Topics in Catalysis

, Volume 58, Issue 1, pp 46–56 | Cite as

Cluster Models for Studying CO2 Reduction on Semiconductor Photoelectrodes

  • John A. Keith
  • Ana B. Muñoz-García
  • Martina Lessio
  • Emily A. Carter
OriginalPaper

Abstract

Sunlight-powered CO2-photoelectroreduction is a promising and potentially sustainable route to recycle CO2 byproducts back into energy-dense liquid fuels. One of the most intriguing processes known to date is the pyridinium-catalyzed CO2 reduction on p-type GaP photoelectrodes, where conversion to methanol has reported faradaic efficiencies nearing 100 %. Modeling this reactive environment requires understanding energetics of differently charged species at semiconductor electrodes, so we develop a cluster model and benchmark binding energies from it to those from Kohn–Sham density functional theory calculations that employ periodic boundary conditions. We then use this cluster model to theoretically predict structures and binding energies for charged and neutral adsorbates on the GaP(110) surface with and without the presence of van der Waals interactions and implicit solvation. We discuss the relative magnitudes of binding energy contributions for different adsorbates considered relevant in this CO2 reduction process and provide details showing pitfalls when using cluster models.

Keywords

Carbon dioxide reduction Gallium phosphide photoelectrodes Density functional theory calculations 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • John A. Keith
    • 1
    • 2
  • Ana B. Muñoz-García
    • 3
  • Martina Lessio
    • 4
  • Emily A. Carter
    • 1
    • 5
  1. 1.Department of Mechanical and Aerospace EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Department of Chemical and Petroleum Engineering, Swanson School of EngineeringUniversity of PittsburghPittsburghUSA
  3. 3.Department of Chemical SciencesUniversity of Naples Federico IINaplesItaly
  4. 4.Department of ChemistryPrinceton UniversityPrincetonUSA
  5. 5.Program in Applied and Computational Mathematics and Andlinger Center for Energy and the EnvironmentPrinceton UniversityPrincetonUSA

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