Predictive model for optimizing the near-field electromagnetic energy transfer in plasmonic nanostructure-involved photocatalysts

• A unique ternary model system is fabricated successfully.
• The SPR effects of plasmonic nanostructure are optimized via the dual modulation.
• The predictive model shows a tremendous SPR-mediated enhancement of photoactivity.

Förster resonant energy transfer (FRET) is critical hindrance for improving the solar-energy-conversion efficiency via the near-field electromagnetic energy transfer (NEET) mechanism in the plasmonic nanostructure-involved photocatalysts. Herein, a plasmonic nanoparticle/graphene/semiconductor ternary model system is fabricated successfully. In this fabrication, the thin graphene (RGO) layer covers completely the semiconductor with different facets exposed, and the plasmonic nanoparticles are separated from the semiconductor in a proper distance. This unique architecture raises a new opportunity to optimize surface plasmon resonance (SPR) effect in plasmonic nanostructure-involved photocatalysts by the dual modulation of interfacial layer’s thickness and fluorescent frequency, resulting a tremendous improvement in the rates of photocatalytic reactions. Furthermore, this predictive model provides a new idea for the design of high-efficient photocatalysts and may upper limits of SPR-mediated enhancement of photocatalytic performance for plasmonic nanostructure-involved photocatalysts.

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