Study on growth rate of TiO₂ nanostructured thin films: simulation by molecular dynamics approach and modeling by artificial neural network

Abstract

Effects of the deposition process parameters on the thickness of TiO 2 nanostructured film were simulated using the molecular dynamics (MD) approach and modeled by the artificial neural network (ANN) and regression method. Accordingly, TiO 2 nanostructured film was prepared experimentally with the sol-gel dip-coating method. Structural instabilities can be expected, due to short-and/or long-range intermolecular forces, leading to the surface inhomogeneities. In the MD simulation, the Morse potential function was used for the inter-atomic interactions, and equations of motion for atoms were solved by Verlet algorithm. The effect of the withdrawal velocity, drying temperature and number of deposited layers were studied in order to characterize the film thickness. The results of MD simulations are reasonably consistent with atomic force microscopy, scanning electron microscopy and Dektak surface profiler. Finally, the outputs from experimental data were analyzed by using the ANN in order to investigate the effects of deposition process parameters on the film thickness. In this case, various architectures have been checked using 75% of experimental data for training of the ANN. Among the various architectures, feed-forward back-propagation network with trainer training algorithm was found as the best architecture. Based on the R-squared value, the ANN is better than the regression model in predicting the film thickness. The statistical analysis for those results was then used to verify the fitness of the complex process model. Based on the results, this modeling methodology can explain the characteristics of the TiO 2 nanostructured thin film and growth mechanism varying with process conditions.

A molecular view on a polyaniline–TiO2 nanostructured thin film: Effect of temperature and pressure on the thermal, mechanical, and dynamical properties

“…In the bulk and surface regions, where bonds between the monomers are randomly oriented the monomer motion is isotropic and relaxation time is lower in comparison with contact and transition regions. A similar conclusion was reported by Borodin et al for the local relaxation of polyethylene oxide (PEO) at the TiO 2 surface [33].…”

“…It is concluded that the influence of the TiO 2 surface molecules on the conformational relaxation of the PANI is local: It is controlled by the first adsorption layer extending up to only 7 Å from the surface, but for torsion angles beyond 18-37 Å, it is identical to that in the bulk region of the PANI. In the contact region, relaxation time is higher than that of other regions because of the low mobility of PANI chains near the TiO 2 substrate, introduced as slowing-down effect [33,66]. Thus, an anisotropic motion is expected near the polymer-substrate interface, where the monomer ordering in the xy-plane results in a preferential monomer motion in that plane.…”

“…The wet film was subjected to heat treatment at temperature of 673 K at heating rate of 3°C min −1 . More details of the film preparation were described in the previous articles [33][34][35].…”

“…After construction of the TiO 2 molecular model, the coordinates of Ti and O atoms were changed by the msi2lmp commercial software to the suitable format for use as input to LAMMPS code [33,36,[38][39]. Detailed simulation model of TiO 2 system were described in previous papers [35][36].…”

“…The local dynamics of the PANI structure can be quantified in terms of the torsion time autocorrelation function (TACF), defined as: [33,64] …”