The name is absent



Substituting these inhomogeneous chemical potentials or the functional derivatives

of the free energy in Euler-Lagrange eqn. 7.2 for the segment a gives

lnpɑ(r) + J2 lnʌ^(r) = -L>α(r) + βμα,
λγ(q)

(7.11)


where Do,(r) is given by

floW = ⅛Σ f ⅜(fι)sln1            ("-«"С)

2      , J               δpa(r)              δpa(r) δpa{r)

7                                                                     (7∙12)

The set of these non-linear eqs. 7.11 (for all the segments) can be solved with
eqn. 7.10 for
Хд, for the density profile of the segments. However, in eqn. 7.10, Хд
for a segment a depends on +1. This coupling of Хд and X^+1 leads to numerical
complexities. This interdependence is decoupled for the branched chain molecule by
simultaneously solving eqn. 7.11 for the segment densities and eqn. 7.10 for the
Хд’з.
The procedure is similar to that followed for the linear chains in chapter 4. For the
first segment

(7.13)


Pι(rι)X⅛(rι) = exp[D1(rι)] exp(∕3μι).

Substituting this result in eqn. 7.10 for Xg (neglecting the 1 in the denominator in
comparison to the second term which contains the bonding energy and ɛɑ → ∞) gives

•2 (r ∖ _ _______________________1_______________________

(7.14)


b exp(βμ1) ʃ dr1 exp[A(rι)]Δ(1>2)(rι, r2) '

188



More intriguing information

1. The Economics of Uncovered Interest Parity Condition for Emerging Markets: A Survey
2. Social Irresponsibility in Management
3. Optimal Tax Policy when Firms are Internationally Mobile
4. What should educational research do, and how should it do it? A response to “Will a clinical approach make educational research more relevant to practice” by Jacquelien Bulterman-Bos
5. Chebyshev polynomial approximation to approximate partial differential equations
6. Activation of s28-dependent transcription in Escherichia coli by the cyclic AMP receptor protein requires an unusual promoter organization
7. The name is absent
8. Education Responses to Climate Change and Quality: Two Parts of the Same Agenda?
9. Conflict and Uncertainty: A Dynamic Approach
10. Mean Variance Optimization of Non-Linear Systems and Worst-case Analysis