Modelling polydisperse nanoparticle size distributions as produced via flame spray pyrolysis

Neto, Pedro Bianchi; Meierhofer, Florian; Meier, Henry França; Fritsching, Udo; Noriler, Dirceu

doi:10.1016/j.powtec.2020.05.019

Cited by 22 publications

(11 citation statements)

References 42 publications

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“…The PBM code was then coupled with CFD to elucidate the effect of the inlet coating vapor concentration, mixing intensity, and reactor geometry on the thickness, texture, and efficiency of the coating. , The predicted fraction of non-hermetic coatings was in good agreement with experimental data, , suggesting that this CFD–PBM approach is highly suitable to design and optimize aerosol-coating reactors. Recently, Bianchi Neto et al implemented a DQMOM-based PBM into RANS CFD to describe ZrO 2 nanoparticle growth in FSP. The experimental logarithmic particle size distribution could be matched with 80–90% accuracy by adjusting the quadrature approximation order from 1 and 3.…”

Section: Principles In Process Design and Computational Reactor Modelingmentioning

confidence: 99%

Synthesis of Metal Oxide Nanoparticles in Flame Sprays: Review on Process Technology, Modeling, and Diagnostics

Meierhofer

Fritsching

2021

Energy Fuels

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The gas synthesis of nanoparticles has gained major interest by different industries and research groups for the development of new materials and their subsequent implementation in numerous devices and applications. Actually, there are numerous research and application activities in flame-based nanoparticle production, mainly focusing on the process and its development, the in situ and ex situ analyses in experiments and simulations and new material and material combination developments. This review provides an overview on the flame synthesis of metal oxide nanoparticles and focuses in particular on the process design of flame spray pyrolysis (FSP). The first part of this review describes the historical background of flame-based particle production and further summarizes the emerging developments, achievements, and trends during the past 2 decades. The second part covers a general process overview of the flame aerosol-based manufacturing by explaining the key aspects of synthesis (chemical raw materials, metal precursors, solvents, and reactor configurations), in situ conditioning approaches, and powder and byproduct collection, along with additional post-processing steps (storage, handling, and modification). Applications of flame-made nanoparticles involve catalysts, gas sensors, energy storage materials, and nanotoxicity studies, among others, which will not be discussed in detail but are overviewed by listing the latest reviews of the respective field. The third part focuses on the growth mechanisms of solid particle products in liquid droplet/spray pyrolysis (liquid-to-particle and gas-to-particle conversion). The concept of characteristic process times is then exemplified for a lab-scaled FSP reactor, while afterward, particle dynamics and continuum models for reactor simulation are reviewed. The fourth part is a survey of more than 20 different diagnostic techniques that are commonly employed for characterization of reactive sprays and flame-made powders. We briefly explain the metrology basics of each method, indicate advantages and limitations, and present exemplary measurement results that have been acquired on flame reactor facilities for particle synthesis. Finally, we summarize the review and address some current challenges that might potentially shape the near future of nanoparticle synthesis in flame sprays.

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Section: Principles In Process Design and Computational Reactor Modelingmentioning

confidence: 99%

Synthesis of Metal Oxide Nanoparticles in Flame Sprays: Review on Process Technology, Modeling, and Diagnostics

Meierhofer

Fritsching

2021

Energy Fuels

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“…The model used to characterize the FSP process combines Eulerian and Lagrangian frameworks under steady conditions, as described by Bianchi Neto et al [12]. The former is applied to characterize the compressible gas phase as a continuum (considering equations for total mass, momentum, energy, and chemical species).…”

Section: Mathematical Modeling and Numerical Methodsmentioning

confidence: 99%

“…For the precursor-solvent mixture, a solution of zirconium n-propoxide (IV) in ethanol and npropanol at 0.5 M (72% C 2 H 5 OH, 8% C 3 H 7 OH and 20% C 12 H 28 O 4 Zr, in mass) is considered in order to produce nanoparticles of zirconium dioxide. The liquid droplets are injected from a conical surface positioned on top of the nozzle [16], and the initial droplet size distribution is represented by a Rosin-Rammler-Sperling-Bennett (RRSB) function [12]. A mixture of methane and oxygen (19% and 81%, in mass, respectively) composes the pilot flame, and the chemical reactions associated with the process are considered as presented by Bianchi Neto et al [12].…”

Section: Mathematical Modeling and Numerical Methodsmentioning

confidence: 99%

“…The liquid droplets are injected from a conical surface positioned on top of the nozzle [16], and the initial droplet size distribution is represented by a Rosin-Rammler-Sperling-Bennett (RRSB) function [12]. A mixture of methane and oxygen (19% and 81%, in mass, respectively) composes the pilot flame, and the chemical reactions associated with the process are considered as presented by Bianchi Neto et al [12].…”

Section: Mathematical Modeling and Numerical Methodsmentioning

confidence: 99%

“…In that sense, this study aims to generate a polydisperse solution for the PBM, coupled to computational fluid dynamics (CFD), to represent nanoparticle formation and evolution inside an enclosed FSP reactor, and other aspects of the turbulent reactant flow. For that, the model presented by Bianchi Neto et al [12] will be applied to the desired geometric configuration. Experimental data presented by Buss et al [11] are compared to the obtained numerical results for validation.…”

Section: Introductionmentioning

confidence: 99%

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Modelling polydisperse nanoparticle production in an enclosed flame spray pyrolysis reactor

Neto

Buss

Fritsching

et al. 2021

ICLASS

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In recent years, the scientific interest in population balance models (PBM) is increasing in many research fields, from environmental studies to material production. This technique allows for a precise prediction of population characteristics and behaviour and is a precious ally in process development. Combining PBM and computational fluid dynamics (CFD) has proved very helpful for designing and improving new equipment. That also applies to the production of nanoparticles via the flame spray pyrolysis (FSP) process. The use of reactors with enclosed flames, which allows the fine control of the oxidant in the flame reaction zone, for instance, could benefit from better numerical models for nanomaterial development prediction. In this work, a previously developed model was adapted for the enclosed reactor configuration. In the model, an Eulerian framework is applied to characterize the continuous gas phase, while a Lagrangian framework represents the discrete droplets of the burning spray. A bivariate PBM describes the solid phase, and its polydisperse solution was achieved using the direct quadrature method of moments (DQMoM). Numerical results are validated through comparison with experimental data available in the literature regarding nanoparticle size, presenting good agreement.

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Numerical prediction of cyclone efficiency curve using an Eulerian-Eulerian approach

Mendes

Noriler

2022

Advanced Powder Technology

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Modelling polydisperse nanoparticle size distributions as produced via flame spray pyrolysis

Cited by 22 publications

References 42 publications

Synthesis of Metal Oxide Nanoparticles in Flame Sprays: Review on Process Technology, Modeling, and Diagnostics

Synthesis of Metal Oxide Nanoparticles in Flame Sprays: Review on Process Technology, Modeling, and Diagnostics

Modelling polydisperse nanoparticle production in an enclosed flame spray pyrolysis reactor

Numerical prediction of cyclone efficiency curve using an Eulerian-Eulerian approach

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