Nanoparticle evolution in flame spray pyrolysis—Process design via experimental and computational analysis

In flame spray pyrolysis (FSP), the evolution of metal oxide nanoparticles relies on quite a number of droplet (liquid) and vapor phase related physical mechanism as for instance precursor evaporation, oxidation, nucleation via gas-to-particle conversion mechanism, and subsequent particle (solid) growth mechanisms based on coagulation, sintering/coalescence, and agglomeration. The liquid precursor and dispersion oxygen feed rates are relevant control parameters of the FSP process for tailoring the nanoparticle size (diameter) and structure as well as the atomizer nozzle configuration. Sophisticated nonintrusive, laser-based in situ and ex situ diagnostics with multiscale spatial resolution (micrometer to meter range) are applied for analyzing droplet formation and size, gas velocity, temperature, species concentration, as well as primary and agglomerate diameters along the flow direction. Computational fluid dynamics (CFD) are coupled with population balance modeling (PBM) to elucidate the nanoparticle dynamics within the reactive spray. It is found that the CFD-PBM approach allows estimations of primary and agglomerate nanoparticle diameters within 80 and 75% accuracy compared to experimental data, suggesting that the methods presented could pave the way for designing next-generations of flame reactors.     

On the relevance of accounting for the evolution of the fractal dimension in aerosol process simulations

A population balance model is presented, which tracks particle growth in the gas phase and accounts for simultaneous agglomeration and sintering: Simulations reveal the evolution of the full distribution of a volume equivalent diameter and, amongst others, the evolution of the agglomerate collision diameter, a mean primary particle size and the number of primary particles per agglomerate. Furthermore, assuming fractal behaviour of the growing agglomerate particles—for the first time—a model for the evolution of a mean value of the fractal dimension based on physical and process parameters is proposed and incorporated into the simulation model. PARSIVAL, a commercial solver for integro-differential equations is employed to solve the equations involved. It is based on a generalised finite-element scheme with self-adaptive grid- and order construction. Calculations are performed to validate the model against monodisperse and sectional models published in literature for the exemplary case of Si production. The results are in good agreement if the same simplifying assumptions are made. However, results obtained from the new model for both—isothermal and non-isothermal process conditions—clearly show that it is important to consider the changing fractal dimension in many cases.     

A molecular simulation study on the sintering mechanism of TiO2

The sintering process of TiO₂ nanoparticles with different particle sizes and temperatures was studied thoroughly using equilibrium molecular dynamics simulations. The results show that when two nanoparticles contact, the sintering process was initiated by the merge of surface atoms of nanoparticles, and the process subsequently drives the internal atom to merge until two particles being merged homogeneously. There is mutual attraction between atoms and the particles gain kinetic energy to migrate due to the heating at high temperatures, leading to a faster sintering reaction. Moreover, there is a large difference in the sintering speed between 1600 and 1800 K. In the vicinity of the melting point, a small change in temperature makes a great impact on the sintering rate of TiO₂ nanoparticles. Furthermore, by using the Lindemann index, it can be found that the larger particles have higher lattice structure stability compared to the smaller ones. The larger particle has a greater effect on the sintering behaviors when particles with different sizes’ contact. As a consequence, the sintering of two particles with different sizes is mainly initiated by the smaller particles moving toward the larger particle and is ended up with the atoms of smaller particle spreading around the larger particle. Therefore, the large nanoparticle size reduces the overall sintering rate.     

A Novel Porous Tube Reactor for Nanoparticle Synthesis with Simultaneous Gas-Phase Reaction and Dilution

A novel porous tube reactor that combines simultaneous reactions and continuous dilution in a single-stage gas-phase process was designed for nanoparticle synthesis. The design is based on the atmospheric pressure chemical vapor synthesis (APCVS) method. In comparison to the conventional hot wall chemical vapor synthesis reactor, the APCVS method offers an effective process for the synthesis of ultrafine metal particles with controlled oxidation. In this study, magnetic iron and maghemite were synthesized using iron pentacarbonyl as a precursor. Morphology, size, and magnetic properties of the synthesized nanoparticles were determined. The X-ray diffraction results show that the porous tube reactor produced nearly pure iron or maghemite nanoparticles with crystallite sizes of 24 and 29 nm, respectively. According to the scanning mobility particle sizer data, the geometric number mean diameter was 110 nm for iron and 150 nm for the maghemite agglomerates. The saturation magnetization value of iron was 150 emu/g and that of maghemite was 12 emu/g, measured with superconducting quantum interference device (SQUID) magnetometry. A computational fluid dynamics (CFD) simulation was used to model the temperature and flow fields and the decomposition of the precursor as well as the mixing of the precursor vapor and the reaction gas in the reactor. An in-house CFD model was used to predict the extent of nucleation, coagulation, sintering, and agglomeration of the iron nanoparticles. CFD simulations predicted a primary particle size of 36 nm and an agglomerate size of 134 nm for the iron nanoparticles, which agreed well with the experimental data.

