The global energy crisis has intensified the demand for efficient, clean hydrogen production. Polymeric carbon nitride (PCN) is a visible-light-responsive, metal-free semiconductor, but its practical performance is hindered by poor charge mobility and insufficient active sites. Strategies such as heteroatom doping, defect engineering, and heterostructure construction have been widely explored to overcome these limitations.
In particular, alkali-metal incorporation can induce internal polarization fields, while isolated d¹⁰ metal species (e.g., Ga³⁺) anchored on PCN frameworks can optimize electronic structure and promote charge separation. Clay minerals offer abundant, low-cost layered supports, and transition-metal modification can introduce semiconducting behavior, enabling effective heterojunction formation.
Here, AI/ML-assisted literature mining and descriptor-based screening were employed to prioritize rational design directions. A Ga–Na–PCN photocatalyst with Ga–N anchoring sites and intercalated Na⁺ was synthesized via molten-salt calcination and coupled with Fe-modified Kunipia-F clay (Fe–KF). The built-in electric field at the heterointerface significantly enhanced charge separation and transfer, leading to markedly improved hydrogen evolution. These findings clarify the structure–activity relationship and provide guidance for designing advanced carbon nitride-based photocatalysts.
This work entitled “ Artificial intelligence-guided design of metal-doped polymeric carbon nitride/clay composites for increased photocatalytic hydrogen evolution ” was published on Acta Physico-Chimica Sinica (published on January 19, 2026).
Acta Physico-Chimica Sinica
Experimental study
Not applicable
Artificial intelligence-guided design of metal-doped polymeric carbon nitride/clay composites for increased photocatalytic hydrogen evolution
19-Jan-2026