Turing-Inspired Architecture for Efficient Full-Spectrum Photothermal Catalytic CO₂ Reduction

Area of Science:

• Materials Science
• Catalysis
• Nanotechnology

Background:

• Catalyst design is crucial for efficient chemical reactions, but precise control over active sites and microenvironments remains challenging.
• Developing self-organized catalytic architectures is key to overcoming limitations in charge separation and reactant activation.

Purpose of the Study:

• To report a novel Turing-inspired photo-thermal-catalytic architecture for selective CO₂ reduction to CO.
• To investigate the structure-activity relationship of Turing-type Ta₂O₅ nanomesh supported on graphitic carbon nanosheets (g-CNS).

Main Methods:

• Fabrication of a Turing-type Ta₂O₅ nanomesh supported on g-CNS.
• Characterization of the material's structure, including labyrinthine networks and twin boundaries.
• Evaluation of catalytic performance for CO₂ reduction under full-spectrum irradiation with H₂O as the reducing agent.

Main Results:

• The Turing Ta₂O₅@g-CNS architecture exhibited enhanced mass transfer, improved charge separation, and expanded active surface area.
• Abundant coordinatively-unsaturated Ta sites were created, lowering the energy barrier for CO₂ reduction.
• A cocatalyst-free CO yield of 366.6 µmol g^-1 h^-1 was achieved, a 19-fold enhancement over the non-Turing material.

Conclusions:

• The study demonstrates the effectiveness of Turing structure design for enhancing catalytic efficiency in CO₂ reduction.
• The developed photo-thermal-catalytic architecture offers a promising strategy for full-spectrum catalyst applications.
• This work advances the field of catalysis by providing a novel approach to catalyst design and CO₂ utilization.