Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main, Germany.
Institute for Biophysics, Goethe University Frankfurt, Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany.
ACS Appl Bio Mater. 2024 Feb 19;7(2):528-534. doi: 10.1021/acsabm.2c00585. Epub 2022 Sep 7.
Nanofiltration technology faces the competing challenges of achieving high fluid flux through uniformly narrow pores of a mechanically and chemically stable filter. Supported dense-packed 2D-crystals of single-walled carbon nanotube (CNT) porins with ∼1 nm wide pores could, in principle, meet these challenges. However, such CNT membranes cannot currently be synthesized at high pore density. Here, we use computer simulations to explore lipid-mediated self-assembly as a route toward densely packed CNT membranes, motivated by the analogy to membrane-protein 2D crystallization. In large-scale coarse-grained molecular dynamics (MD) simulations, we find that CNTs in lipid membranes readily self-assemble into large clusters. Lipids trapped between the CNTs lubricate CNT repacking upon collisions of diffusing clusters, thereby facilitating the formation of large ordered structures. Cluster diffusion follows the Saffman-Delbrück law and its generalization by Hughes, Pailthorpe, and White. On longer time scales, we expect the formation of close-packed CNT structures by depletion of the intervening shared annular lipid shell, depending on the relative strength of CNT-CNT and CNT-lipid interactions. Our simulations identify CNT length, diameter, and end functionalization as major factors for the self-assembly of CNT membranes.
纳滤技术面临着通过机械和化学稳定的过滤器中的均匀窄孔实现高流体通量的竞争挑战。具有约 1nm 宽孔的单壁碳纳米管(CNT)porin 的支撑致密堆积 2D 晶体原则上可以满足这些挑战。然而,目前无法以高孔密度合成这种 CNT 膜。在这里,我们使用计算机模拟来探索脂质介导的自组装作为一种致密堆积 CNT 膜的途径,这是受膜蛋白 2D 结晶的类比启发的。在大规模粗粒度分子动力学(MD)模拟中,我们发现 CNT 在脂质膜中很容易自组装成大簇。夹在 CNT 之间的脂质在扩散簇碰撞时润滑 CNT 的重新堆积,从而促进了大有序结构的形成。簇扩散遵循 Saffman-Delbrück 定律及其由 Hughes、Pailthorpe 和 White 推广的定律。在更长的时间尺度上,我们预计通过耗尽中间共享的环形脂质壳,形成紧密堆积的 CNT 结构,这取决于 CNT-CNT 和 CNT-脂质相互作用的相对强度。我们的模拟确定了 CNT 长度、直径和末端官能化是 CNT 膜自组装的主要因素。