Copyright 2015 American Association for Aerosol Research     

A Monte Carlo determination of the effectiveness of nanoparticles as spacers for optimizing TiO2 opacity

Optimal TiO₂ opacity is achieved when the TiO₂ particles are well spaced. Nanoparticle extenders are claimed to improve TiO₂ spacing by positioning themselves between the larger TiO₂ particles (0.25 μm) and preventing the TiO₂ particles from approaching or touching one another. Claims have been made that this can as much as double the light-scattering efficiency of TiO₂. This concept was tested by a combination of Monte Carlo simulations and image analysis techniques to determine computationally how the effectiveness of this approach is affected by TiO₂ concentration, nanoparticle concentration, and nanoparticle size. Surprisingly, the results showed that nanoparticles did not, in fact, improve the spacing of TiO₂ particles under any of the conditions examined (specifically, in the absence of any inter-particle interactions). Instead, TiO₂ distributions and spacings were completely indifferent to the presence of smaller particles (however, large extender particles were found to cause TiO₂ crowding, consistent with expectations and experiments). Explanation and implications of these findings are discussed.     

Stochastic simulation of agglomerate formation in fluidized bed spray drying: A micro-scale approach

A stochastic model that describes agglomerate growth during fluidized bed spray agglomeration is presented and numerically solved by constant volume Monte Carlo method. The methodology overcomes the difficulties of solving multivariate population balance equations and includes continuous binder addition and drying. Agglomerate formation is treated as a complex combination of consecutive and parallel micro-mechanisms. Due to the discrete nature of the approach, the individual role of the micro-mechanisms on the agglomeration behavior can be analyzed.The results suggest that the droplet capture mechanism governs the agglomeration speed while the maximum agglomerate diameter is ruled by the equilibrium reached between coalescence, rebound and breakage. The mechanism of deposited binder drying is found to play a negligible role on agglomerate formation because of an extremely rapid droplet consumption. The main process variables affecting each micro-mechanism have been identified showing that the liquid spraying rate affects directly the droplet capture mechanism whereas binder properties influence mainly the agglomeration and rebound interactions.The model presented in this study is able to predict qualitatively the experimentally observed response of the system as well as the general shape of the agglomerate size distribution under the variation of several process parameters, demonstrating the potential of the discrete micro-level approach.     

Effects of particle type on thermal and mechanical properties of polyoxymethylene nanocomposites

The effects of particle size of titanium dioxide (TiO₂) on mechanical, thermal, and morphological properties of pure polyoxymethylene (POM) and POM/TiO₂ nanocomposites were investigated and compared with the results for nanoparticle ZnO in the same matrix, reported in a previous paper. POM/TiO₂ nanocomposites with varying concentration of TiO₂ were prepared by the melt mixing technique in a twin screw extruder, the same method that used for blending the homogeneous ZnO nanocomposites. The dispersion of TiO₂ particles in POM nanocomposites was studied by scanning electron microscopy (SEM). The agglomeration, as observed by the mechanical properties of TiO₂ particles in the polymer matrix, increased with increasing TiO₂ content, a result not found for ZnO even at lower particle sizes. Increasing the filler content of POM/TD32.4 and POM/TD130 (130 nm) nanocomposites resulted in a decrease in tensile strength. The Young modulus, stress at break and impact strength of TiO₂ nanocomposite did not improve with increasing filler contents, in opposition to the better agglomeration conditions of ZnO nanocomposite even at lower particle sizes. Because of agglomeration, the POM/TD32.4 nanocomposites had lower mechanical properties and lower degradation temperature than the POM/TD130 ones. The sizes of nanoparticles determined the agglomeration, but however, the agglomeration also depended on the type of nanoparticles, even when using the same matrix (POM) and the same mixing method. TiO₂ nanoparticles were more difficult to mix and were more agglomerated in the POM matrix as compared to ZnO nanoparticles, regardless of the size of the nanoparticles. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Constant number Monte Carlo simulation of population balances with multiple growth mechanisms

We present a complete simulation scheme for particulate processes based on the constant number Monte Carlo methodology. Specifically, the proposed scheme can be applied towards the solution of population balances that include nucleation, coagulation and surface deposition, coupled to chemical reactions. The synthesis of titania (TiO₂) by flame oxidation of TiCl₄ is employed as a comparison basis of the relative advantages and weaknesses of Monte Carlo against more classical numerical approaches. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Modeling of aggregation kernel using Monte Carlo simulations of spray fluidized bed agglomeration

