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Lectures on Modern Physics

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Lectures on Modern Physics. Jiunn-Ren Roan. 21 Dec. 2007. Soft Matter. What Is Soft Matter? Polymers Fundamental Definitions Common Polymers Configuration and Conformation The Ideal Chain Non-ideal Chains Block Copolymers Colloids Fundamental Definitions

Lectures on Modern Physics

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Lectures on Modern Physics

Jiunn-Ren Roan

21 Dec. 2007

Soft Matter

- What Is Soft Matter?
- Polymers
- Fundamental Definitions
- Common Polymers
- Configuration and Conformation
- The Ideal Chain
- Non-ideal Chains
- Block Copolymers

- Colloids
- Fundamental Definitions
- Particle Size and Size Distribution
- Forces between Colloidal Particles

Soft Matter

References

What Is Soft Matter?

Soft matter, according to Pierre-Gilles de Gennes, the winner of the Nobel Prize

in physics in 1991, includes polymers, colloids, surfactants (amphiphiles), and

liquid crystals. These materials are “soft” because their mechanical responses

are often, though not always, intermediate between solids and liquids. The term

“soft matter” is synonymous with “soft condensed matter” or “complex fluids”.

The building blocks of most soft matter are organic molecules, so for a very

long time physicists showed little interest in soft matter. Most studies were

carried out by chemists, chemical engineers, materials scientists, or even food

scientists. De Gennes switched from “hard” matter to soft matter in mid 1960s.

It takes, however, about 30 more years before physicists’ interest in soft matter

began to surge.

Because of soft matter’s interdisciplinary nature, it will be very helpful to know

a little more about relevant chemistry, especially molecular structure and

physical chemistry.

Polymers

Fundamental Definitions

- A polymer molecule ora macromolecule, according to the IUPAC (International
- Union of Pure and Applied Chemistry) definition, is
- A molecule of high relative molecular mass, the structure of which
- essentially comprises the multiple repetition of units derived, actually
- or conceptually, from molecules of low relative molecular mass.

- Related to this definition is the IUPAC definition of the oligomer molecule:
- A molecule of intermediate relative molecular mass, the structure of
- which essentially comprises a small plurality of units derived, actually
- or conceptually, from molecules of lower relative molecular mass.

- Thus, whether a multi-unit molecule is an oligomer or a polymer depends on
- its molecular mass – it is regarded as having an intermediate relative molecular
- mass if it has properties which do vary significantly with the removal of one
- or a few of the units.
- A polymer can be synthesized from monomer molecules. Polymerization is
- the process of converting monomer molecules into a polymer. The number of
- monomeric units in a polymer is called the degree of polymerization.

Polymerization

Polymerization

Polymerization

From U. W. Gedde, Polymer Physics, Chapman & Hall, London (1995).

Monomer

Polymer

Homopolymer

+

Copolymer

Polymers

A homopolymer is a polymer derived from one species of monomer. A

copolymer is derived from more than one species of monomer.

Copolymers can be further classified according to the number of monomer

species used in copolymerization: bipolymers are copolymerized from two

monomer species, terpolymers from three monomer species, quaterpolymers

from four monomer species, etc.

A special type of copolymer is the block copolymer. Especially important are

diblock and triblock copolymers, because they have found many applications.

polyA

polyA-block-polyB

polyA-graft-polyB

poly(A-alt-B)

poly(A-stat-B)

From U. W. Gedde, Polymer Physics, Chapman & Hall, London (1995).

Polymers

A copolymer of unspecified type is named as poly(A-co-B). Others are named

as follows:

Note that an alternating copolymer poly(A-alt-B) may be considered as a

homopolymer polyAB derived from a hypothetical monomer AB.

In a statistical copolymer the sequential distribution of the monomeric units obeys

known statistical laws. A special case of statistical copolymer is the random

copolymer, named poly(A-ran-B), in which the probability of finding a given

monomeric unit at any given site in the chain is independent of the nature of the

adjacent units.

