particle size analysis-2011

8/13/2019 Particle Size Analysis-2011

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Dr Ali Nokhodchi

PARTICLE SIZEANALYSIS

-Particle shape

-Particle size distribution

-Particle size measurements


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Dr Ali Nokhodchi 2

Particle size Analysis

Primary characteristics, relating tobasic material properties, particulate size,shape and surface area

Secondary characteristics, behaviouralproperties such as flow, bulk and tappeddensity, compactibility, lubricity


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Dr Ali Nokhodchi 3

The ideal particle and reality

Sol id Sphere

One dimension (radius) describessize, shape, surface area, volume

Sol id Geometr ical Part ic les

Two-three dimensions required todescribe size, shape, surface area,

volume

r

r

hh

h

wl r

Solid Irregu lar Partic lesSize and shape can only be

approximated, surface area and

volume can be measured

Porou s Irregular Part ic lesSize, shape, surface area and

volume can only be approximated


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Dr Ali Nokhodchi 4

Particle Shape TerminologyAcicular: needle-shaped

Angular : sharp edged; having a roughly polyhedral shape

Dendritic: having a branched crystalline shape

Fibrous: thread-likeFlaky : plate-like

Granular : irregular but of approximately spherical overall form

Irregular : lacking any symmetry

Modular : having a rounded, irregular shape


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Dr Ali Nokhodchi 5

Particle shape

The thickness is the height of the particlewhen it is resting in its position of maximumstability.

The breadth is the minimum distance betweentwo tangential planes which are perpendicular

to those defining the thickness and breadth.

The length is the distance between two planeswhich are perpendicular to those defining thethickness and breadth.

x

yz


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Dr Ali Nokhodchi 6

Shape factors

Elongation ratio = length/breadth

Flakiness ratio = breadth/thickness

Bulkiness factor= projected area/(length xbreadth)

The ratio of two equivalent diameters obtained by differentmethods is termed a shape factor.


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Dr Ali Nokhodchi 7

Circularity

Circumscribed circle (dc)

Inscribed circle (di)

dc=10 mmdi= 1 mm

Circularity=0.1

dc=10 mmdi= 5 mmCircularity=0.5

P i l i i fl di l i


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Dr Ali Nokhodchi 8

Particle size influences dissolution

DissolutionSmall particles dissolves more rapidly than larger ones.

Drug in Solid

Dosage Form

Drug Crystals

Exposed to GI Fluids

Drug Dissolved

In GI Fluids

Drug in

Blood

Dissolution Absorption

Release from

Dosage Form


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Dr Ali Nokhodchi 9

Particle size effect on bioavailability of a poorly

soluble drug

Particle sizedissolution rate ?Bioavailability ?

Other examples:

Tetracycline

Aspirin

Sulphonamides

Digoxin

Dicoumarol


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Dr Ali Nokhodchi 10

1 g

1 cm

1 cm

1 cmSurface area = area/face 6 faces = 6 cm2

Weight-specific surface area = Sw = [surface area/weight]

Therefore , Sw = 6 cm2 /g


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Dr Ali Nokhodchi 11

The total surface area in 1 g is now: [1012 cubes][6 x 10-8 cm2/cube]

Sw = 6 104 cm2 = 60000 cm2


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Dr Ali Nokhodchi 12

Range of particle size and units

Pharmaceutical system usually are confined to anarrower size range

Most pharmaceutical systems lie in the range 1mm to 10 mm

Colloidal range is considered less than 1 mm 1 mm forms useful boundary since the properties

of colloidal materials are often very different tothose of coarser systems, and the techniquesused to study them are quite distinct from thoseused for larger particulates


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Dr Ali Nokhodchi 13

Individual particle characteristics

The way that we characterise the particles largely

depends on the technique used to measurethem.

The way that we measure a particle size is as

important as the value of the measured size. Forexample, how would you quantify yourself ifmeasured by

1) Circumference around your waist?

2) Diameter of a sphere of the same displacement

volume as your body? 3) Length of your longest chord (height)?


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Dr Ali Nokhodchi 14

As you can deduce, the measured values have different

meanings and wil l be important relative to those meanings. I f

you are sizing a li fe jacket belt you would be interested in the

f irst size (circumference around your waist). I f you are

buying a sleeping bag I suggest the last one (length of your

longest chord) .We have already been using a length to describeparticle size, with the intention that it indicatesthe distance from one side of the particle to itsopposite side.The description is unambiguous in the case of aspherical particle, example: emulsion droplet or

microsphere.

Measures of Particle size


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Dr Ali Nokhodchi 15

The ideal particle and reality

Sol id Sphere

One dimension (radius) describessize, shape, surface area, volume

Sol id Geometr ical Part ic les

Two-three dimensions required todescribe size, shape, surface area,

volume

r

r

hh

h

wl r

Solid Irregu lar Partic lesSize and shape can only be

approximated, surface area and

volume can be measured

Porou s Irregular Part ic lesSize, shape, surface area and

volume can only be approximated


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Dr Ali Nokhodchi 16

Particle sizing of powders

1 cm

1 cm

1 cm

Large, geometric objects:Size is described in three

dimensions (minimum)

Small, irregular particles:Three dimensional size

description is impractical,

only one dimension

(average diameter) is used.

1 m


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Dr Ali Nokhodchi 17

What is the diameter of this particle?

1

23

4

What should we do if the particle is irregular?


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Dr Ali Nokhodchi 18

Measures of Particle size

The problem can be solved by quoting theparticle size of a non-spherical particle asthe diameter of a sphere which is in some

way equivalent to the particle; such asphere is termed an equivalent sphereand the diameter is an equivalentdiameter.


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Dr Ali Nokhodchi 19

Measures of Particle size Example:

Weigh a particle

measure its density

Find the particle volume

The volume equivalent sphere is the spherewhich has the same volume as the irregularparticle, and is characterised by the volumeequivalent diameter.

Mass= 1gDensity=2 g/cm3

Volume= 0.5 cm3

V=(4/3)r3

0.5= (4/3) 3.14r3

R3= (0.5/4.19)=0.119r=0.49 cmDiameter = 0.98 cm


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Dr Ali Nokhodchi 20

r

h

r

h

r

h

Size of cylinder Aspect

ratio

Equivalent

spherical

diameter

(m)

Height

(m)

Diameter

(m)

20 20 1:1 22.9

40 20 2:1 28.8

100 20 5:1 39.1

For cylinder:

Radius= 10 m, h= 100 m

Volume of cylinder

Volume of cylinder = 3.14 (10)2x100

Volume of cylinder = 3.14 x 10000

Volume of sphere =

Volume of sphere =(4/3) 3.14 x r3

In equivalent assumption both volume

should be the same

(4/3) x 3.14 x r3 =3.14 x 10000

4r3 = 3x10000

r3= 30000/4

r3= 7500 r=19.5 diameter= 2 x r

diameter = 39.1 m


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Dr Ali Nokhodchi 21

Selected ESDs

da (the projected area diameter): the diameterof a sphere having the same projected area asthe particle in question

dv (the volume diameter) the diameter of a

sphere having the same volume as the particle ds (the surface diameter) the diameter of a

sphere having the same surface area as theparticle

dst (the stokes diameter) the diameter of a

sphere having the same density and free-fallvelocity in given fluid as the particle

dsieve (the sieve diameter) the diameter of asphere that is just able to pass through thesame square aperture as the particle


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Illustrations of equivalent diameters

dp da dv dsa dmass ds

xm/s2dst


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Selected EDSs (continued)

dF (Ferets diameter) the (mean) value betweenpairs of parallel tangents to the projected outlineof the particle

dM (Martins diameter) the (mean) chord lengthof the projected outline of the particle

dd (aerodynamic diameter) the diameter of asphere with the same viscous drag as a particlein a fluid at the same viscosity

dM1

dF1

dM2

dF2

dM3

dF3

* Ferets and Martins diameters are

taken from a statistical mean of

diameters measured from different

particle orientations.


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Dr Ali Nokhodchi 24

There are many different ways of defining equivalent

diameters. All of these diameters will generally be

different-unless the particle really is a sphere:

Projected area diameter da Projected perimeter diameter dp


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Dr Ali Nokhodchi 26

ESDs for a simple shape

Bear in mind that all equivalent diameters available to us will be differentfor a given irregular particle.

Select an equivalent diameter (and associated measurement technique)which is relevant to the property of the particle that we are interested in.


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Dr Ali Nokhodchi 27

To obtain a good description of the particle system, we must selectan equivalent diameter (and associated measurement technique)

which is relevant to the property of the particle in which we are

interested.

e.g. Paint pigment particles: Projected area diameter

measured by microscopy.

Aerosol deposition in the lungs: Aerodynamic diameter

measured by inertial impaction methods.

Sedimentation properties of the material: Stokesdiameter

Which equivalent diameter do you

use?


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Dr Ali Nokhodchi 28

Arithmetic mean

The mean value is the center of gravity of thedistribution. It is calculated using thefollowing equation:

1

21 2 3 4 5 6 7 8 9 10 11

6 6 6 6 6 6 6 6 6 6 6

Calculate the arithmetic mean for the particle populations 1 & 2.

Arithmetic Mean (d = ESD)


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Dr Ali Nokhodchi 29

Arithmetic Mean (d = ESD)

Number-length mean diameter which is

arithmetic mean of a number distribution

of length; D [1, 0]

1 m 3 m 5 m 8 m 10 m

Calculate the arithmetic mean for the above particle populations.

3x1 + 1x3 + 5x5 + 2x8 + 2x10 = 67Dav = [67/13] = 5.15 m


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Dr Ali Nokhodchi 30

Assume a simple situation of 10particles having the following length

(i.e., ESDs) in m :

1, 3, 3, 4, 5, 2, 2, 6.5, 6, 5

What are the arithmetic and geometric means?

Arithmetic

mean


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Dr Ali Nokhodchi 31

Geometric mean

Thegeometric mean is calculated using thefollowing equation:

Calculate the geometric mean for the particle populations 1 & 2.

1

21 2 3 4 5 6 7 8 9 10 11

6 6 6 6 6 6 6 6 6 6 6

Geometric Mean (d =ESD)


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Dr Ali Nokhodchi 32

Geometric Mean (d =ESD)

1 um 3 um 5 um 8 um 10 um

Calculate the geometric mean for the particle populations 1 & 2.

log dg = 1/12 x [3xlog1 + 1xlog3 + 4xlog5 + 2xlog8 + 2xlog10]log dg = 1/12 x [0 + 0.48 +2.80 + 1.81 + 2 ]log dg = 0.59

dg = 3.89 m


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Geometric mean


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Dr Ali Nokhodchi 34

Number Surface area mean diameter

D [2, 0]

Thesurface mean diameter (SMD) isthe calculated using the followingequation:

Calculate the SMD for the particle populations 1 & 2.

SMD = n1d12 + n2d22 + n3d32 + = nd

2

n n

where n1, n2, = number of particles in size

groups d1, d2 , and n is the total number

of particles (i.e., n1 + n2 + )

N b V l /M di


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Dr Ali Nokhodchi 35

Number-Volume/Mass mean diameter

D [3, 0]

The volume-number mean diameter is the diameter of a particle having

average weight (i.e. weight = volume density)

therefore, dnv is uniquely related to Nw, the specific particle number

VMD =n1d13 + n2d23 + n3d33 +

=nd3

n n


groups d1, d2 , and n is the total number of

particles (i.e., n1 + n2 + )

3 3


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Dr Ali Nokhodchi 36

Volume-surface mean diameter; D [3, 2]

Surface area moment mean (Sauter mean diameter)

The volume surface mean diameter(VSMD) is calculated using the followingequation:

Calculate the VMD for the particle populations 1 & 2.

VSMD =n1d13 + n2d23 + n3d33 +

=nd3


groups d1, d2

n1d12 + n2d22 + n3d32 + nd2

It is defined as the diameter of a sphere that has the samevolume/surface area ratio as a particle of interest

Typical statistics (d= ESD)


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Dr Ali Nokhodchi 37

Typical statistics (d= ESD)

d=(0.5 + 1)/2 = 0.75 m

nd = 2 x 0.75 = 1.5

nd2 = 2 x (0.75)2 = 1.13

nd3

= 2 x (0.75)3

= 0.85

dnl=265.5/118 = 2.25 m D[1, 0]

dns =(640.89/118)1/2 = 2.33 m D[2, 0]

dnv = (1645.25/118)1/3 = 2.41 m D[3, 0

dvs = 1645.25/640.89 = 2.57 m D[3, 2]


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Dr Ali Nokhodchi 38


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Dr Ali Nokhodchi 39

Particle size distribution

A particle population which consists of spheres or equivalent

spheres with uniform dimensions is monosized and its

characteristics can be described by a single diameter or equivalent

diameter.

Unusual for the particles to be completely monosized in a batch:

most powders contain particles with a large number of different

equivalent diameters.

What do we do to compare the characteristics of two or more

powders consisting of particles with many different diameters?


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Dr Ali Nokhodchi 40


No. ofparticles

Particlesize (m)

No. ofparticles

Particle size(m)

50

50

Mean

5

15

10

10

10

10

mean

5

10

15

10

1

2

1 2 3 4 5 6 7 8 9 10 11

6 6 6 6 6 6 6 6 6 6 6


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Dr Ali Nokhodchi 41


We would not only want to know aboutthe characteristics of the average'particle, but have some idea of the

variation between the particles. Divide the data into size classes

Draw a histogram of the number of particles ineach size class.


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Particle size distributionsFrequency Distribution Data

ESD(m)

Number ofparticles

(frequency)

Per centparticles

(% frequency)

0-1 0 0

1-2 4 0.92-3 25 5.4

3-4 50 10.7

4-5 86 18.5

5-6 93 20.0

6-7 88 18.9

7-8 67 14.4

8-9 39 8.4

9-10 14 3.0

0

4

25

50

86

93

88

67

39

14

0-1

1-2

2-3

3-4

4-5

5-6

6-7

7-8

8-9

9-10 Frequency histogram

466

F(%) = (4/466) x100 = 0.9%

S h hi t


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Dr Ali Nokhodchi 43

Such a histogram:

- reflect the distribution of particle sizes.

- presents an interpretation of the particle size distribution.

- enables the percentage of particles having equivalent.

diameter to be determined.- allows different particle size distribution to be compared.

Normal: symmetrical about mean (+) skewed Bimodal

Commonly encountered distributions

% per mm


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Dr Ali Nokhodchi 44

Particle size [mm]

% per mm

% per mm

Particle size [mm]

Distributions may have apointed or rounded shape,

this is quantified as the kurtosis of the distribution.

A distribution which ispointedis termed leptokurtic.

A distribution which isflattenedis termedplatykurtic.

l b


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Dr Ali Nokhodchi 45

Particlesize[mm]

Numberofparticles

In band

Percentagein range

d

Percentageunder size

Percentageover size

1-2 4 0.86 99.14

2-3 25 5.36 93.78

3-4 50 10.73 16.95 83.05

4-5 86 18.45 35.40 64.6

5-6 93 19.96 55.36 44.646-7 88 18.89 74.24 25.76

7-8 67 14.38 88.62 11.38

8-9 39 8.37 96.99 3.01

9-10 14 3.00 100 0

%in range= (4/466)*100= %undersize=(0.86+5.36)=6.220.86

0.86

6.22

Total particles= 466

Less than 2 m

Larger than 2 m


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Dr Ali Nokhodchi 46

The graph shows, at any

size, what fraction of the

particles are smaller than

that size, and so is termed

apercentage undersize graph.

