journal article·2018-08

Synthesis of Mo-doped TiO2/reduced graphene oxide nanocomposite for photoelectrocatalytic applications

Z. Dargahi, H. Asgharzadeh, H. Maleki-Ghaleh

Ceramics International· Volume 44· Issue 11
Publisher page

Abstract

Abstract This study was carried out with the goal of lowering the band gap and electron recombination of the titanium dioxide through doping with Mo, and hybridizing with reduced graphene oxide, respectively, for photoelectrocatalytic applications. Mo with various atomic percentages (0.5, 1, 2, and 3 at%) was doped with TiO 2 using the mechanical alloying technique. The results of the ultraviolet diffuse reflectance spectroscopy analysis revealed that the TiO 2 doped with 1 at% Mo possessed the lowest band gap energy. Also, to further improve the photoelectrocatalytic efficiency, the 1 at% Mo-doped TiO 2 powder was hybridized with the reduced graphene oxide (RGO) through UV-assisted photocatalytic reduction of the graphene oxide. The XRD, FESEM, EDX, and FTIR analyses were implemented for characterization of the TiO 2 , 1 at% Mo-doped TiO 2 , and 1 at% Mo-doped TiO 2 /RGO powders. The superior integration of the TiO 2 and RGO was achieved as a result of the photocatalytic reduction method. After coating the powders on the fluorine-doped tin oxide (FTO) glass using the doctor blade technique, linear sweep voltammetry, amperometry, and electrochemical impedance spectroscopy tests were used to study the photoelectrocatalytic behavior of the samples. The results of the electrochemical tests showed that the photoelectrocatalytic activity of the TiO 2 was significantly enhanced as doped with Mo, and hybridized with the RGO. The mechanisms affecting the photoelectrocatalytic response in the Mo-doped TiO 2 /RGO composite are discussed, as well.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: TiO2 Photocatalysis and Solar Cells · Advanced Photocatalysis Techniques · Transition Metal Oxide Nanomaterials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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