Polymers

Common Polymers

I. W. Hamley, Introduction to Soft Matter (Wiley, 2000).

Polymers

I. W. Hamley, Introduction to Soft Matter (Wiley, 2000).

Polymers

To understand how polymers are named, a few more IUPAC definitions are

needed. A constitutional unit is an atom or group of atoms comprising a part

of the essential structure of a polymer. A monomeric unit (or monomer unit)

is the largest constitutional unit contributed by a single monomer molecule to

the structure of a polymer. A constitutional repeating unit is the smallest

constitutional unit the repetition of which constitutes a polymer.

Take poly(ethylene) as an example. Its constitutional repeating unit is –CH2–,

while its constitutional unit can be any one of the following groups: –CH2–,

–CH2CH2–, –CH2CH2CH2–, etc. Since poly(ethylene) is normally synthesized

from ethylene, H2C=CH2, the monomeric unit of poly(ethylene) is –CH2CH2–.

Polymers can be named as poly(constitutional repeating unit) or poly(monomer

unit). The former is called structure-based and the latter source-based. The

structure-based names are seldom used in practice.

Finally, note that a polymer can have more than one constitutional repeating unit

and, therefore, more than one possible structural-based name. For example, the

constitutional repeating unit for poly(butadiene) can be either –CH=CHCH2CH2–

or –CH2CH=CHCH2–. Ambiguities such as this have been resolved in IUPAC’s

Compendium of Macromolecular Nomenclature.

From U. W. Gedde, Polymer Physics, Chapman & Hall, London (1995).

Polymers

Configuration and Conformation

The ‘permanent’ stereostructure of a polymer is called its configuration. The

configuration of a polymer is permanent in the sense that it is defined when

the polymer is synthesized and is preserved until the polymer reacts chemically.

A polymer’s configuration is thus defined by its molecular architecture. Major

molecular architecture types are linear, branched, ladder, star, and network:

From R. T. Morrison and R. N. Boyd, Organic

Chemistry, 4th ed., Allyn & Bacon, Boston (1983).

chiral

carbons

From R. T. Morrison and R. N. Boyd, Organic

Chemistry, 4th ed., Allyn & Bacon, Boston (1983).

Polymers

However, molecular architecture alone does not completely define a polymer’s

configuration. A polymer’s configuration is also determined by the way atoms

are arranged about double bonds (if any) and chiral centers.

It is well known that about a double bond two arrangements, cis- and trans-, are

possible:

Thus, about each double bond, there will be two distinct configurations.

A chiral center is a carbon atom to which four different groups are attached.

The four groups have two different orientations in space and they result in

isomers (called stereoisomers) that are mirror images of each other, but are

not superimposable on each other:

From G. Strobl, The Physics of Polymers, 2nd ed., Springer-Verlag, Berlin (1996).

From R. T. Morrison and R. N. Boyd, Organic Chemistry, 4th ed., Allyn & Bacon, Boston (1983).

From R. T. Morrison and R. N. Boyd, Organic Chemistry, 4th ed., Allyn & Bacon, Boston (1983).

From R. T. Morrison and R. N. Boyd, Organic Chemistry, 4th ed., Allyn & Bacon, Boston (1983).

Polymers

Thus, a poly(ethylene) molecule has only one configuration,

whereas a poly(propylene) molecule has isotatic configuration,

syndiotactic configuration,

and atactic configuration.

From R. T. Morrison and R. N. Boyd, Organic Chemistry, 4th ed., Allyn & Bacon, Boston (1983).

Polymers

While configuration defines the ‘permanent’ stereostructure of a polymer,

conformation refers to the ‘transient’ stereostructures generated by rotations

about single bonds. These stereostructures are transient in the sense that

interconversions among the rotational minima are rapidly executed because the

barrier heights of bond rotational potentials are usually only a few RT, quite

surmountable at room temperature.

P. J. Flory, Statistical Mechanics of Chain Molecules, John Wiley, New York (1969).

From M. Doi, Introduction to Polymer Physics, Oxford University Press, New York (1996).

Polymers

Because of these rapid interconversions, polymers are very flexible and can be

regarded as a long, flexible piece of string:

From M. Doi, Introduction to Polymer Physics, Oxford University Press, New York (1996).

