Photocatalytic Flow Synthesis of Acetone from Methane Carbonylation Using Customized Flow Patterns.

Area of Science:

• Catalysis
• Materials Science
• Chemical Engineering

Background:

• Direct synthesis of acetone from methane and carbon monoxide via photocatalysis offers a sustainable route but faces challenges in yield and selectivity.
• Optimizing catalyst design and reaction conditions is crucial for efficient methane carbonylation.

Purpose of the Study:

• To develop a highly selective and active photocatalytic process for acetone synthesis from methane and carbon monoxide.
• To enhance the performance of photocatalytic methane carbonylation using machine learning and advanced reactor engineering.

Main Methods:

• Utilized a microchannel flow reactor integrated with machine learning for catalyst screening.
• Developed Au-NiO heterostructure-decorated ZnO catalysts with interfacial regulation.
• Employed custom-made micropillar channels to enhance gas-liquid mass transfer.

Main Results:

• Achieved a high acetone production rate of 50.9 μmol h⁻¹ with 87.6% selectivity.
• Identified the Au-NiO interface as the active site for acetyl intermediate formation.
• Demonstrated enhanced mass transfer through optimized reactor design.

Conclusions:

• The combination of atomic engineering (Au-NiO interfaces) and multiphase flow regulation in a microchannel reactor significantly improves photocatalytic methane carbonylation.
• This approach provides a viable pathway for sustainable acetone production.