Experimental and numerical insights into the formation of zirconia nanoparticles: a population balance model for the nonaqueous synthesis

Stolzenburg, Pierre; Garnweitner, Georg

doi:10.1039/c7re00005g

Cited by 17 publications

(16 citation statements)

References 49 publications

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“…2 underpinned the independence of the process kinetics in terms of the initial precursor concentrations under the investigated process conditions. Similar findings of strong dependence on the temperature but low influence of the precursor concentration on the reaction kinetics have also been observed for the non-aqueous formation of ZrO 2 nanoparticles in benzyl alcohol [20]. The successful description of the AZO synthesis using the Arrhenius model showed, on the one hand, the reproducibility and applicability of the measuring methods used over an extended process control range.…”

Section: Behavior Of the Reaction Kinetics In The Low Process Temperasupporting

confidence: 76%

Formation of Aluminum‐Doped Zinc Oxide Nanocrystals via the Benzylamine Route at Low Reaction Kinetics

et al. 2020

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The influence of essential process parameters on the adjustability of specific process and particulate properties of aluminum-doped zinc oxide (AZO) nanocrystals during synthesis via the benzylamine route at low reaction kinetics is demonstrated by enabling time-resolved access of the selected measurement technique. It is shown that the validity of the pseudo-first-order process kinetics could be extended to the minimum operable reaction kinetics. On the other hand, the impacts of the process temperature and the initial precursor concentration on both the process kinetics and the particle morphology are discussed. The obtained data provide a versatile tool for precise process control by adjusting defined application-specific particle properties of AZO during synthesis.

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Section: Behavior Of the Reaction Kinetics In The Low Process Temperasupporting

confidence: 76%

Formation of Aluminum‐Doped Zinc Oxide Nanocrystals via the Benzylamine Route at Low Reaction Kinetics

et al. 2020

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“…The number and complexity of nanostructures that it is possible to synthesize without a deep understanding of the physical and chemical aspects involved in their synthesis processes is nevertheless impressive; the situation is well described by Xia et al [12] who report that "at the current stage of development, it is not an exaggeration to say that the chemical synthesis of metal nanocrystals (as well as for other solid materials) remains an art rather than a science". In recent years an increasing interest in the modeling of these syntheses has appeared, generally based on the use of population balance modelling [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Modelling the synthesis of nanoparticles in continuous microreactors: The role of diffusion and residence time distribution on nanoparticle characteristics

Panariello

Mazzei

Gavriilidis

2018

Chemical Engineering Journal

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A mathematical model for the liquid-phase synthesis of spherical nanoparticles in continuous flow microand milli-reactors was developed, accounting for residence time distributions (RTD). These distributions were derived for the reactants and the particles involved in the synthesis. The kinetic parameters needed to describe the reactions were calculated from experimental data available in the literature from batch reactors, with the aid of population balance modelling. They were subsequently used, without further modification, to simulate the synthesis in flow reactors via population balance modelling, averaging the results at the reactor outlet using the reactor RTDs. The model was employed to describe the synthesis of silica nanoparticles as a case study, and validated against flow reactor results from the literature. The results demonstrate direct RTD effects and indirect diffusion-induced effects on the evolution of the particle size distribution during the flow synthesis. The former are created by the inherent widening of the RTDs, due to the different residence time experienced by each fluid element, leading to a broadening of the particle size distribution. The latter are induced by the difference in diffusivity between nanoparticles and liquid reactants, which leads to different dispersion processes in the reactor for the different components of the reaction mixture. These effects appear as the major cause of particle size distribution difference between (single phase) flow and batch syntheses. Highlights • A model for the design of microreactors synthesising nanoparticles is proposed; • The model utilises only kinetics and diffusion parameters from the literature; • Residence time distribution effects on nanoparticle size distribution is quantified; • Effects of transport phenomena on reaction kinetics in the flow synthesis are shown.

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“…Nevertheless, before employing the synthesis in the microfluidic system, we evaluated the reaction times of the model systems by performing in situ turbidity measurements in a 250 mL steel reactor equipped with a laser-detector system which was designed for previous studies to investigate the particle formation mechanisms during the nonaqueous synthesis (Stolzenburg and Garnweitner, 2017).…”

Section: Materials and Experimental Conditionsmentioning

confidence: 99%

Microfluidic synthesis of metal oxide nanoparticles via the nonaqueous method

Stolzenburg

Lorenz

Dietzel

et al. 2018

Chemical Engineering Science

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Microfluidic synthesis allows for a good control of the particle formation conditions while minimizing the consumption of material. In this study, we exploited these advantages for the nonaqueous synthesis of TiO 2 , ZnO and CeO 2 nanoparticles in a closed micro droplet reactor which resulted in well-defined particle structures. Monodisperse droplets are generated in microfluidic flow-focusing area and

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Experimental and numerical insights into the formation of zirconia nanoparticles: a population balance model for the nonaqueous synthesis

Abstract: The nonaqueous synthesis of zirconia nanoparticles was investigated and modeled by a comprehensive population balance equation framework that simulates the entire particle formation process to predict final nanoparticle properties as well as their evolvement during the synthesis.

Cited by 17 publications

References 49 publications

Formation of Aluminum‐Doped Zinc Oxide Nanocrystals via the Benzylamine Route at Low Reaction Kinetics

Formation of Aluminum‐Doped Zinc Oxide Nanocrystals via the Benzylamine Route at Low Reaction Kinetics

Modelling the synthesis of nanoparticles in continuous microreactors: The role of diffusion and residence time distribution on nanoparticle characteristics

Microfluidic synthesis of metal oxide nanoparticles via the nonaqueous method

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