The two curves are

mirror images aroun

a horizontal axis.The graphs shows, at any

size, what fraction of theparticles are larger than

that size, and so termed

apercentage oversize graph.

Typical statistics (d = ESD)


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Dr Ali Nokhodchi 47

Typical statistics (d = ESD)

%n=(2/118)x100= 1.69

Cumulative undersize = 1.69+8.47=10.69=10.69+18.64=28.80


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Dr Ali Nokhodchi 48

Particle

size

(m)number of

particles

10 1

20 2

30 3

40 450 3

60 2

70 1

Plot Cumulative % undersize and oversize (number and massdistributions) against particle size. Determine the median ofeach distribution.

Question?

T th d f l tti di t ib ti


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Dr Ali Nokhodchi 49

Two methods of plotting distributions:

Incremental & cumulative

0

25

50

75

100

1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10

No. of particles

in band

Particle size [micrometers]

0

25

50

75

100

0 1 2 3 4 5 6 7 8 9 10

Cumulative % undersize

Cumulative % oversize

Micrometers

The histogram is termed

an incrementaldistribution

because it shows how many

particles fall within a given

size increment.

A cumulat ivedistribution

shows how much material

lies above or below a

particular size.

Cumulative(%)

Incremental? C m lati e?


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Dr Ali Nokhodchi 50

Incremental? Cumulative?

Which to use?

Both are widely used, since various particle sizeanalysis methods lead to one or the other.

Examples:

Sieving: sorts out the material which issufficiently small to fall through one sieve, but toolarge to fall through a finer one, and so sorts theparticles into increments. Plotting this dataleads to an incremental distribution.

Sedimentation: where all the material largerthan a certain size has sedimented at a particulartime, naturally leads to a cumulativedistribution.

Representing the size distribution:


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Dr Ali Nokhodchi 51

Representing the size distribution:

Number, Area & Mass distributions

It is important to realise that, for aparticular sample of material, the curvesdescribing the distribution of particle

number, area, and mass will not beidentical in SHAPE.

One million 1 micrometer sphericalparticles will occupy the same volume as

one 100 micrometer (the number/volumerelationship)

V= 4/3r3

30

(1/10)x100 = 10%


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Dr Ali Nokhodchi 52

0

10

20

1 2 3 4 5 6 7 8 9 10

Particle s ize [micrometer]

%

Number distribution

Area distribution

Volume distribution

It is evident from this example that we need to specify which type of

distribution we are using.

The particular type of distribution obtained depends on thesizing method used.

- Sievingandsedimentation: provide the mass of material in a

given size band.

- Coulter counter: measure the number of particles in a given

band.

2 m

(1/10)x100 = 10%

Area = [(2)2/385]x100=1%

Total surface area for 10particles

Number of Particle size Surface Mass dist.


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Number ofparticles

Particle size(mm)

Surfacearea (mm2)

Mass dist.(mm3)

1

11

1

1

11

1

1

1

1

23

4

5

67

8

9

10

1

49

16

25

3649

64

81

100

?

?27

?

?

??

512

?

?

53

Total 10 particlesF(%) = (no. particles/total)x 100F (%) = (1/10)x 100 = 10%

Total surface area is 385F(%) = (surface area of 1st Particle/total)x 100F (%) = (1/385)x 100 = 0.26%

Total 10 particlesF(%) = (no. particles/total)x 100

F (%) = (1/10)x 100 = 10%

Total surface area is 385F(%) = (surface area of 1st Particle/total)x 100

F (%) = (100/385)x 100 = 26%


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Difference between number distribution and mass

distribution

Dr Ali Nokhodchi 54

% by number %by massSize (cm) Number of

objects

10-1000 7000 0.2 99.961-10 17500 0.5 0.03

0.1-1 3500000 99.3 0.01

Total 3524500 100 100

Number mean diameter =1.6 cm

Mass mean diameter = 500 cm

So, which one is correct or important?


