 adsorption applications.
• To identify MOFs suitable as catalyst supports or membranes to enhance NH3 synthesis in plasma reactors.
• To develop data-driven guidelines for designing MOFs for improved NH3 production.

Main Methods:

• Developed a hierarchical screening strategy combining machine learning (ML) and molecular simulations.
• Initial screening of 13,460 MOFs based on NH3 adsorption Henry's constants.
• Iterative ML model refinement using molecular simulation data for performance prediction.

Main Results:

• Identified 20 extant MOFs for experimental testing as catalyst supports or membranes.
• Established data-driven design guidelines: ~10 Å pore diameter and ~90 kJ/mol heat of adsorption for catalyst supports.
• Established data-driven design guidelines: ~2.75 Å pore diameter and ~80 kJ/mol heat of adsorption for membranes; Vanadium presence is beneficial.

Conclusions:

• The developed hierarchical screening approach efficiently identifies promising MOFs for NH3 synthesis.
• Specific MOF properties (pore size, heat of adsorption) are critical for catalyst support and membrane applications.
• This work provides a pathway for data-driven material design in energy-intensive chemical production.