Machine-Learning-Assisted Synthesis of Bimetallic Metal-Organic Frameworks for the Optimized Oxygen Evolution...

Area of Science:

• Materials Science
• Electrochemistry
• Catalysis

Background:

• Bimetallic metal-organic frameworks (MOFs) are promising electrocatalysts for the oxygen evolution reaction (OER).
• Precise tuning of metal precursors and composite ratios is crucial for optimizing OER performance and minimizing overpotential.
• Existing MOF catalysts require further development for enhanced efficiency and stability.

Purpose of the Study:

• To apply machine learning (ML) algorithms for optimizing metal precursor and composite ratios in bimetallic MOFs for OER.
• To identify key factors governing OER performance through ML-driven analysis.
• To design and synthesize a highly efficient electrocatalyst for water splitting.

Main Methods:

• Synthesis of a bimetallic FeCo squarate-based MOF (FeCo-Sq MOF) via solvothermal method.
• Optimization of metal precursor ratios using ML algorithms.
• Coating the FeCo-Sq MOF with S-doped graphitic carbon nitride (SCN) and wrapping with polydopamine (PDA).
• Fine-tuning SCN loading using ML for optimal OER catalyst performance.

Main Results:

• ML algorithms successfully optimized metal precursor ratios for low overpotential.
• PDA wrapping enhanced stability, charge transfer kinetics, and SCN anchoring.
• The ML-optimized PDA-SCN@FeCo-Sq MOF achieved a low overpotential of 310 mV and a Tafel slope of 56 mV/dec at 10 mA cm⁻² in 1 M KOH.
• Demonstrated high electrocatalytic performance for water splitting.

Conclusions:

• ML provides a powerful strategy for designing high-performance MOF electrocatalysts.
• The developed PDA-SCN@FeCo-Sq MOF is a promising catalyst for efficient water splitting.
• This approach facilitates precise tuning of MOF composition for targeted catalytic applications.