Assessment of TiO2 band gap from structural parameters using artificial neural networks

Role of pH in the Hydrothermal Synthesis of TiO2 Nanorod Photocatalysts

“…[34] Titanium dioxide rutile has direct allowed transitions. [30][31][32][33][34][35] The plot of (hυα) 2 versus photon energy, hυ was given in figure 3 (b) for all the ChemistrySelect synthesized photocatalysts described through the Tauc method. The intercept of the tangent to the plot is considered a good approximation of the band gap energy for synthesized TiO 2 photocatalysts.…”

“…The intercept of the tangent to the plot is considered a good approximation of the band gap energy for synthesized TiO 2 photocatalysts. [28][29][30][31][32][33] The TiO 2 NRs can be activated by radiation with wavelengths less than 420 nm (band gap ∼ 2.95 eV) and the electrons were excited from O2p to Ti3d. Reducing the crystallinity and crystallite size compared to TiO 2 synthesized at pH = 2.5 may have an effect on increasing the energy band gap to 3.01 and 3.54 eV which corresponds to TiO 2 prepared at pH = 3.5 and 1.5, respectively.…”

“…Moreover, the absorption coefficient of direct bandgap semiconductors is usually larger than that of indirect bandgap semiconductors because indirect transitions require phonon absorption or emission, which is less likely to occur [34] . Titanium dioxide rutile has direct allowed transitions [30–35] . The plot of (hυα) 2 versus photon energy, hυ was given in figure 3 (b) for all the synthesized photocatalysts described through the Tauc method.…”

“…The plot of (hυα) 2 versus photon energy, hυ was given in figure 3 (b) for all the synthesized photocatalysts described through the Tauc method. The intercept of the tangent to the plot is considered a good approximation of the band gap energy for synthesized TiO 2 photocatalysts [28–33] . The TiO 2 NRs can be activated by radiation with wavelengths less than 420 nm (band gap ∼2.95 eV) and the electrons were excited from O2 p to Ti3 d .…”

Photocatalytic Performance of TiO₂-ZnAl LDH Based Materials: Kinetics and Neural Networks Approach

“…Considering the ANN advantages regarding the reduced time requirement for the development of the model than traditional mathematical models, this model has recently been applied in engineering and science [14]. The model is based on biological neural systems with an interconnection of functions and variables that are revealed in three main layers of neurons (input, hidden and output layers) which are adjusted with their weights and biases [15]. Besides the numerous advantages of the ANN model, this model can successfully be employed to simulate the process and define the significance of the various operating variables [16,17].…”