CST – Computer Simulation Technology

Nanostructured Zinc Oxide Systems with Gold Nanoparticle Pattern for Efficient Light Trapping

Title:
Nanostructured zinc oxide systems with gold nanoparticle pattern for efficient light trapping
Author(s):
Elzbieta Robak, Michal Kotkowiak, Henryk Drozdowski
Source:
Journal of Physics D: Applied Physics
Vol./Issue/Date:
Vol 49 No. 4
Year:
2016
Page(s):
pp 1 - 9
Keywords:
plasmonic NPs, nanowires, optical properties, nanomaterials, solar cells, finite integration technique
Abstract:
In this work we describe the design of a system consisting of a zinc oxide nanowire array and ITO glass nanostructured with gold NPs. Our goal was to create a more efficient system that could be used in various optical applications, such as photovoltaics or photodetectors. The impact of gold NPs of different shapes, single as well as arranged in a pattern, on the optical properties of the system was studied by using a finite integration technique. The absorptance and transmittance spectra of individual components of the system were calculated. Finally, the integrated spectral enhancement factors of the photons absorbed and transmitted by the electrode were estimated using the different geometrical parameters of the electrode. The results suggested that the most effective absorber of light should include zinc oxide nanowires (NWs), with smaller diameters and cylindrical shapes of single gold NPs, as well as in a pattern, while the highest transmittance is obtained for greater diameter of NWs and conical shapes of gold NPs in a pattern. Based on these results, the absorption current density (derived from the generation and collection of light-generated charge carriers) was calculated for the ZnO-CdTe core-shell NWs nanostructured with gold NPs arranged into a pattern. The results suggest that the most efficient electrode contains ZnO NWs with gold NPs in a conical shaped pattern. Our results confirm the importance of computational simulation in the design of the photonic and photovoltaic devices, making it possible to predict the most efficient systems. These results could be useful to further optimize photonic or photovoltaic devices based on plasmonic NPs and semiconductor nanostructures.
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