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The Direct Electrochemistry of Fuel-Forming Enzymes on Semiconducting Electrodes: How Light-Harvesting Semiconductors Can Alter the Bias of Reversible Electrocatalysts in Favour of H2 Production and CO2 Reduction

  • Andreas S. J. L. BachmeierEmail author
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Part of the Springer Theses book series (Springer Theses)

Abstract

This chapter describes the direct electrochemistry of the fuel-forming enzymes [NiFeSe]-hydrogenase (Sect.  1.6.3) and carbon monoxide dehydrogenase (CODH, Sect.  1.6.4) immobilised on porous n-type semiconductor electrode materials. Being reversible electrocatalysts for CO2/CO and H+/H2 interconversion, these enzymes show high activities for both oxidation and reduction when attached to metallic-like graphite electrodes (PGE). Yet, whereas the most efficient catalysts for the formation of solar fuels should, in addition to operating close to reversible potentials, possess a catalytic bias for the fuel-forming direction, both [NiFeSe]-hydrogenase and CODH possess an inherent catalytic bias that favours the oxidation over the reduction half-reaction. When [NiFeSe]-hydrogenase and CODH are adsorbed on n-type semiconductor electrodes constructed from CdS and TiO2 nanoparticles, a marked shift in bias is observed, now favouring CO2 or H+ reduction. The fuel-forming reaction is efficiently catalysed at minimal overpotentials (both in the dark and under illumination), while the, in this case destructive oxidation reaction is greatly suppressed. This chapter describes how the electronic state of the electrode can strongly bias the direction of electrocatalysis of both CO2 and H2 cycling. Electrical impedance spectroscopy (EIS) experiments shed light on the binding mode of these enzymes on porous surfaces and indicate possible rate-limiting parameters in photocatalytic CO2 or H+ reduction by metalloenzymes adsorbed on semiconductor nanoparticles.

Keywords

Transparent Conducting Oxide TiO2 Electrode Direct Electrochemistry Semiconductor Electrode Interfacial Electron Transfer 
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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Business Unit CatalystsClariantMunichGermany

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