Empleadores de los graduados de pregrado

The relation between the size of the pellets and their mechanical strength could be learnt from the strength measuring equations reviewed on (section 1.2.4). Furthermore, narrow pellet size distribution is important for (i) the uniformity of coating thickness; (ii) reduction in segregation which could result in non-uniform die or capsule fill, and (iii) to facilitate blending of pellets of different batch or content when required. Ragnarsson and Johansson (1988) observed that the rate of drug release in their multiple unit preparations was influenced by particle size of the coated core material. In an effort to examine the effect of particle size on film coating process Iley (1991) observed a decrease in thickness of coating material with decrease in particle size (700|im to 1000p,m) due to increase in surface area and derived a theoretical equation to relate the particle size and coating thickness of the spheroids. Johansson and Alderbom (1998) studied the effects of MCC pellets size (agglomerated by ethanol/water 70/30) on the mechanism of compaction and the strength of their compacts. They reported that the tensile strength of compacts was independent of the original pellet size at applied pressure of 80MPa. At 160 MPa, however, the tablets made from the larger pellets were significantly stronger. Their reasons were three fold based on the difference in: contact area that lead to force concentration, inter-granular space, and intra- granular porosity. They reported that the porosity changed the pellets densification process. Morever, the process of removing air from the space between pellets was independent of the original pellet size although the larger pellets were deformed to a higher degree during compression.

The size of pellets can be readily expressed in terms of their mean diameter, for they are nearly spherical in shape. However, it is inconvenient and impractical to measure the diameters of every individual pellet and from different direction. Thus, microscopy or sieve analysis is used from which the mean diameter and size distribution is estimated.

The employment of sieve analysis for the characterization of pellets size and size distribution is a common practice for it provides the advantage of inexpensive, simple and rapid process with little variation among operators. However, the blinding of the screen and inability of the sieves to detect variation in the shape of the particles, which allows thin although longer particles to pass are some of the shortcoming of the practice. In a more extensive evaluation

Former et al.(1966) studied the effect of loading, shaker speed, and time of sieving process on the data obtained for the particle size distribution of model granules.

Many researcher (eg. Lundqvist et al. 1997) sieved their pellets according to the British Standard. The retained pellets in the successive sieves were weighed. The data was plotted in either the cumulative percentage over or under weight versus an average pellet size retained in each set. From the resulted sigmoidal curve, interquartile range (the size difference between the 75* percentile and 25* percentile) were determined to indicate the size distribution while the 50* percentile was expressed as the median pellet size. Vertommen and Kinget (1997) used the same technique of sieve analysis but analysed their data using a log-normal frequency distribution, where the logarithm of the mean pellet size in each sieve was plotted against the cumulative percent frequency on a probability scale. Using a linear regression they determined the geometric mean diameter on a weight basis, which is the pellet size at 50% on the probability scale. They illustrated the size distribution by determining the difference in sizes between the pellet size at 84% and 16% (geometrical standard deviation) on the probability scale.

Helen et al. (1993a) employed an optical microscope in conjunction with image analyser to study the number average size and size distribution of their pellets. They determined the mean pellet diameter from the projected perimeter or area diameters as well as from the average of 32 distances between two tangent lines on opposite sides of the pellets, which is commonly known as Feret diameter. Moreover Fielden et al. (1993) used both techniques. The batch weight and number size distribution which were obtained by sieving and by image analysis respectively, and from these, they determined the weight and number median diameters.

1.6.2 THE SHAPE OF PELLET

In all the strength measuring techniques discussed on (section 1.2.4), it was assumed that the shape of the agglomerates were spherical. Thus, determination of the shape of the pellets or investigation on the deviation of their shape from a sphere is important to validate this assumption. Chopra et al. (2001) produced pellets of different shapes by modifying the processing parameters of standardised pellet formulations. The different pellet fractions were then characterised for their size, surface and density properties employing a series of established techniques in order to identify the most appropriate methods of characterisation

and interrelationship between these properties. Their results showed that preparing pellets of graded difference in shape from the same powder blend can result in changes of other important pellet properties such as surface roughness, surface area and pellet dimensions.

Several two-dimensional shape characterizing techniques have been used to express the deviation of pellets from sphericity. One of the most commonly used techniques (eg. York, 1992) is aspect ratio, i. e. the ratio between the longest calliper distance and the calliper distance perpendicular to it. However, a circle, a square or other polygonally symmetric shapes will all have an aspect ratio of 1.0, because in these examples length and breadth are equal. Some other popular techniques are the elongation ratio, a ration between the length and width, (eg. Barrau et al. 1993), and circularity, i.e. the ratio of the area of te pellets to the area of a circle having the same perimeter, (eg. Helen and Yliruusi, 1993).

