Simulation of reversible, electrostatically linked Modelnetworks

两亲性锥体网络（ACN）是由亲水和疏水聚合物组成的网络，形成具有有趣可调性质的复杂结构。在我们的建议中，由可逆离子连接的构建块（例如星形聚合物）组成的acn特别有趣，因为对于这些系统，网络特性可以通过外部参数（如盐浓度和pH值）改变（在构建块上的弱电荷的情况下）。到目前为止，这些acn几乎没有被探索过，但由于它们有趣的可调谐特性，这些网络有许多潜在的应用，例如，作为细胞基质。在这个项目中，分子动力学和蒙特卡罗模拟粗粒度的珠子弹簧聚合物模型将用于深入了解微观结构以及可逆（由离子四臂星形聚合物构建）和共价可逆（即已经部分预交联）ACN的动力学。首先，完全可逆四臂恒星体系的网络形成和平衡状态将被表征为不同参数（如pH、盐浓度、区块长度和不同恒星结构）的函数。随后，模型蛋白在储存库和网络之间的分配将被研究。这些蛋白质将被表示为“弱带电的片状粒子”，我们将扩展我们的G-RxMC方法以能够模拟这些。下一步，我们将使用先前研究中的蛋白质模型来研究网络中探针粒子的传输等动态特性。为了解释流体动力学的相互作用，我们将探针粒子以及聚合物和离子耦合到晶格玻尔兹曼流体。在第三个工作包中，将对已经预交联的共价可逆混合网络重复先前的研究。最后，将研究固定在表面上的薄混合网络的膨胀行为和力学性能，并将其与体系统性能进行比较。

Amphiphilic cone networks (ACN) are networks consisting of hydrophilic and hydrophobic polymer building blocks, forming complex structures with interesting tunable properties. In our proposal, ACNs consisting of reversible-ionically linked building blocks (e.g. star polymers) are of particular interest, since for these systems network properties can be changed by external parameters such as salt concentration and pH (in the case of weak charges on the building blocks). Those ACNs have hardly been explored so far, but because of their interesting tunable properties, these networks have numerous potential applications, for example, as cell substrates. In this project, molecular dynamics and Monte Carlo simulations of coarse-grained bead-spring polymer models will be used to gain insights into the microscopic structure as well as the dynamics of reversible (built from ionic four-arm star polymers) and covalent-reversible (i.e. already partially pre-crosslinked) ACN. First, the network formation as well as the equilibrium state of the fully reversible four-arm star systems will be characterized as a function of different parameters such as pH, salt concentration, block length, and for different star architectures. Subsequently, the partitioning of model proteins between a reservoir and the networks will be investigated. These proteins will be represented as "weakly charged patchy particles" and we will extend our G-RxMC method to be able to simulate those. In the next step, we will investigate dynamic properties such as the transport of probe particles in the networks, using the protein models from the previous studies. To account for hydrodynamic interactions, we will couple the probe particles as well as polymers and ions to a lattice Boltzmann fluid. In the third work package, the previous investigations will be repeated for already pre-crosslinked covalent-reversible hybrid networks. Finally, thin hybrid networks fixed on a surface will be investigated with respect to their swelling behavior and mechanical properties and compared with bulk system properties.