journal article·2024-05-01Open access

Enhancing photocatalytic performance of BiOI/TiO 2 NTs nanocomposites through SILAR optimization for methylene blue degradation

M Zaghdoudi, J Bennaceur, R Ouertani

Materials Research Express· Volume 11· Issue 5

Abstract

Abstract BiOI nano-leaves were deposited on to TiO 2 nanotubes (NTs) using the Successive Ionic Layer Adsorption and Reaction (SILAR) technique, which was developed using the electrochemical anodization method. Various SILAR cycle numbers (three, five, and seven cycles) were employed in the experiment. The as-prepared nanocomposites (NCs) were characterized by several technique, the morphology of the elaborated NCs samples was examined using a S − 4800 field emission scanning electron microscope (SEM), the Reflectance and diffuse reflectivity of the NCs samples were measured by a Shimadzu UV-3100S spectrophotometer in the spectral range [300 –1200 nm] and the XRD diffraction was used to identify the crystalline structure of the processed BiOI/TiO 2 NTs nanocomposites. A diffractometer with a Cu K α anode ( λ = 0.1542 nm) operating at 40 kV and 30 mA was used. The as-prepared NCs, specifically BiOI/TiO 2 NTs, were designed for the photocatalytic degradation of methylene Blue (MB). X-ray diffraction analysis revealed the formation of the TiO 2 anatase phase and polycrystalline BiOI films in all processed NCs. UV/Vis measurements indicated a shift in the nanocomposite’s active region from UV to visible light. The highest absorption and the lowest bandgap energy (Eg value, ∼2 eV) were observed in the NCs with 5 BiOI cycles. The photocurrent density reached 27 μ A cm −2 , approximately three times higher than the photocurrent density exhibited by TiO 2 nanotubes under similar conditions. The optimal photocatalytic rate was achieved with BiOI/TiO 2 NCs processed after five SILAR cycles.
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Research context

OpenAlex topics: Advanced Photocatalysis Techniques · TiO2 Photocatalysis and Solar Cells · Catalytic Processes in Materials Science

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

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