Chapman et al. (1988) developed a method to characterize the roundness of pellets in terms of the theoretical angle necessary to tilt a plane such that the particle would roll, the “one plane critical stability (OPCS)”. This method was based on the determination of the centre of gravity of the pellet from a digitized image of the coordinates of its outline and computing the angle necessary to incline a plane such that the centre of gravity moves outside the boundary of the pellet. Fielden (1987) employed this technique to assess the sphericity of pellets. Small changes in roundness could be differentiated using OPCS, but each pellet has to be analysed individually and there is a need of special computer system.

Podczeck and Newton (1994) derived a shape factor, e^, a two terms expression, which describes the deviation of a shape from a perfect circle, being very sensitive towards an ellipse. Based on a two-dimensional image analysis, and a term describing the irregularity of the surface of a circle, e^ is expressed as a ratio of the theoretical and measured values of the perimeter, equation (15). •2 InVe. G r = ---

\

where, r^ is a mean radius calculated from the centre of gravity to the perimeter in different directions (eg. 36 measurements 10® apart) using the image analyser, P^, being the measured perimeter, 1 and b are the length and breadth of an ellipse.

The technique was sensitivity on differentiating a set of model shapes, such as circular, square, triangular, diamond, rectangular, stars and flowers of different points and petals (Podczeck and Newton, 1994). Moreover in another work Podczeck and Newton (1995), were able to develop a three dimensional shape factor, 0^3, by measuring the shape factor at a perpendicular angle. This technique gives an assessment of the deviation from the spherical shape as well as the extent of the surface roughness.

In an experiment on nine batches of pellets Podczeck et al. (1995) illustrated the advantage of the e^g over the other techniques in identifying sphericity and surface roughness of pellets. They illustrated the ability of the techniques on differentiating the shape of four nearly spherical pellet batches employing analysis of variance (ANOVA) to test for the significance. They found that none of the batches were differentiated by aspect ratio, e^ was able to differentiate one, while e^g differentiated three of the four batches.

Eriksson et al. (1997) performed a comparison on the sensitivity of elongation ratio, aspect ratio, one-plane critical stability (OPCS), and shape factor, e^, in detecting the sphericity difference between their five pellet batches produced by extrusion and spheronization and a smooth surfaced spherical steel ball bearings. They reported that the elongation and aspect ratio methods were poor in distinguishing the shape variation between pellets of the same batch as well as between the batches. OPCS and the shape factor, e, were, however, able to distinguish the variations between and within the batches. Furthermore, they indicated that only the values obtained from the shape factor, e^, approach were normally distributed, and their results could be investigated using statistical analysis which assumes a normal distribution.

1.6.3 DENSITY AND POROSITY

Density is universally defined as weight per unit volume, the difficulty arises when one attempts to determine the volume of particles, granules or pellets containing microscopic cracks, internal pores, and capillary spaces. For convenience, three types of densities may be defined, the true, granular, and bulk densities.

The true density, p^, is that of actual solid material, exclusive of the voids and inter-particle pores larger than molecular or atomic dimensions in the crystal lattices. In B. S. 2955:1958 it is define as “apparently particle density” due to the difficulty of measuring the true density.

It may be determined by air pycnometer as used by (eg. Wilkberg and Alderbom, 1990a, 1991,1992; Johansson et al. 1995; Johansson and Alderbom, 1996), and helium pycnometer as used by (eg. Kleinebudde, 1994a,b). Since helium is not adsorbed by any material and penetrates into the smallest pores and crevices, it is generally conceded that the helium method gives the closest approximation to the tme density when no intemal pores occur.

The granule density or effective granule density, pg, includes the tme density of the materials and the intemal pores (open and closed pores). This may be determined by liquid displacement method when the material is insoluble. Mercury is commonly used since it fills the void spaces but fails to penetrate into the pores. The effective granule density was measured using mercury pycnometer by Wilkberg and Alderbom (1990a, 1991, 1992), Johansson et al. (1995) and Johansson and Alderbom, (1996) among others. In such technique the pellets are immersed in mercury and the volume displaced is used to determine the effective pellets volume from which the effective density is determined. Selkirk and Ganderton (1970), Bataille et al. (1993) and Kleinebudde (1994a,b) used mercury intmsion porosimetry, where pressure is applied to exclude the open pores of a limited size from the volume of the pellets. The volume of the open pores below that limit, the volume of the pellets together with their closed pores gives the effective pellet volume, and from a knowledge of the weight, the effective pellet density is obtained. The intemal pores or intra- granular porosity, can be determined from:

int

—

P ‘ - P s . P s (16)

=

1

p , p ,

Bulk density, py, is defined as the mass of pellets divided by the bulk volume, which is measured by a standard measuring cylinder. The intergranular porosity or void is the relative volume of intergranular voids to the bulk volume of the pellets, exclusive of the intemal pores. The intergranular space or porosity is computed from a knowledge of the bulk density and the granular density and is expressed by the equation: