Particles properties

Heisenburg uncertainty principle For small particles which possess both wave and particle. properties, it is impossible to determine accurately both the position and momentum of the particle simultaneously. Mathematically the uncertainty in the position A.v and momentum Ap are related by the equation... 

Fluidized-bed design procedures requite an understanding of particle properties. The most important properties for fluidization are particle size distribution, particle density, and sphericity. 

Fig. 3. Zenz plot. Correlation of bed voidage, S, the volume fraction of the fluidized bed that is occupied by gas, for values of S from 1.0 to 0.5, and dimensionless velocity and particle properties, where p — p )/(3 PgU ). The horizontal lines represent the different values of e = 0.5...

Figure 18 is an entrainment or gas-carryiag capacity chart (25). The operating conditions and particle properties determine the vertical axis the entrainment is read off the dimensionless horizontal axis. For entrainment purposes, the particle density effect is considered through the ratio of the particle density to the density of water. When the entrainable particle-size distribution is smaller than the particle-size distribution of the bed, the entrainment is reduced by the fraction entrainable, ie, the calculated entrainment rate from Figure 18 is multipfled by the weight fraction entrainable. 

In sohd—sohd separation, the soHds are separated iato fractions according to size, density, shape, or other particle property (see Size reduction). Sedimentation is also used for size separation, ie, classification of soHds (see Separation, size separation). One of the simplest ways to remove the coarse or dense soHds from a feed suspension is by sedimentation. Successive decantation ia a batch system produces closely controUed size fractions of the product. Generally, however, particle classification by sedimentation does not give sharp separation (see Size MEASUREMENT OF PARTICLES). 

The basis of all bulk conveyor engineering is the precise definition and accurate classification of materials according to individual characteristics under a specific combination of handling conditions (1). Since the late 1960s there has been an extraordinary growth in research into the fundamental properties and behavior of particulate soHds. However, as of this writing, it is not possible to predict the handling behavior of a bulk soHds material relevant to conditions in a specific conveyor, merely on the basis of the discrete particle properties. 

Minimum Fluidizing Velocity U,nj, the minimum fluidizing velocity, is frequently used in fluid-bed calculations and in quantifying one of the particle properties. This parameter is best measured in small-scale equipment at ambient conditions. The correlation by Wen audYu [A.l.Ch.E.j., 610-612 (1966)] given below can then be used to back calculate d. This gives a particle size that takes into account effects of size distribution and sphericity. The correlation can then be used to estimate U, at process conditions, if U,nj cannot be determined experimentally, use the expression below directly. 

The conventional scale-up criteria scale-up with constant stirrer speed , scale-up with constant tip speed and scale-up with constant specific energy input are all based on the assumption that only one mixing process is limiting. If, for example, the specific energy input is kept constant with scale-up, the same micromixing behaviour could be expected on different scales. The mesomixing time, however, will change with scale-up as a result, the kinetic rates and particle properties will be different and scale-up will fail. 

The model is able to predict the influence of mixing on particle properties and kinetic rates on different scales for a continuously operated reactor and a semibatch reactor with different types of impellers and under a wide range of operational conditions. From laboratory-scale experiments, the precipitation kinetics for nucleation, growth, agglomeration and disruption have to be determined (Zauner and Jones, 2000a). The fluid dynamic parameters, i.e. the local specific energy dissipation around the feed point, can be obtained either from CFD or from FDA measurements. In the compartmental SFM, the population balance is solved and the particle properties of the final product are predicted. As the model contains only physical and no phenomenological parameters, it can be used for scale-up. 

Gertlauer, A., Mitrovic, A., Motz, S. and Gilles, E.-D., 2001. A population balance model for crystallization processes using two independent particles properties. Chemical Engineering Science, 56(7), 2553-2565. 

PSS has developed proprietary packing procedures for its sorbents, that allow a homogeneous filling of the column hardware with no change in particle properties. This thoroughness in the packing procedure is reflected in the superior performance of PSS SEC columns and their long life, even in difficult conditions. 

Dynamic information such as reorientational correlation functions and diffusion constants for the ions can readily be obtained. Collective properties such as viscosity can also be calculated in principle, but it is difficult to obtain accurate results in reasonable simulation times. Single-particle properties such as diffusion constants can be determined more easily from simulations. Figure 4.3-4 shows the mean square displacements of cations and anions in dimethylimidazolium chloride at 400 K. The rapid rise at short times is due to rattling of the ions in the cages of neighbors. The amplitude of this motion is about 0.5 A. After a few picoseconds the mean square displacement in all three directions is a linear function of time and the slope of this portion of the curve gives the diffusion constant. These diffusion constants are about a factor of 10 lower than those in normal molecular liquids at room temperature. 

Type Particles Particles Particles Properties Examples... 

The degree of bed expansion contributes to the efficiency of fluidised bed/expanded bed adsorption as a composite function of liquid distribution, liquid and particle properties (size, shape and density) and process conditions. Besides being an important design feature, the degree of bed expansion may be used as a quick and simple measure of bed stability.48... 

Although both of these models provide a reasonable description of the precipitation polymerization process, they do not illustrate the relationship between the reactor variables and the polymer particle properties. 

The effect of the particle properties on the overall heat-transfer coefficient was investigated in our laboratory (43) for an acrylic precipitation polymerization as shown in Figure 6. 

B bulk property d deactivation e effective property G gas phase i component index i reaction index L liquid phase p catalyst particle property equilibrium conditions... 

For L=NH3 (1) and L=Pr2NH (3), the isotherms are of type II as expected for non-porous materials [27]. Sample 2 shows a significant uptake at 0.6

narrow particle-size distribution which results in a more regular packing with interparticle pores of size similar to that of the particles [27]. The latter shows that the ligand-assisted synthesis does not only allow one to affect the total surface area and particle size, but also the size distribution which is an important tool for tailoring the particle properties. 

We are used to thinking of electrons as particles. As it turns out, electrons display both particle properties and wave properties. The French physicist Louis de Broglie first suggested that electrons display wave-particle duality like that exhibited by photons. De Broglie reasoned from nature s tendency toward symmetry If things that behave like waves (light) have particle characteristics, then things that behave like particles (electrons) should also have wave characteristics. 

From Single Particle Properties to Collective Charge Transport... 

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