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Dr Ali Nokhodchi 55

Characterization of particle size


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Dr Ali Nokhodchi 56

Characterization of particle size

distributions

Histograms contain a great amount of

data, which may be summarized by

statistics to yield a measure of:

Central tendency Dispersion


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Dr Ali Nokhodchi 57

Measures of central tendency

Central tendency = the tendency of the particle sizeto cluster around a particular value. Such values areevident as a peak in the particle size distribution.These values are normally known as averages ormeans of set of data.

Three different quantities are in common use:

1. Mode

2. Median

3. Mean


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Median

The median value is the size which splits thedistribution into two halves, with 50% of the mass orparticle number larger, and 50% of the mass or particlenumber smaller. It is always given the symbol D50.

The easiest way to find the D50 is to construct a

cumulative graph, from which the 50% point can beread off directly.

0

25

50

75

100

0 5 10 15 20 25 30 35 40 45 50 55

Cumulative

(%)

Particle size (um)

0

25

50

75

100

0 20 40 60

16 um


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Dr Ali Nokhodchi 59

Median


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Mode

The value of the peak of the distribution ( Themode of the distribution is the most commonvalue occurring in distribution). If the distributionhas two or more peaks, it is said to be bimodal

or multimodal.

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60

Particle size

Percentage/micron

Mode

Unimodal distribution


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Dr Ali Nokhodchi 61

Number of

Particles

particle size

(um) Frequency

(%)

Cumulative

under size(%)

Mean

diameter

3 50

5 100

10 150

20 200

25 250

20 300

10 350

5 400

2 450

1. Arithmetic mean?

2. Determine mode and median?

3. Plot Cumulative undersize against particle size?

4. Plot Frequency against particle size?

Span = D -D /D


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Dr Ali Nokhodchi 62

Span = D90% -D10%/D50%

Span for A= (340-20)/100Span A= 3.2

Span for B= (340-30)/150Span B = 2.1

2


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Dr Ali Nokhodchi 63

Model Distributions

Particle size distributions may take many forms,but there are small number of model distributionswhich are of particular interest.

may allow us to infer something about the

material or the processes through which it haspassed

Normal distribution

lognormal distribution

0 2CumulativeIncremental


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Dr Ali Nokhodchi 64

0

184%

50%

16%

3 5 7

Mean - Mean Mean +

Normal distribution with X=5 and s = 2.

0

0.2

The standard deviation is the difference between

the 16% and 50%, or the 50% and 84% points.


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Positively skewed


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Positively skewed

distribution

Normal

a: lognormal distribution with

Mode =5 and =2.

lognormal distribution of fig. a. plotted on a

logarithmic x axis.


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The lognormal distribution

Examples:

Particles reduced by grinding follow lognormal

distribution.-Emulsion made by valve homogenizer

Particles grown by crystallisation often show alognormal distribution of size.


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Normal and lognormal distributions on linear probability

paper.

Normal and lognormal distributions on log- probability

paper.

Number Cumulative


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Dr Ali Nokhodchi 72

Number

of Particle %frequency particle size

Cumulative

(%)

3 3 50 3

5 5 100 8

10 10 150 18

20 20 200 38

25 25 250 63

20 20 300 83

10 10 350 93

5 5 400 97

2 2 450 100

Total 100 100

Question? Determine the type of distribution.

