Dispersing additives

Additional dispersal problems may occur when the prevailing wind occurs perpendicular to the valley or hill ridgeline. This may lead to speed up and turbulence over the valley or it may simply reduce the effect of airflow carrying away airborne pollutants. 

The table data show that the stress/strain properties of compositions are improved by additional dispersion (mixing). Ultrasonic analysis is sufficiently reliable and informative as a means of mixing quality assessment. The very small change of the characteristics for filled compositions (chalk + kaolin) can be due to the fact that these fillers are readily distributed in the matrix as they are. 

The cost/performance factor of individual surfactants will always be considered in determining which surfactants are blended in a mixed active formulation. However, with the recent advent of compact powders and concentrated liquids, other factors, such as processing, density, powder flowability, water content, stabilization of additives, dispersibility in nonaqueous solvents, dispersion of builders, and liquid crystalline phase behavior, have become important in determining the selection of individual surfactants. 

The minute sample sizes allowed in SFE-SFC analysis (typically 0.5 mg cf. the approximate weight of 30 mg for a single pellet), which is several orders of magnitude smaller than the sample weights used in GC, HPLC or IR analysis (5-10g), allows us to perform additive dispersion studies on a pellet-to-pellet basis [106]. 

A void at the top of the column, (ii), produces additional dispersion. Uneven packing as in (iii) adds to the dispersion and may cause a solute to appear as two or more peaks. 

Under realistic experimental conditions, additional dispersion may well be introduced as a consequence of factors, such as a finite initial width of excess carrier packet or fluctuations in electric field across a specimen film. Most, if not all, of these factors would be expected to yield a relative dispersion that decreases with increasing specimen thickness. In a well-conducted experimental measurement, a relative dispersion of the order of 20% might typically be achieved, giving a transit pulse similar to that shown in Fig. 3.2 (full line) (from which the mean carrier transit time is readily determined). 

Q The most detailed studies were reported by Hermansson and Akesson ( , 41) and Hermansson (42) in which the properties of a soy isolate, caseinate, WPC, and model test systems of additive and lean beef or pork were studied. Solubility, swelling, and viscosity (properties reviewed as related to water absorption) were correlated with moisture loss in the raw systems. In cooked systems, the best predictability of meat texture as affected by additive was a statistical model that included the functional properties of swelling and gel strength of protein additive dispersions. 

Chemical cleaning. The necessary chemical cleaner is added and circulated for 12 to 24 hours, with the bleed valve closed. (Some systems may require the cleaner to be circulated for up to 3 to 4 days or longer, but if so, then ensure that additional dispersant is dosed every 1 to... 

Just as with emulsions and foams, suspensions can exist with additional dispersed phases present. They may contain, in addition to solid particles and a continuous liquid phase (and possibly a stabilizing agent), emulsified droplets and/or gas bubbles. Figure 2.4 (in Section 2.2.1) shows photomicrographs of a practical suspension that contains suspended oil droplets in addition to the particles. The terminology used to describe such systems can become confusing. Consider an aqueous dispersion of solid particles and emulsion droplets. If the solid particles are adsorbed on the emulsion droplets then it is an emulsion that also contains solids. If, however, the particles and droplets are not mutually associated then the system is at once a suspension and an emulsion. Which term is used becomes a matter of choosing the most appropriate context frequently one or the other is considered to be the primary dispersion while the other phase is considered to be an additive or a contaminant. 

The structure of the interfacial layers in food colloids can be quite complex as these are usually comprised of mixtures of a variety of surfactants and all are probably at least partly adsorbed at interfaces which even individually, can form complex adsorption layers. The layers can be viscoelastic. Phospholipids form multi-lamellar structures at the interface and proteins, such as casein, can adsorb in a variety of conformations [78]. Lecithins not only adsorb also at interfaces, but can affect the conformations of adsorbed casein. The situation in food emulsions can be complicated further by the additional presence of solid particles. For example, the fat droplets in homogenized milk are surrounded by a membrane that contains phospholipid, protein and semi-solid casein micelles [78,816], Similarly, the oil droplets in mayonnaise are partly coated with granular particles formed from the phospho and lipo-protein components of egg yolk [78]. Finally, the phospholipids can also interact with proteins and lecithins to form independent vesicles [78], thus creating an additional dispersed phase. 

Although the Bergman-Hynen statistic provides a clever correction to some problems with the Box-Meyer statistic, it remains problematic in the face of multiple dispersion effects (see Brenneman and Nair, 2001 and McGrath and Lin, 2001). If factor j alone has a dispersion effect, the numerator and denominator of the statistic D H in (7) are unbiased estimators of the variances at the high and low levels of j. However, if several factors have dispersion effects, one has instead unbiased estimates of the average variances at these two levels, where the averaging includes the effects of all the other dispersion effects. This dependence of I)1-11 on additional dispersion effects can lead to inflated type I error probabilities and thus to spurious identification of dispersion effects. 

To check for possible additional dispersion effects, fit joint location-dispersion models. These methods are easier to implement than the statistics proposed for identifying multiple dispersion effects. They give both estimates of the strength of the dispersion effects and approximate test statistics. 

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