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Chapter 13. Diffusion
13.1 Introduction
Diffusion is the physical process of matter spreading from a region of higher concentration
to a region of lower concentration. Examples include the spreading of cream in a cup of coffee and
the deflation of a helium balloon over time. In more general terms, diffusion may refer not only to
the movement of matter but also of heat. For example, if one end of a block is heated rapidly and
then the heat source is removed, over time the heat will diffuse through the block until the
temperature of the block becomes uniform.
13.2 The Theoretical Basis of Diffusion
Atoms and molecules are constantly in motion. Each atom or molecule undergoes many
collisions with others in the gas or liquid in a very short period of time. Because of this, the atom
or molecule appears to move in a somewhat random fashion, as illustrated in Figure 13.1. This
motion is often referred to as a random walk.
Figure 13.1. The motion of an atom or molecule in solution appears to be a random walk.
The average distance that an atom or molecule travels between collisions is called its mean
free path. The mean free path depends upon the size of the atom or molecule. For a typical small
molecule such as O2 in the gas phase, the mean free path at 300 K is about 80 nm. Combining this
with an average speed of 400 m/s, on average the time between collisions for a small molecule is
about 2×10– 1 0 s, or 200 ps. Looking at this in another way, a typical small molecule undergoes
about 5×109 collisions per second in the gas phase!
As a molecule undergoes this seemingly random motion, induced by collisions with other
molecules, it migrates through the solution. If there was initially a larger proportion of molecules of
a particular type in one region of the solution (e.g., a drop of ink in a container of water), then over
time the molecules will spread out and become evenly distributed throughout the solution: this is the
process of diffusion. The diffusion of molecules throughout a solution is in accord with the Second
Law of Thermodynamics that states that the entropy change for a closed system must be positive
(tending toward more randomness).
For an initial concentration gradient in a gas or liquid solution, the rate at which the
molecules spread out is proportional to the difference in the concentration gradient (or curvature),
Diffusion Rate = D × Concentration Curvature.
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(13.1)
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Equation (13.1) is known as Fick’s Second Law of Diffusion. The proportionality constant D is
called the Diffusion Coefficient. More specifically, Fick’s Second Law has the form
∂c
∂ 2c
= D
,
∂t
∂ x2
(13.2)
where c is the concentration, t is time, and x is the direction of the concentration gradient.
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13.3 The Diffusion Coefficient
The diffusion coefficient D has units of m2 s– 1 and provides a measure of the number of
molecules moving through a particular cross sectional area per unit of time. Experimental
measurements of typical diffusion coefficients in water at 300 K yield values in the range of 10– 9
m2 s– 1.
13.3.1 Relation of Diffusion Coefficient to Mean Free Path
The diffusion coefficient is related to the mean free path that a molecule travels via the
Einstein-Smoluchowski equation,
λ2
D =
,
2τ
(13.3)
In Equation (13.3), λ is the mean free path and τ is the average time between collisions.
Assuming that the average time between collisions can be calculated from the average speed and the
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mean free path, the time between collisions
can be expressed as
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τ =
λ
v ave
,
(13.4)
where v ave is the average speed. Substituting this relation into the Einstein-Smoluchowski equation
gives
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D =
1
λv ave .
2
(13.5)
Using the gas phase values of 80 nm for the mean free path and 400 m/s as the average speed, the
predicted diffusion coefficient for a small molecule in the gas phase is D ~ 2×10– 5 m2 s– 1, much
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larger than the typical liquid phase diffusion
coefficient of 10– 9 m2 s– 1. This is because, with fewer
collisions in the gas phase, a molecule can move further away from its starting point in a shorter
period of time. The higher frequency of collisions in the liquid phase leads to slower diffusion.
13.3.2 Relation of Diffusion Coefficient to Viscosity
In the liquid phase, diffusion can be related to a measurable property known as viscosity.
Viscosity is a measure of the resistance of a fluid to flow. A liquid such as water that flows easily
has a relatively low viscosity, while a liquid such as syrup that is very thick and flows more slowly
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has a high viscosity. More specifically, the viscosity η of a liquid is a quantitative measure of the
liquid’s ability to transport momentum. The units of viscosity are Poise (P), where 1 P = 0.1
kg m– 1s– 1. Typical viscosities are in the range of 10– 4 P for gases and 10– 2 P for liquids.
The relation between viscosity and the diffusion coefficient is given by the Einstein-Stokes
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equation,
kT
D =
,
(13.6)
6π r η
where k is the Boltzmann constant, T is temperature, and r is the radius of the molecule. Note that
Equation (13.6) was derived assuming that the molecule was roughly spherical in shape.
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Qualitatively, the Einstein-Stokes equation
shows that the viscosity and diffusion coefficient are
inversely related. This makes sense when considering a molecule with a high viscosity flows
slowly and thus would be expected to diffuse slowly.
13.4 Random Walks: The Statistical Model of Diffusion
Random numbers can be used in simulations of deterministic processes involving many
(102 3) particles. Processes such as diffusion, in which a single particle moves through a solution
containing a huge number of other particles, can be modeled. The other particles in the solution
affect the dynamics of the particle of interest through interactions and collisions. Though in
principle it would be theoretically possible to write down the equations of motion for all the
particles in the system, in practice this is not feasible. Therefore, the effects of the other particles
in the solution upon the particle of interest can be modeled by assuming random motion.
