Foundations of Chemical Kinetics Lecture 7: Statistical treatment - - PowerPoint PPT Presentation
Foundations of Chemical Kinetics Lecture 7: Statistical treatment - - PowerPoint PPT Presentation
Foundations of Chemical Kinetics Lecture 7: Statistical treatment of equilibrium Marc R. Roussel Department of Chemistry and Biochemistry Equilibrium: A statistical picture For a reaction A B, 0 A B both together
Equilibrium: A statistical picture
For a reaction A ⇋ B,
both together ε B A ∆ε
Equilibrium: A statistical picture (continued)
◮ There is a Boltzmann distribution for both sets of molecular
states together.
◮ By summing the probabilities of the states belonging to one of
the two chemical identities and multiplying by the total number of molecules (N), we get the average number of molecules which are of the corresponding type. P(A) = 1 Q
- a
exp
- − ǫa
kBT
- ∴ NA = N
Q
- a
exp
- − ǫa
kBT
- and
NB = N Q
- b
exp
- − ǫb
kBT
Equilibrium: A statistical picture (continued)
◮ It is tempting to conclude that the sums
a exp(−ǫa/kBT)
and
b exp(−ǫb/kBT) are the partition functions of A and B.
◮ Normally, the partition function of a molecule is computed by
setting the zero of energy at the ground state. (Think about our computation of the partition function of the harmonic oscillator.)
◮ To deal with both molecular states together, we need to add
∆ǫ0, the difference between the ground-state energies of A and B, to the energies of B. This has the effect of multiplying the partition function of B by exp(−∆ǫ0/kBT).
◮ Thus,
a exp(−ǫa/kBT) = QA and
- b exp(−ǫb/kBT) = QB exp(−∆ǫ0/kBT).
Equilibrium: A statistical picture (continued)
◮ The expected numbers of molecules of A and B are therefore
NA = NQA/Q NB = (NQB/Q) exp(−∆ǫ0/kBT)
◮ The equilibrium constant is K = NB/NA, or
K = QB QA exp
- − ∆ǫ0
kBT
General case: aA + bB ⇋ cC + dD
K = Qc
CQd D
Qa
AQb B
N−∆n exp
- −∆E0
kBT
- where
◮ ∆ǫ0 = cǫ(C)
+ dǫ(D) −
- aǫ(A)
+ bǫ(B)
- ◮ ∆n = c + d − (a + b) (difference of stoichiometric coefficients)
◮ N is the number of molecules (dimensionless). ◮ The translational partition function depends on V .