From H. Yamakawa, Modern Theory of Polymer Solutions, Harper & Row, New York (1971).

Polymers

The Ideal Chain

The simplest model for a flexible polymer is the random walk model. Since

the model allows the polymer chain to cross itself, it defines is an unrealistic

polymer, i.e. an ideal chain.

Consider a random walk on a square lattice. Let

b be the step size (bond length), N the number of

steps, and rn the displacement vector of the nth

step (bond vector).

On a square lattice, rn can be b1, b2, b3, or b4 with

equal probability. Because the walk is random,

different steps are not correlated. Therefore,

A convenient way to define the size of a polymer

molecule is the end-to-end vector:

b5

b2

b3

b4

b1

b6

I. Teraoka, Polymer Solutions, John Wiley & Sons, New York (2002).

Polymers

Because R and –R occur with equal probability and cancel each other out, giving

the end-to-end vector itself is not a good measure of the polymer size. On the

other hand, R2 is immune to this problem, so it has become a standard measure

of the polymer size.

For the random walk considered here,

and size of the polymer is given by the end-to-end distanceRF (the subscript F

stands for Paul J. Flory, a chemist who pioneered polymer physics)

Note that the size of the polymer is proportional to N1/2 and the above derivation

also holds for a three-dimensional random walk on a cubic lattice:

Polymers

We can proceed further and calculate the probability distribution function of R

for a random walk on a cubic lattice. Let P(R, N) be the probability that an

N-step walk results in an end-to-end vector R. From the site at the (N-1)th step

to the final site at the Nth step, there are six equally possible ways:

If the polymer is very long, N 1 and RFb, then we can expand P(R-bi, N-1):

It is easy to show that

Therefore,

and this gives

which is a partial differential equation for P(R, N).

Polymers

For a very long polymer, we expect that large-scale properties such as polymer

size will not be affected by small-scale details like number of nearest neighbors.

Indeed, it can be shown that for a very long polymer the specific structure of the

lattice on which the polymer is modeled makes no difference at all. Therefore,

the same partial differential equation holds for all kinds of lattices.

The initial condition for P(R, N) is simply

i.e. the walker remains at the starting point before taking the first step. It can

be verified that the solution to the partial differential equation subject to this

initial condition is

Thus, the probability distribution of R is a Gaussian (normal) distribution.

Knowing the probability distribution

enables us to find all kinds of averages

such as the end-to-end distance:

which has the same form as before.

From M. Doi, Introduction to Polymer Physics, Oxford University Press, New York (1996).

Polymers

Since Gaussian distributions are mathematically very amenable, it is convenient

to assume that the bond vector rn itself follows a Gaussian distribution:

The ideal chain thus defined is called a Gaussian chain.

Because bond vectors are not correlated, the probability distribution for the entire

Gaussian chain is given by

Let the position vectors of the “beads” (lattice

sites) joined by the bond vectors r1, r2, ..., rN be

R0, R1, ..., RN. Because rn = Rn-Rn-1, the probability

distribution for the Gaussian chain becomes

where . From this distribution function, it can be shown

that for any n and m

Polymers

The equilibrium state of the Gaussian chain must be described by a distribution

function proportional to the Boltzmann factor

so if we write

then U can be regarded as the potential energy for a system of springs connected

in series:

Thus, the Gaussian chain model is often called the bead-spring model.

The spring constant for the entire chain is the equivalent spring constant for the

system of springs in series:

This will be used to find the size of a non-ideal chain.

Prohibited!

From M. Doi, Introduction to Polymer Physics, Oxford University Press, New York (1996).

I. Teraoka, Polymer Solutions, John Wiley & Sons, New York (2002).

Polymers

Non-ideal Chains

The ideal chain model is obviously incorrect and the fact that a polymer chain

cannot cross itself, which is a manifestation of the Pauli exclusion principle,

must be taken into account. The resulting effect is usually called the excluded

volume effect and the polymer that cannot cross itself is called an excluded

volume chain.

In models defined on a lattice such as the random walk model, the excluded

volume effect is achieved by prohibiting the same lattice site being stepped on

more than once, thus defining a self-avoiding random walk.

