Multiscale Modeling of TiO₂ Nanoparticle Production in Flame Reactors: Effect of Chemical Mechanism

Abstract

For titanium dioxide (TiO 2 ) nanoparticles manufactured in flame reactors, the precursor is injected into a pre-existing flame, exposing it to a high-temperature gas phase, leading to nucleation and particle growth. Predictive modeling of this chemical process requires simultaneous development of detailed chemical mechanisms describing gas-phase combustion and particle evolution, as well as advanced computational tools for describing the turbulent flow field and its interactions with the chemical processes. Here, a multiscale computational tool for flame-based TiO 2 nanoparticle synthesis is developed and a flamelet model representing detailed chemistry for particle nucleation is proposed. The effect of different chemical mechanisms (i.e., one-step, detailed, flamelet) on the prediction of nanoparticle nucleation is investigated using a plug-flow reactor and a partially stirred tank reactor to model the flow field. These simulations demonstrate that particle nucleation occurs much later in the flame with detailed titanium oxidation chemistry, compared to one-step chemistry. Finally, a large-eddy simulation tool is developed to study the effect of precursor injection configuration on nanoparticle formation in turbulent flames. For titanium dioxide (TiO 2 ) nanoparticles manufactured in flame reactors, the precursor is injected into a pre-existing flame, exposing it to a high-temperature gas phase, leading to nucleation and particle growth. Predictive modeling of this chemical process requires simultaneous development of detailed chemical mechanisms describing gas-phase combustion and particle evolution, as well as advanced computational tools for describing the turbulent flow field and its interactions with the chemical processes. Here, a multiscale computational tool for flame-based TiO 2 nanoparticle synthesis is developed and a flamelet model representing detailed chemistry for particle nucleation is proposed. The effect of different chemical mechanisms (i.e., one-step, detailed, flamelet) on the prediction of nanoparticle nucleation is investigated using a plug-flow reactor and a partially stirred tank reactor to model the flow field. These simulations demonstrate that particle nucleation occurs much later in the flame with detailed titanium oxidation chemistry, compared to one-step chemistry. Finally, a large-eddy simulation tool is developed to study the effect of precursor injection configuration on nanoparticle formation in turbulent flames.

Large‐eddy simulation modeling of turbulent flame synthesis of titania nanoparticles using a bivariate particle description

“… First, find the volume node weights and abscissas ( and ) from number of the pure volume moments ( ) via the product‐difference algorithm. This procedure is identical to that of the univariate QMOM case). Once the volume nodes are found, the conditional area node information can be determined by following the substeps listed below Using the CQMOM definition of construct a linear equation system for , the l ‐th area moment for the i ‐th volume node, and solve it for each . For example, for l = 1 when , following linear system is constructed with a Vandermonde matrix: …”

“…, , ) are solved using a fifth‐order weighted essentially nonoscillatory (WENO) scheme . CQMOM utilizes precisely the same six QMOM integer volume moments ( ) used in Sung et al to describe the NDF with the three volume nodes. Unlike QMOM, however, three additional volume‐area mixed moments ( ) are solved in order to recover conditional area node information.…”

Bivariate extensions of the Extended Quadrature Method of Moments (EQMOM) to describe coupled droplet evaporation and heat-up

“…QMOM was mainly applied in the field of solid particle population modeling, e.g. soot (Blanquart & Pitsch, 2007;Chittipotula, Janiga, & Thévenin, 2012;Cuoci, Frassoldati, Alessio, Faravelli, & Ranzi, 2008;Marchisio & Barresi, 2009;Zucca, Marchisio, Barresi, & Fox, 2006) or nano-particles (Mehta, Sung, Raman, & Fox, 2010Sung, Raman, Koo, Mehta, & Fox, 2014), fluidized suspensions (Mazzei, Marchisio, & Lettieri, 2012) and of gas bubbly flows (Petitti et al, 2010). In the field of spray modeling, Quadrature-Based Moment Methods (QBMMs) have been applied to model the spray PBE in physical space (Carneiro, Kaufmann, & Polifke, 2008;Chalons, Fox, & Massot, 2010;Choi, 2010;Fox et al, 2008;Gumprich & Sadiki, 2013;Kah, Massot, Tran, Jay, & Laurent, 2010;Mukhopadhyay, Jasor, & Polifke, 2012).…”

Synthesis of well-defined functional crystals by high temperature gas-phase reactions

“…Compared to wet chemical methods, HTGR method has several unique merits, (1) the sample can be produced with continuous or semi-continuous operation; (2) high temperature provides the product with controlled scale dimensions and a high degree of crystallinity; and (3) a remarkable growth rate; which can be expected to substantially reduce the cost of producing well-defined crystals on an industrial scale. Although HTGR has been applied sporadically in industrial production to manufacture fumed silica (Cabot Corporation, USA) and titanium dioxide (DuPont Company, USA) since 1940s and 1950s, respectively, mass production of well-defined single crystals via the HTGR process is still a big challenge, considering the fact that the time of chemical reaction and crystal growth during the HTGR process is very short only about several seconds [13][14][15][16][17][18][19][20][21][22][23].…”