Combining density functional theory and CFD-PBM model to predict TiO2 nanoparticle evolution during chemical vapor deposition

• DFT and CFD-PBM models are combined to predict the evolution of TiO 2 nanoparticles. • TiCl 4 hydrolysis mechanisms including gas phase and surface reactions are studied. • The main pathway of gas phase hydrolysis from TiCl 4 to TiO 2 is via TiO(OH) 2 . • The largest energy barrier of surface reactions is on (TiO 2 ) 6 nanoclusters surface. • Prediction of PSD by CFD-PBM model agrees well with experiments in the literature. Preparation of TiO 2 nanoparticles by TiCl 4 hydrolysis during chemical vapor deposition (CVD) is one of the effective techniques for producing TiO 2 nanoparticles, while the mechanisms of TiO 2 nanoparticle evolution are not well understood. This paper uses computational fluid dynamics (CFD) - population balance model (PBM) to predict particle size distribution during CVD, and density functional theory (DFT) calculations to explore reaction mechanisms. DFT calculations reveal that the gas phase hydrolysis reaction can be simplified into two processes, the hydrolysis of TiCl 4 to form TiO(OH) 2 and the decomposition of TiO(OH) 2 to TiO 2 . During the surface reactions, the energy barriers of TiO(OH) 2 decomposition reactions on the surfaces of (TiO 2 )n nanoclusters (n=1-8) are generally lower than those in the gas phase reactions, and the largest energy barrier of the surface reaction occurs on the surface of (TiO 2 ) 6 nanoclusters. Simulations using a perfect stirred reaction model indicate that Ti(OH) 4 is the dominant hydrolysis products when the temperature is lower than 548 K, while TiO 2 production starts at ∼ 610 K, indicating reaction temperature is a crucial factor that governs the products of TiCl 4 hydrolysis. CFD-PBM model predict the evolution of Particle Size Distribution (PSD) of nanoparticles in a CVD reactor, which is agreed with experimental measurements in the literature. Increasing the reaction temperature results in an increase of the peak of the PSD of nanoparticles in the reactor. This study provides atomic insights into the nanoparticle evolution and a practical model to predict the nanoparticle evolution during the CVD process.