The present work attempts to consider the microscopic mechanisms of spray fluidized bed agglomeration while modeling the macroscopic kinetics of the process. A microscale approach, constant volume Monte‐Carlo simulation, is used to analyze the effects of micro‐processes on the aggregation behavior and identify the influencing parameters. The identified variables, namely the number of wet particles, the total number of particles, and the number of droplets are modeled and combined in the form of an aggregation kernel. The proposed kernel is then used in a one‐dimensional population balance equation for predicting the particle number density distribution. The only fitting parameters remaining in the population balance system are the collision frequency per particle and a success fraction accounting for the dissipation of kinetic energy. Predictions of the population balance model are compared with the results of Monte‐Carlo simulations for a variation of significant operating parameters and found to be in good agreement. © 2014 American Institute of Chemical Engineers AIChE J, 60: 855–868, 2014     

Large-eddy-simulation-based multiscale modeling of TiO2 nanoparticle synthesis in a turbulent flame reactor using detailed nucleation chemistry

In this work, we present a multiscale computational model for flame synthesis of TiO₂ nanoparticles in a turbulent flame reactor. The model is based on large-eddy simulation (LES) methodology in conjunction with detailed gas-phase chemical kinetics to accurately model the highly complicated combustion and nucleation processes in a turbulent flame. A flamelet-based model is used to model turbulence–chemistry interactions. In particular, the transformation of TiCl₄ to the solid primary nucleating TiO₂ nanoparticles is represented using an unsteady kinetic model considering 30 species and 69 reactions in order to accurately describe the important event of nanoparticle formation. The evolution of the TiO₂ number density function is tracked using the quadrature method of moments (QMOM). For validation purposes, the detailed computational model is compared against experimental data and reasonable agreement is obtained.     

A Detailed Model for the Sintering of Polydispersed Nanoparticle Agglomerates

In this study the coagulation, condensation, and sintering of nanoparticles is investigated using a stochastic particle model. Each stochastic particle consists of interacting polydisperse primary particles that are connected to each other. In the model sintering occurs between each individual pair of neighboring primary particles. This is important for particles in which the range of the size of the primary particles varies significantly. The sintering time is obtained from the viscous flow model. The model is solved using a stochastic particle algorithm. The particles are represented in a binary tree that contains the connectivity as well as the degree of sintering information. Particles are forme, coagulate, sinter, and experience condensation according to known rate laws. The particle binary tree, along with it the degree of sintering, is updated after each time step according to the rates of the different processes. The stochastic particle method uses the technique of fictitious jumps and linear process deferment. The theoretical results are fitted against experimental values for the formation of SiO ₂ nanoparticles and computer generated TEM pictures are presented and compared to experiments.     

Numerical simulation of nanoparticle synthesis in diffusion flame reactor

A computational model combining the fluid dynamics with the particle kinetics was employed to study TiO₂ nanoparticle synthesis in a diffusion flame reactor. A one-step chemical kinetics approach was used to model titanium tetraisopropoxide (TTIP) decomposition that leads to homogeneous nucleation and particle formation. The momentum, heat, and mass transfer, Brownian coagulation and diffusion, surface growth, coalescence and thermophoresis have been taken into account. Based on the particle size distributions, an efficient quadrature method of moments was allowed to approximate the general dynamics equation of particle, and the eddy dissipation concept (EDC) combustion model was used to estimate the flame temperature field. Excellent agreements between the model predictions and experimental data, with respect to the flame temperature distribution and particle kinetics, are reached. By taking the particle size and surface area as independent variables, the full distributions of volume equivalent diameters, evolution of the agglomerate number, the geometric standard deviation based on volume and agglomerate fractal nature, mean primary particle size and the number of primary particles per agglomerate are revealed. The variation of oxygen flow rate has an important influence on the temperature distribution and hence on the particle kinetics accordingly.     

反应沉淀法制备亚微米粒子的粒度分布模拟与实验验证

采用基于粒数衡算方程及物料衡算方程的数值模拟方法模拟了包括反应、成核、生长及凝并的反应沉淀制备亚微米粒子的过程,其中颗粒间的凝并过程采用分级模型来模拟,产物颗粒的粒度分布（PSD）由离散化的粒数衡算方程求得.以BaSO₄的反应沉淀体系作为研究体系,将实验测量数据与数值模拟结果进行对比,验证了所构建的数学模型的正确性与适用性.应用此模型进行分析,发现沉淀时间、产物过饱和度及凝并对粒度及粒度分布有着显著的影响.在此基础上,提出了反应沉淀法制备亚微米粒子过程中颗粒粒度及粒度分布的控制方法.     

