Zhao Yinong, Wei Xingfei, Hernandez Rigoberto
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.
J Phys Chem C Nanomater Interfaces. 2024 Nov 27;128(49):21164-21172. doi: 10.1021/acs.jpcc.4c06055. eCollection 2024 Dec 12.
Nanoparticle networks have potential applications in brain-like computing yet their ability to adopt different states remains unexplored. In this work, we reveal the dynamics of the attachment of polyelectrolytes onto gold nanoparticles (AuNPs), using a bottom-up two-bead-monomer dissipative particle dynamics (TBM-DPD) model to show the heterogeneity of polymer coverage. We found that the use of one polyelectrolyte homopolymer limits the complexity of the possible engineered nanoparticle networks (ENPNs) that can be built. In addressing this challenge, we first found the commensurability rules between the numbers of AuNPs and poly(allylamine hydrochloride)s (PAHs). This gives rise to a well-defined valency of a AuNP which is the maximum number of PAHs that it can accommodate. We further use an engineered block copolymer, which has a conductive middle block to mediate the distance between a dimer of AuNP. We argue that by controlling the length of conductive block that is connecting the AuNPs and their respective topology, we can have ENPNs potentially adopt multiple states necessary for primitive neuromorphic computing.
纳米粒子网络在类脑计算中具有潜在应用，但它们采用不同状态的能力尚未得到探索。在这项工作中，我们揭示了聚电解质附着在金纳米粒子（AuNP）上的动力学，使用自下而上的双珠单体耗散粒子动力学（TBM-DPD）模型来展示聚合物覆盖的不均匀性。我们发现，使用一种聚电解质均聚物会限制可以构建的可能的工程化纳米粒子网络（ENPN）的复杂性。为应对这一挑战，我们首先发现了AuNP数量与聚（烯丙胺盐酸盐）（PAH）数量之间的可公度性规则。这产生了一个明确的AuNP价态，即它可以容纳的PAH的最大数量。我们进一步使用一种工程化嵌段共聚物，其具有一个导电中间嵌段来介导AuNP二聚体之间的距离。我们认为，通过控制连接AuNP的导电嵌段的长度及其各自的拓扑结构，我们可以使ENPN潜在地采用原始神经形态计算所需的多种状态。
