Björn Niemann

Nanoparticle Precipitation in Reverse Microemulsions: Particle Formation Dynamics and Tailoring of Particle Size Distributions

In this work, a detailed experimental analysis of the nanoparticle formation dynamics and the formation mechanism in a reverse microemulsion system is given. The precipitation of barium sulfate nanoparticles inside microemulsion droplets is investigated at the molecular scale with respect to the evolution of the particle size distribution and the particle morphology by an extensive transmission electron microscope (TEM) analysis. Different mixing procedures (feeding strategies) of two reactants, barium chloride and potassium sulfate, are evaluated concerning their ability for a tailored particle design under consideration of the complete particle size distribution (modality and polydispersity). It is shown that improved knowledge about the particle formation mechanisms, the dynamics, and the influence of the colloidal microemulsion structure could be used for a tailored design of particles,for example, controlled synthesis of nanoparticles with a bimodal particle size distribution by the application of a sophisticated feeding strategy.

Nanoparticle precipitation in microemulsions: Population balance model and identification of bivariate droplet exchange kernel

A discrete population balance model is derived in order to investigate nanoparticle precipitation inside droplets of a water-in-oil microemulsion. Special attention is being paid to the droplet exchange. This mechanism is responsible for the mixing of the reactants before precipitation starts. Thus, a very precise determination is important for the subsequent steps nucleation and growth which depend on the supersaturation being a function of the reactant concentrations inside the droplets. For a reliable evaluation of the droplet exchange a bivariate population balance for the two dissolved reactants and a one-dimensional population balance comprising the particle size has been formulated. The coalescence and redistribution of the droplets is described by a two-dimensional agglomeration-type term. Different bivariate kernel functions for a situation with the low soluble product BaSO(4) and its two reactants BaCl(2) and K(2)SO(4) have been analyzed within this term. In conclusion, it was found that a kernel with a linear profile for the attractive forces between the two reactants fits experimental findings best. It takes into account that the redistribution of two merged mother droplets is different for the case that only one of the two reactants exists in the merged droplets and the case that a satisfactory amount of both reactants to form a stable nucleus appears inside them. An equal redistribution in the daughter droplets happens in the first case and an accumulation inside one daughter droplet in the second case. Intermediate states are obtained by the linear profile of the kernel function between these two limiting cases.

CFD model of a semi-batch reactor for the precipitation of nanoparticles in the droplets of a microemulsion

Abstract Precipitation inside the droplets of a microemulsionis a promising technology for the production of nanoparticles with tailored properties, like particle size or shape [1]. In this work, a water-in-oil (w/o)-microemulsion consisting of water, cyclohexane and the non-ionic technical surfactant Marlipal O13/40 is used to synthesise BaSO 4 nanoparticles. The reaction is initiated by the mixing of two microemulsions, one containing the first reactant BaCl 2 and the other containing the second reactant K 2 SO 4 , in semi-batch operation mode in a standard Rushton tank (V=300 ml). A narrow particle size distribution (Fig. 1) and particle sizes between 4 and 40 nm have been achieved by experiments [2].

Methods of state estimation for particulate processes

Abstract Determining property distributions of particles online by measurement is difficult in many cases, especially if the particles are in the nanometre range. An alternative may be state estimation techniques, which use information from process simulations in addition to the measurement signals. Two examples of state estimators for particulate processes are presented in this contribution. The first one is an extended Kalman filter based on a population balance model. The second one is a bootstrap filter based on a Monte Carlo simulation.