Sintering characteristics of TiO2 nanoparticles by microwave processing

In many applications, sintering of particles is required to improve device efficiency. In particular, sintering of TiO₂ nanoparticles attracts great attention because of growing of solar cell applications, and conventional sintering using an electrical furnace has been widely used for sintering of nanoparticles. In this study, conventional and microwave sintering processes were investigated to examine the possibility of application of microwave sintering method to TiO₂ nanoparticles. Microwave sintering of TiO₂ nanoparticles showed promising results compared with the conventional heat treatments in terms of surface area, crystalline phase, optical property and morphology. Considering the short sintering time, the microwave method could be more advantageous than the conventional sintering method in some application areas.     

The influence of additives on the primary particle nucleation and agglomeration in poly(vinyl-chloride)

The formation and agglomeration of PVC primary particles were studied in bulk polymerization experiments. In the absence of additives, the primary particles started to agglomerate at low conversions. The agglomeration conversion, as well as the size of the agglomerated particles, decreased when the agitation speed increased. At the highest speed tested, the agglomeration started already at 0.05 percent conversion. The primary particle size was about 0.16-0.18 μm, and seemed to be constant in the conversion interval studied (up to 5 percent). This indicated that the nucleation rate of primary particles was almost constant and that the growth rate of agglomerated particles was very low. The addition of sorbitan monolaurate produced a decrease in primary particle size. Polymeric additives such as PMMA, EVA, and PVAc stabilized primary particles against agglomeration but had no marked effect on the primary particle size. The monomer-soluble fraction of poly(vnyl alcohol-b-vinyl acetate) with high content acetate groups did not affect either the particle size or the agglomeration process.     

Size Distribution Change of Titania Nano-Particle Agglomerates Generated by Gas Phase Reaction,Agglomeration, and Sintering

In the manufacturing of nanometer-sized material particlulates by aerosol gas-to-particle conversion processes, it is important to analyze how the gas-phase chemical reaction, nucleation, agglomeration, and sintering rates control the size distribution and morphology of particles. In this study, titania particles were produced experimentally by the thermal decomposition of titanium tetraisopropoxide (TTIP) and oxidation of titanium tetrachloride (TiCl 4 ) using a laminar flow aerosol reactor. The effect of reaction temperature on the size and morphology of the generated particles was investigated under various conditions. The size distributions of agglomerates were measured using a DMA/CNC system. The size distributions of primary particles were measured using TEM pictures of the agglomerates sampled by a thermophoretic aerosol sampler. In order to model the growth of both agglomerates and primary particles simultaneously, a two-dimensional discrete-sectional representation of the size distribution was employed, solving the aerosol general dynamic equation for chemical reaction, agglomeration, and sintering. Qualitative agreement between the experimentally observed results and the simulation are satisfactory for the large variations in reactor temperature explored.     

Statistical simulation of aluminum agglomeration during combustion of heterogeneous condensed mixtures

A statistical model of aluminum agglomeration during combustion of solid composite rocket propellants is considered; the model describes the process dynamics, beginning from propellant heating in the combustion wave and ending by separation of agglomerates from the burning surface. An algorithm of computing the agglomeration process by the Monte Carlo method is proposed. A series of computations of aluminum agglomeration is performed; the density distribution functions for agglomerate sizes are derived; the dependence of the mean-mass size of agglomerates on the mean-mass size of ammonium-perchlorate particles is determined. The model proposed predicts power dependences of the mean-mass size of agglomerates on pressure and burning rate, which agrees with available experimental data.__________Translated from Fizika Goreniya i Vzryva, Vol. 41, No. 2, pp. 62–74, March–April, 2005.     

Friction and wear behavior of the hybrid PTFE/cotton fabric composites filled with TiO2 nanoparticles and modified TiO2 nanoparticles

A chemical grafting method was applied to modify TiO₂ nanoparticles through covalently introducing glycidoxypropyltrimethoxy silicane (KH560) followed by polyoxymethylene onto the particles to overcome the disadvantages generated by the agglomeration of nanoparticles. TiO₂ nanoparticles unmodified and modified were introduced into hybrid polytetrafluoroethylene (PTFE)/cotton fabric composites. Friction and wear test demonstrated that TiO₂ nanoparticles unmodified and modified can significantly increase the wear resistance of hybrid PTFE/cotton fabric composites but cannot reduce the friction coefficient. Fabric composites filled with grafted TiO₂ nanoparticles exhibited a lower wear rate due to the disintegration of agglomeration and the improvement of interfacial adhesion between filler/matrix. POLYM. ENG. SCI., 2009. © 2008 Society of Plastics Engineers