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Area of Science:

  • Electrochemistry
  • Materials Science
  • Catalysis

Background:

  • Electrosynthesis of hydrogen peroxide (H2O2) via the two-electron oxygen reduction reaction (2e-ORR) is crucial for sustainable chemical production.
  • Developing highly selective and active electrocatalysts remains a significant challenge.
  • Predictive models are needed to rationalize selectivity trends and accelerate materials discovery.

Purpose of the Study:

  • To evaluate the effectiveness of the ΔΔG selectivity descriptor for predicting H2O2 production selectivity.
  • To explore diverse active sites in 2D materials, including carbon-based structures, boron nitrides, and single atom catalysts.
  • To identify materials that are both catalytically active and highly selective for the 2e-ORR.

Main Methods:

  • Utilized the ΔΔG descriptor, based on thermodynamic analysis of adsorption free energies (ΔGOOH* and ΔGO*), to quantify selectivity.
  • Systematically investigated various active sites in carbon-based materials, boron nitrides, and single atom catalysts.
  • Assessed the correlation between catalytic activity and selectivity using the ΔΔG model.

Main Results:

  • The ΔΔG descriptor effectively captures selectivity trends across different material classes.
  • A small fraction of investigated active sites in carbon-based materials demonstrated both high activity and high selectivity for 2e-ORR.
  • Identified specific active sites that show promise for selective H2O2 electrosynthesis.

Conclusions:

  • The ΔΔG descriptor is a valuable predictive tool for designing selective electrocatalysts for H2O2 production.
  • Not all active sites exhibit high selectivity; careful screening is essential.
  • This work provides insights for developing advanced 2D materials for efficient and selective 2e-ORR.