99.99

Probability graph


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Dr Ali Nokhodchi 73

Particle size diameter (um)

99.9

99.8

99

98

95

90

80

70

60

50

4030

20

10

5

2

1

0.5

0.2

0.1

0.05

0.01

Cumulative

(%)

100 200 300 400 500 600 700 800 900 1000 1100


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Dr Ali Nokhodchi 74

Particle size diameter (um)

10 20 30 40 50 60 70 80 90 200 300 400 500 600 800 1000

100 700 900

99.99

99.9

99.8

9998

95

90

80

70

60

5040

30

20

10

5

2

1

0.5

0.2

0.1

0.05

0.01

Cumulative

(%)


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Dr Ali Nokhodchi 75

Techniques

Technique ESD Sizerange

Wet /dry

Manual /automatic

Speed

Sieving ds > 45

m

Dry /

wet

Manual Slow

Microscopy dp > 1nm

Dry /wet

Manual Slow

Sedimentation dst > 0.5

m

Wet Manual Medium

Coulter

counter

dv >0.1

m

Wet Automatic Fast

Laser light

scattering

dv,

da

> 1

nm

Wet Automatic Fast

Dry sieving


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Dr Ali Nokhodchi 76

y g

The results are expressed in the form of a cumulat ive und ersize

percentage distr ibu t ion.

Fill seal agitate weighStacking the sieves in order of ascending aperture size and placing thepowder on the top sieve and agitating, the powder is classified intofractions.A closed pan, a receiver, is placed at the bottom of the stack to collectthe fines and a lid is placed on top to prevent loss of powder.Agitation may be manual or mechanical.


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Dr Ali Nokhodchi 77

What does sieving measure?The sieve equivalent diameter(d

s) is defined as the size

of a sphere that will just pass through the aperture of a

particular sieve. This is a two-dimensional value.

ds ds

ds ds

Types of dry sieves


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Dr Ali Nokhodchi 78

Types of dry sieves

Punched sieves:

Usually circular, 1 mm to 10 cm apertures

Woven sieves:

Apertures are square, > ~50 m

This type of sieve was originally specified in terms of themesh number, which is the number of wires to the inchof mesh cloth; e.g. a 120 mesh sieve has 120 wires perinch.

Etched sieves:

Circular, finer sieves ( ~5 mm).

Remember!It i i t t t if th h f th i


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It is important to specify the shape of the sieve

mesh, i.e. square-meshed or round-hole.

Example:A 100 mm sphere will just pass through the hole of 100 mm

square or round-hole sieve, but an irregular particle may

pass through one sieve and not the other.

The part ic le wou ld have dif ferent sieve diameters (ds ) in

round o r square hole sieves!

In practice dry sieving is predominant


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p y g p

Sieving times:It is recommended that sieving be continued until less than 0.2%

of material passes a given sieve aperture in any 5-minute interval.

The material separated in the sieves is measured on a weight basis

providing a mass (or volume distribution). The results are usually

presented as a percentage mass against sieve equivalent diameter.


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Sieving time

90%

10 20


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Wet sieving

When?

Fine powder (


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When to use sieving

Sieving is a basic technique and is employed wheneverany of the following conditions, or combination ofthem, hold:

Separates fractions

Excellent method for powders with agood flowability

(coarse powders) Excellent method for powders that exhibit a range of

different densities (causes difficulties for sedimentationtechniques)

Excellent method for powders that exhibit different

refractive indices (causes difficulties for light scatteringtechniques)

Excellent method for water-soluble or conductivepowders (causes difficulties for Coulter countermeasurements).

Sieving is best avoided when:


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Sieving is best avoided when:

Cannot be used when powder is too fine Cannot be used when particles are fragile

and may break during sieving

Not an appropriate method for particles in

the form ofelongated needles Cannot be used when powder adheres to

the sieve

Cannot be used when powder forms

clumps Cannot be used when powder easily

acquires an electrostatic charge

Powder must be relatively robust


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Sieving errors may arise fromFactors influencing the probability that a particle will

correctly present itself at an aperture include:

Theparticle size distribution of the powder The number of particles on the sieve (load):A good

starting point is 50-100g for 100 mm sieves and 200g for200 mm sieves.

Thephysical properties of the particles (e.g., surface) The method of shaking the sieve The dimension and shape of the particles

Obtaining a correct size distribution during a sievingoperation also depends on the following variables:

Durat ionof sieving

Variation of sieve aperture

particle size analysis-2011

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