Enhanced Electrocatalytic Activity of Nanoparticle Catalysts in Oxygen Reduction by Interfacial Engineering

  • Christopher P. Deming
  • Peiguang Hu
  • Ke Liu
  • Shaowei ChenEmail author
Part of the Nanostructure Science and Technology book series (NST)


Currently, the widespread integration of fuel cells into the energy market is limited by the large amounts of precious metal catalysts necessary for effective oxygen reduction (ORR) and sufficient energy output. To meet this challenge, many methods have been successfully adopted to improve the activity of fuel cell electrocatalysts and to provide a basis for controlled manipulation of particle properties, specifically, the interaction between the catalyst surface and ORR intermediates. Of these, interfacial engineering of the nanoparticle surface has proven an effective and facile method for enhancing catalytic activity and is the focus of this chapter. What follows is a review of electrocatalytic enhancement from the prospective of surface modifications for nanoparticle alloys, organically capped nanoparticles, and metal oxide nanoparticles. For alloy nanoparticles, the mixing of the metals will modify the d band structure of the surface atoms and result in different binding affinities for oxygenated intermediates. Organic functionalization will alter the kinetics of catalysis by imparting electronic effects on the surface atoms based on the nature of the ligand and nature of the interfacial bond. Surface oxygen vacancies and other stoichiometry modifications of metal oxide particles have also been shown to alter surface properties and thus alter the dynamics of oxygen electroreduction. In each case, we closely examine the connection between particle characteristics and activity as well present experimental methods used to control these properties and the prevailing theories detailing the basis for electrocatalytic enhancement.


Oxygen Reduction Electrocatalytic Activity Alloy Catalyst Interfacial Engineering Ligand Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank the National Science Foundation for partial support of the work.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christopher P. Deming
    • 1
  • Peiguang Hu
    • 1
  • Ke Liu
    • 1
  • Shaowei Chen
    • 1
    Email author
  1. 1.Department of Chemistry and BiochemistryUniversity of CaliforniaSanta CruzUSA

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