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Polymers in Confined Geometry.pdf

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Contents<br />

1 Introduction 1<br />

2 Polymer models 5<br />

2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br />

2.2 Ideal phantom cha<strong>in</strong> . . . . . . . . . . . . . . . . . . . . . . . . . 6<br />

2.3 Semi-flexible polymers . . . . . . . . . . . . . . . . . . . . . . . . 9<br />

2.3.1 Kratky-Porod model . . . . . . . . . . . . . . . . . . . . . 9<br />

2.3.2 Worm-like cha<strong>in</strong> model . . . . . . . . . . . . . . . . . . . . 10<br />

2.4 Stiff polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br />

2.4.1 Weakly-bend<strong>in</strong>g rod approximation . . . . . . . . . . . . . 14<br />

2.5 Real cha<strong>in</strong>s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15<br />

3 <strong>Polymers</strong> <strong>in</strong> conf<strong>in</strong>ed geometry 21<br />

3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21<br />

3.2 Scal<strong>in</strong>g relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22<br />

3.2.1 The flexible regime . . . . . . . . . . . . . . . . . . . . . . 23<br />

3.2.2 The stiff regime . . . . . . . . . . . . . . . . . . . . . . . . 25<br />

3.3 Analytical results . . . . . . . . . . . . . . . . . . . . . . . . . . . 27<br />

3.3.1 Averages by Fourier transformation . . . . . . . . . . . . . 28<br />

3.3.2 Averages by Fourier series . . . . . . . . . . . . . . . . . . 34<br />

3.3.3 Radial distribution function . . . . . . . . . . . . . . . . . 36<br />

4 Simulation methods 37<br />

4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37<br />

4.2 The Monte-Carlo method . . . . . . . . . . . . . . . . . . . . . . 38<br />

4.3 Sampl<strong>in</strong>g the canonical ensemble . . . . . . . . . . . . . . . . . . 39<br />

4.3.1 Markov cha<strong>in</strong>s . . . . . . . . . . . . . . . . . . . . . . . . . 40<br />

4.3.2 Metropolis algorithm . . . . . . . . . . . . . . . . . . . . . 41<br />

4.3.3 Correlations and error calculation . . . . . . . . . . . . . . 42<br />

4.3.4 Recursive averag<strong>in</strong>g and error calculation . . . . . . . . . . 46<br />

4.3.5 Random number generators . . . . . . . . . . . . . . . . . 47<br />

4.4 Simulation of unconf<strong>in</strong>ed worm-like cha<strong>in</strong>s . . . . . . . . . . . . . 47<br />

4.4.1 Initial part of the simulation . . . . . . . . . . . . . . . . . 50<br />

iii

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