Mapping Nanostructure: A Systematic Enumeration of Nanomaterials by Assembling Nanobuilding Blocks at Crystallographic Positions

Dean C. Sayle,^†^,^* Sudipta Seal,^‡^,^§ Zhongwu Wang,^⊥ Benoît C. Mangili,^† David W. Price,^∥ Ajay S. Karakoti,^‡^,^# Satyanarayana V. T. N. Kuchibhatla,^‡^,^# Quan Hao,^⊥ Günter Möbus,^¶ Xiaojing Xu,^¶ and Thi X. T. Sayle^†

^† Department of Applied Science, Security and Resilience Defence College of Management and Technology, Cranfield University, Defence Academy of the United Kingdom, Shrivenham SN6 8LA, U.K., ^‡ Surface Engineering and Nanotechnology Facility, Advanced Materials Processing and Analysis Center, Department of Mechanical Materials and Aerospace Engineering, University of Central Florida, Orlando, Florida 32862, ^§ Nanoscience and Technology Center, University of Central Florida, Orlando, Florida 32862, ^⊥ Cornell High Energy Synchrotron Source, Wilson Laboratory, Cornell University, New York 14853, ^∥ Material Science Research Division, Research and Applied Science, AWE, Aldermaston RG7 4PR, U.K., ^# Pacific Northwest National Laboratory, 3335 Q Avenue, Richland, Washington 99352, and ^¶ Department of Engineering Materials, University of Sheffield, Mappin Street, Sheffield S1 3JD, U.K.

^*Address correspondence to d.c.sayle@cranfield.ac.uk.

ABSTRACT

Nanomaterials synthesized from nanobuilding blocks promise size-dependent properties, associated with individual nanoparticles, together with collective properties of ordered arrays. However, one cannot position nanoparticles at specific locations; rather innovative ways of coaxing these particles to self-assemble must be devised. Conversely, model nanoparticles can be placed in any desired position, which enables a systematic enumeration of nanostructure from model nanobuilding blocks. This is desirable because a list of chemically feasible hypothetical structures will help guide the design of strategies leading to their synthesis. Moreover, the models can help characterize nanostructure, calculate (predict) properties, or simulate processes. Here, we start to formulate and use a simulation strategy to generate atomistic models of nanomaterials, which can, potentially, be synthesized from nanobuilding block precursors. Clearly, this represents a formidable task because the number of ways nanoparticles can be arranged into a superlattice is infinite. Nevertheless, numerical tools are available to help build nanoparticle arrays in a systematic way. Here, we exploit the “rules of crystallography” and position nanoparticles, rather than atoms, at crystallographic sites. Specifically, we explore nanoparticle arrays with cubic, tetragonal, and hexagonal symmetries together with primitive, face centered cubic and body centered cubic nanoparticle “packing”. We also explore binary nanoparticle superlattices. The resulting nanomaterials, spanning CeO₂, Ti-doped CeO₂, ZnO, ZnS, MgO, CaO, SrO, and BaO, comprise framework architectures, with cavities interconnected by channels traversing (zero), one, two and three dimensions. The final, fully atomistic models comprise three hierarchical levels of structural complexity: crystal structure, microstructure (i.e., grain boundaries, dislocations), and superlattice structure.