1 / 34

Random copolymer adsorption

Random copolymer adsorption. Juan Alvarez Department of Mathematics and Statistics, University of Saskatchewan. E. Orlandini, Dipartimento di Fisica and Sezione CNR-INFM, Universit`a di Padova C.E. Soteros, Department of Mathematics and Statistics, University of Saskatchewan

riva
Télécharger la présentation

Random copolymer adsorption

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Random copolymer adsorption Juan Alvarez Department of Mathematics and Statistics, University of Saskatchewan E. Orlandini, Dipartimento di Fisica and Sezione CNR-INFM, Universit`a di Padova C.E. Soteros, Department of Mathematics and Statistics, University of Saskatchewan S.G. Whittington, Department of Chemistry, University of Toronto

  2. Dilute solution (polymer-polymer interactions can be ignored). • System in equilibrium. • Polymer's conformations: self-avoiding walks, Motzkin paths, Dyck paths.

  3. Degree of polymerization: n • Two types of monomers: A and B. • Monomer sequence (colouring) is random. • ci is colour of monomer i (ci=1→ A) • i.i.d. Bernoulli random variables, P(ci=1) = p • Energy of conformation w for fixed colour c : • E(w|c) = -a nA,S. • a = - 1/kT • nA,S : number of A monomers at the surface. • Conformations with same energy are equally likely, so • cn(nA,S|c): number of walks withnA,S vertices coloured A at the surface.

  4. Intensive free energy at fixed c : • Quenched average free energy : • Limiting quenched average free energy (exists): • Indicates if polymer prefers desorbed or adsorbed phase. • Asa → ∞ , asymptotic to a line with slope • Q: What is the value ofaq ? • Q: What is fora > aq?

  5. Annealed average free energy: • Limiting annealed average free energy: • So, • Asa → ∞ , is asymptotic to a line with slope

  6. The Morita Approximation (Constrained Annealing) • Consider the constrained annealed average free energy with the Lagrangian • Minimization of with respect to lC constrains • Mazo (1963), Morita (1964), and Kuhn (1996) showed that can be obtained as the solution to

  7. Setting some l’s to zero and minimizing to obtain yields an upper bound on . • In particular, we obtain and so • e.g., • annealed • 1st order Morita:

  8. Minimization to obtain is quite complex. • Upper bound it by • Consider the grand canonical partition function • with radius of convergence . • We obtain

  9. Gs in terms of a homopolymer generating function Bs. • Bs keeps track of the number of segments w of the path that have the same sequence of surface touches. • Obtained via factorization, • e.g., dn: number of n-step Dyck paths.

  10. Gs in terms of a homopolymer generating function Bs. • Bs keeps track of the number of segments w of the path that have the same sequence of surface touches. • The radii of convergence are related by • where • z1: branch cut from the desorbed phase (square root) • the other zi's are the nr poles from the adsorbed phase.

  11. Direct Renewal approach • Consider only colouring constraints on sequences of non-overlapping vertices.

  12. Direct Renewal approach • Consider only colouring constraints on sequences of non-overlapping vertices. • As an example, consider the case s = 2 for Motzkin paths. Then • Di = 1 if vertex i is at surface. • Term in square brackets depends only on sequence

  13. Then where • is the number of Motzkin paths of lengthnwithnj segments with the sequence as the sequence of bits in j base 2 . with the sequence s1s0given by the bits in ibase2 .

  14. Transfer Matrix approach • Consider the following colouring constraints:

  15. Transfer Matrix approach • Consider the following colouring constraints: • As an example, consider s = 2 for Motzkin paths. Then

  16. Need to find a sequence of 2×2 real matrices such that • Using the properties of the trace of a real matrix where denotes the eigenvalue with largest modulus.

  17. only depends on w through seq. • Index the 8 possible matrices by the binary string in base 10. • Then, • and • with • The matrix is symmetric if .

  18. Lower bounds • We can obtain a lower bound using the fact that so that • Another lower bound can be obtained from so that

  19. Monte Carlo • Quenched average free energy: • Limiting quenched average free energy: • Fir fixed n, average over a random set of colors

  20. Q: What is for a > aq ?

  21. Heat capacity

  22. Scaling

  23. Thanks.

  24. Direct Renewal approach • Consider only colouring constraints on sequences of non-overlapping vertices. • As an example, consider the case s = 2 for Motzkin paths. Then • Di = 1 if vertex i is at surface. • Term in square brackets depends only on sequence

  25. Transfer Matrix approach • Consider the following colouring constraints: • As an example, consider s = 2 for Motzkin paths. Then

  26. Need to find a sequence of 2×2 real matrices such that • Using the properties of the trace of a real matrix where denotes the eigenvalue with largest modulus. • Let where

More Related