In models defined in a continuous space such as the Gaussian chain model, the

convention is to use an excluded volume parameter to model the repulsive

interaction between polymer segments.

R

From M. Doi, Introduction to Polymer Physics, Oxford University Press, New York (1996).

Polymers

The repulsive interaction comes into effect when two polymer segments collide,

so it is proportional to the probability of two segments being at the same point.

Consider a polymer of N segments and size R. If we assume

that the segments are uniformly distributed in the volume

occupied by the polymer, then the probability of finding a

segment within the volume is proportional to N/Rd, where

d is the dimension of space. So the repulsive energy at the

point where the two segments collide is proportional to

where is the excluded volume parameter. Therefore, the

total repulsive energy is

On the other hand, the elastic energy is assumed to be that of a Gaussian chain:

So the total energy is given by (omitting all numerical coefficients)

I. Teraoka, Polymer Solutions, John Wiley & Sons, New York (2002).

Slope n = 0.5936

Size (nm)

I. Teraoka, Polymer Solutions, John Wiley & Sons, New York (2002).

Molecular weight (g/mol)

Polymers

Minimizing the total energy gives the optimum

size, which is identified as the optimum

end-to-end distance

The relation between size and molecular weight

(or degree of polymerization) is often written as

where the exponent n is called the Flory exponent. The exponent for Gaussian

chains has been derived to be 1/2 whereas the minimum-energy argument here,

devised by Flory himself, gives for the

excluded volume chain

that is, n = 3/5 for a polymer in solution

(d = 3) and n = 3/4 for a polymer adsorbed

on a substrate (d = 2).

The exponent n = 3/5 agrees with experiments

very well. In fact, the agreement is so well

that for a long time it was thought to be exact.

From A. Yu Grosberg and A. R. Khokhlov, Statistical Physics of Macromolecules, American Institute of Physics, New York (1994).

Polymers

Block Copolymers

In general, polymers of different types are immiscible. When polymers of two

immiscible types, A and B, are connected together to form block copolymers,

the immiscibility will tend to separate A blocks and B blocks as far away as

possible. Meanwhile, however, the chemical bonds that join the A blocks to

neighboring B blocks do not allow complete separation of connected blocks.

In a block copolymer melt, the balance between immiscibility and chemical

connectedness results in A-rich and B-rich domains. The size of each domain

is mainly determined by the length of the block that dominates the domain, so

domains are usually very small, in the order of 10 nm to 500 nm. The

appearance of these small domains in block copolymers is called a microscopic

phase separation or microphase separation.

From M. Kleman and O. D. Lavrentovich, Soft Matter Physics, Springer-Verlag, New York (2003).

Polymers

OBDD = Ordered bicontinuous double diamond

Colloids

Fundamental Definitions

The colloidal range (or colloid dimension), according to the IUPAC definition,

is roughly between 1 nm and 1 mm. A system is called a colloidal system or

simply a colloid if subdivisions or discontinuities in the system occur, at least

in one direction, in the colloidal range. Thus, the solution of gold particles

studied by Faraday in 1857, porous solids, fibers, thin films, and foams all are

colloidal systems.

A colloidal dispersion is a system in which particles of colloidal size of any

nature (e.g. solid, liquid or gas) are dispersed in a dispersion medium, a

continuous phase of a different composition. If the colloidal particles have the

properties of a bulk phase of the same composition, the term dispersed phase

(or disperse phase) is used to refer to the particles.

A latex is a fluid colloidal system in which each colloidal particle contains a

number of polymers. The milky sap of many plants are latexes, in which the

colloidal particles are aggregates of biopolymers such as proteins and starches.

(Because phase separation probably will occur in bulk aggregates of the same

composition, for plant latexes the term “dispersed phase” should not be used.)

Colloids

A fluid (gas or liquid) colloidal system composed of two or more components

may be called a sol. Thus, aerosol, fog, smoke, foam, emulsion, and colloidal

suspension all are sols.