The simplest example of this type of modeling is called a one-dimensional random walk.
In this simulation, a random number between 0 and 1 is selected for each step. If the random
number selected is less than 1/2, the walker takes a step of –1. If the random number selected is
greater than or equal to 1/2, the walker takes a step of +1. The distance of the walker from the
origin is monitored.
13.4.1 Random Walks in One Dimension
Two simulations illustrating the one-dimensional random walk are shown in Figure 13.2.
Each simulation was carried out for 1000 steps.
50
40
30
20
X
10
0
-10
0
200
400
600
800
1000
-20
-30
-40
Step number
Figure 13.2. Random walk in one dimension (two different trials).
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The diffusional motion of a random walk simulation can be observed by calculating the
average squared distance from the origin, <x2 >. To do this, many individual random walk
simulations are carried out, and the average is taken over many individual random walkers. Results
for <x2 > versus time for a random walk in one dimension are shown in Figure 13.3.
Figure 13.3. Results for <x2> versus time for random walk in one dimension (from
Computational Physics, N. J. Giordano, Prentice Hall, Upper Saddle River, NJ,
1997).
The results presented in Figure 13.3 illustrate that diffusional behavior is observed. That is, the
system follows the equation <x2 > ∝ Dt, where D is the diffusion coefficient and t is time
(proportional to the number of steps).
13.4.2 Random Walks in Two or Three Dimensions
Random walk simulations similar to those illustrated in the previous section can be carried
out in two or three dimensions. For example, for a simulation in two dimensions, at each step two
random numbers are selected (one for the x direction and one for the y direction). The random
numbers are in the range [–1, 1] and indicate the x and y distances that the particle moves.
The first 100 steps of a sample simulation in two dimensions are shown in Figure 13.4.
3
2
1
Y
0
-2
-1
0
2
4
6
-2
-3
-4
X
Figure 13.4. Random walk in 2-dimensions (first 100 steps). The x and y distances stepped are
random numbers in the interval [–1, 1].
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If the same two-dimensional simulation is continued for many more steps (10,000), the result
shown in Figure 13.5 is obtained.
40
30
20
10
Y
0
-60
-40
-20
-10
0
20
40
60
-20
-30
-40
X
Figure 13.5. Random walk in 2-dimensions (10,000 steps). The x and y distances stepped are
random numbers in the interval [–1, 1].
Finally, if the same type of simulation is carried out in three dimensions, three random
numbers are required: one for the x, one for the y, and one for the z directions. When many
separate three-dimensional random walk simulations are carried out, the average distance squared
from the origin, <r2 >, can be computed at each step. When this is plotted as a function of time,
as shown in Figure 13.6, diffusional behavior in three dimensions is observed.
Figure 13.6. Results for <r2> versus time for random walk in three dimensions (from
Computational Physics, N. J. Giordano, Prentice Hall, Upper Saddle River, NJ,
1997).
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13.5 Another Example: Diffusion of Cream in Coffee Model
To carry out example simulations of diffusion of many particles, for example, a drop of
cream in a cup of coffee, similar random walk techniques may be employed. As an illustration,
consider 400 “cream” molecules initially constrained to a 20×20 grid initially centered at the origin
at as shown in Figure 13.7a. Each cream molecule is allowed to undergo a random walk. Figures
13.7b-13.7d show the behavior of the cream as time increases.
(a)
(c)
(b)
(d)
Figure 13.7. Behavior of 400 model cream molecules diffusing from the center of a solution in
two dimensions (from Computational Physics, N. J. Giordano, Prentice Hall,
Upper Saddle River, NJ, 1997).
As time increases, Figure 13.7 clearly shows the diffusional spread of cream throughout the coffee.
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Chapter 13 Review Problems
1. Which molecule would be expected to have the higher diffusion coefficient in the gas phase,
CH4 or H2 ? Explain.
2. In the liquid phase, which molecule would be expected to have the higher diffusion coefficient,
methanol or octanol? Explain.
3. The gas phase diffusion coefficient of methane is 1.78×10– 5 m2 s– 1. Assuming a mean free path
of 80 nm, calculate the average speed of methane molecules in m/s.
4. The viscosity of carbon tetrachloride, CCl4 , is 0.908×10–2 Poise at 25°C. The viscosity of
cyclohexanol is 57.5×10– 2 Poise at 25°C. Estimate the radius of the molecules assuming that
they are approximately spherical. Use the Einstein-Stokes equation to determine the diffusion
coefficient of each compound.
5. Suppose a large number of random walk simulations have been carried out in one dimension.
(a) What do you expect to be the average value of x at each step? How is this different from
x2 ?
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(b) The spread in a distribution may be determined statistically as the variance, or σ2 . The
variance σ2 is defined as
2
σ 2 = x2 − x .
How is the spread or variance in the particle’s position at each step related to the diffusion
coefficient?
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6. For an ensemble of random walk simulations in three dimensions, the average of the distance
squared from the starting position is linearly related to the time at each step,
r2
= Dt .
If a particle was allowed to freely translate in space with no collisions, determine how the
average distance squared varies with time. Assume classical behavior.
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