Applied Chemical and Materials Division, National Institute of Standards and Technology , Boulder, Colorado 80305, United States.
Materials and Manufacturing Directorate, Air Force Research Laboratory , Wright-Patterson AFB, Ohio 45433, United States.
J Am Chem Soc. 2016 Jan 20;138(2):540-8. doi: 10.1021/jacs.5b09529. Epub 2015 Dec 17.
Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.
肽介导的纳米粒子(NP)合成途径可以在环保条件下创造和/或组装功能性纳米材料,其性质由生物/非生物界面的复杂相互作用决定。通过序列修饰对该界面进行操控,可以提供定制材料性能的能力,从而创造用于能源、催化和传感应用的增强型材料。要充分发挥这些材料的潜力,需要全面了解目前缺乏的序列依赖性结构/功能关系。在这项工作中,使用高能 X 射线衍射数据的原子对分布函数分析和先进的分子动力学(MD)模拟相结合,确定了一系列肽封端 Au NPs 的原子尺度结构。使用不同肽序列合成的 Au NPs 对 4-硝基苯酚还原的示例反应表现出不同程度的催化活性。从实验中得出的原子尺度 NP 构型揭示了 NP 表面结构有序性的序列依赖性差异。然后,使用带有溶质调温 MD 模拟的 replica exchange 来预测这些 Au NPs 上肽覆盖层的形态,并确定决定结构/催化性能关系的因素。我们表明,暴露的 Au 表面量、底层表面结构无序性以及肽与 Au 表面的相互作用强度都影响催化性能。开发了一种简化的催化性能计算预测方法,该方法可能作为未来研究的筛选工具。我们的方法为催化肽介导的金属 NP 系统的分析提供了一个平台,有可能为增强性能的合理设计规则的发展提供依据。