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Copolymers at Selective Interfaces: New Bounds on the Phase Diagram

机译:选择性界面处的共聚物:相图上的新界限

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We investigate the phase diagram of disordered copolymers at the interface between two selective solvents, and in particular its weak-coupling behavior, encoded in the slope m c of the critical line at the origin. We focus on the directed walk case, which has turned out to be, in spite of the apparent simplicity, extremely challenging. In mathematical terms, the partition function of such a model does not depend on all the details of the Markov chain that models the polymer, but only on the time elapsed between successive returns to zero and on whether the walk is in the upper or lower half plane between such returns. This observation leads to a natural generalization of the model, in terms of arbitrary laws of return times: the most interesting case being the one of return times with power law tails (with exponent 1+α, α=1/2 in the case of the symmetric random walk). The main results we present here are: (1) the improvement of the known result 1/(1+α)≤m c ≤1, as soon as α>1 for what concerns the upper bound, and down to α≈0.65 for the lower bound. (2) a proof of the fact that the critical curve lies strictly below the critical curve of the annealed model for every non-zero value of the coupling parameter. We also provide an argument that rigorously shows the strong dependence of the phase diagram on the details of the return probability (and not only on the tail behavior). Lower bounds are obtained by exhibiting a new localization strategy, while upper bounds are based on estimates of non-integer moments of the partition function.
机译:我们研究了两种选择性溶剂之间的界面处无序共聚物的相图,特别是其弱耦合行为,其在临界线的斜率m c 处进行了编码。我们将重点放在定向步行箱上,尽管看起来很简单,但事实证明这是极具挑战性的。用数学术语来说,这种模型的分配函数并不取决于对聚合物进行建模的马尔可夫链的所有细节,而仅取决于连续返回零之间的经过时间以及步幅是在上半部还是下半部。此类收益之间的距离。根据返回时间的任意定律,这一观察结果导致了模型的自然概括:最有趣的情况是幂次方尾部的返回时间之一(指数为1 +α,α为1/2的情况)对称随机游走)。我们在这里呈现的主要结果是:(1)只要涉及上限的α> 1,已知结果1 /(1 +α)≤m c ≤1的改进,下限则降至α≈0.65。 (2)证明对于耦合参数的每个非零值,临界曲线都严格位于退火模型的临界曲线之下。我们还提供了一个论点,它严格显示了相图对返回概率的详细信息(而不仅对尾部行为的依赖)的强烈依赖性。下限是通过展示新的定位策略获得的,而上限则基于分区函数的非整数矩的估计。

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