Colloids

Particle Size and Size Distribution

Characterization of particle size and the associated distribution is an important

issue in colloid science. If all the particles in a colloidal system are of (nearly)

the same size, the system is called monodisperse; otherwise it is heterodisperse.

A heterodisperse system is called paucidisperse if the particles have only a few

different sizes and polydisperse if the particles have many different sizes.

A very important fact to bear in mind is that particle size and size distribution

results should be regarded as relative measurements, so extreme caution should

be exercised when comparing results from different instruments. This is because

different instruments are based on different physical principles and even when

the instruments are based on the same physical principle, they may use different

algorithms, components, etc. that may cause great variation in the measurements.

Care also should be taken when reading particle size and size distribution data

because they can be presented in various forms and because some instruments

report size results as diameters, while some as surface area.

Numerous techniques have been devised for particle size analysis. A useful

guide to some of these techniques was recently issued by the National Institute

of Standards and Technology of the United States.

From A. Jillavenkatesa et al., Particle Size Characterization, NIST Special Publication 960-1 (2001).

Colloids

From R. J. Hunter, Foundations of Colloid Science, Vol. 1, Oxford University Press, Oxford (1986).

Colloids

Data for particle size and size distribution can be represented in tabular or graphic

forms. Three graphic forms for size distribution in common use are histogram,

differential distribution curve, and cumulative distribution curve.

Modal size

d90

Median

size, d50

From R. J. Hunter, Foundations of Colloid Science,

Vol. 1, Oxford University Press, Oxford (1986).

d10

From R. J. Hunter, Foundations of Colloid Science,

Vol. 1, Oxford University Press, Oxford (1986).

Colloids

A histogram, ni(di),can be replaced by a differential distribution curve F(d)

defined by F(di) ddi = number of particles in the range di to di+ddi = ni(di). If

the width ddi is a constant D, then

F(di) = ni(di)/D, which can be sketched

directly from the histogram.

Colloids

The differential particle size distribution curve F(d) is a sort of probability

distribution, because by definition

where N is the total number of particles and fi is the fraction of particles in the

range (di, di+ddi), i.e. the probability of finding a particle of size in this range.

In probability theory the jth moment of a probability distribution f(d) is given by

Consider the second moment. It can be written as

where Ai is the surface area of a particle of diameter di. This suggests that

is an area-averaged diameter, called the area mean diameter. Similarly, the

length mean diameter and volume mean diameter are defined as

respectively.

Colloids

The standard deviation s of the size distribution as usual is defined by

It is a measure of the spread of the distribution and is expected to vanish if all

the particles have the same size. Another way to measure the spread is the ratio

of the area mean and length mean diameters:

where PDI = polydispersity index. Note that PDI ≥ 1.

From W. B. Russel et al., Colloidal Dispersions, Cambridge University Press, Cambridge (1989).

Colloids

Forces between Colloidal Particles

Among the possible forces between colloidal particles, the most important is

electrostatic forces, followed by the van der Waals forces, and the inertial forces

are the weakest. Forces due to thermal agitation (Brownian forces) and viscosity

are equally important, whereas the ubiquitous gravity that dominates macroscopic

scales only plays a minor role on the microscopic colloidal scale.

References

1. U. W. Gedde, Polymer Physics (Chapman & Hall, 1995).

2. W. V. Metanomski ed., Compendium of Macromolecular Nomenclature(Blackwell

Science, 1991).

3. A. D. Jenkins et al., Pure Appl. Chem. 68, 2287 (1996).

4. I. W. Hamley, Introduction to Soft Matter (Wiley, 2000).

5. P.-G. de Gennes, Scaling Concepts in Polymer Physics (Cornell University Press,

1979).

6. M. Doi, Introduction to Polymer Physics (Oxford University Press, 1995).

7. D. H. Everett and L. K. Koopal, Definitions, Terminology and Symbols in Colloid

and Surface Chemistry(Division of Physical Chemistry, International Union of

Pure and Applied Chemistry, 2001).

8. R. J. Hunter, Foundations of Colloid Science (Oxford University Press, 1986) 2 Vols.

9. A. Jillavenkatesa et al., Particle Size Characterization, NIST Special Publication

960-1 (2001).