Particle formation spatial distribution

Each stage of particle formation is controlled variously by the type of reactor, i.e. gas-liquid contacting apparatus. Gas-liquid mass transfer phenomena determine the level of solute supersaturation and its spatial distribution in the liquid phase the counterpart role in liquid-liquid reaction systems may be played by micromixing phenomena. The agglomeration and subsequent ageing processes are likely to be affected by the flow dynamics such as motion of the suspension of solids and the fluid shear stress distribution. Thus, the choice of reactor is of substantial importance for the tailoring of product quality as well as for production efficiency. 

The mass transfer effect is relevant when the chemical reaction is far faster than the molecular diffusion, i.e. Ha > 1. The rapid formation of precipitate particles should then occur spatially distributed. The relative rate of particle formation to chemical reaction and/or diffusion can as yet be evaluated only via lengthy calculations. 

The existence of the (quasi) steady-state in the model of particle accumulation (particle creation corresponds to the reaction reversibility) makes its analogy with dense gases or liquids quite convincing. However, it is also useful to treat the possibility of the pattern formation in the A + B —> 0 reaction without particle source. Indeed, the formation of the domain structure here in the diffusion-controlled regime was also clearly demonstrated [17]. Similar patterns of the spatial distributions were observed for the irreversible reactions between immobile particles - Fig. 1.20 [25] and Fig. 1.21 [26] when the long range (tunnelling) recombination takes place (recombination rate a(r) exponentially depends on the relative distance r and could... 

A computer model has been developed to provide numerical simulations of fluidized bed coal gasification reactors and to yield detailed descriptions, in space and time, of the coupled chemistry, particle dynamics and gas flows within the reactor vessels. Time histories and spatial distributions of the important process variables are explicitly described by the model. With this simulation one is able to predict the formation and rise of gas bubbles, the transient and quasi-steady temperature and gas composition, and the conversion of carbon throughout the reactor. 

It is evident that the properties of nanoparticle structures depend not only on the individual particle size, particle shape, or the degree of size dispersion but also on their spatial distribution and the degree of aggregation or film formation. Therefore, before investigating the optical and electronic properties of nanoparticles, it is important to gain knowledge of how to control the particle size, particle density, and spatial distribution. 

Conversely, the in situ synthesis is a simple and effective route to prepare MNPs that allows fabrication in a one-step procedure from corresponding precursors. In addition, the nanoparticles are grown inside the polymer matrix, leading to the formation of composite materials that can directly serve for a spedlic purpose. For this reason, in situ approaches are preferred because they prevent particle agglomeration, while maintaining a good spatial distribution in the polymer matrix. 

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