|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
||Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
||Aaron Michelson, Brian Minevich, Hamed Emamy, Xiaojing Huang, Yong Chu, Hanfei Yan, Oleg Gang
|On-Site Speaker (Planned)
We applied a scanning hard x-ray microscopy to volumetrically characterize DNA-assembled nanoparticle superlattices with a single-particle precision and to reveal the positions of about 104 individual gold nanoparticles (AuNP) within a superlattice at 7nm resolution. The lattice is assembled from 20-nm AuNP and DNA tetrahedra frames whose vertices were complementary encoded with DNA sequences grafted to AuNP surface. The formed lattice was templated by silica, thus creating a robust 3D architecture. The real-space lattice reconstruction enables the discovery and identification of structural motifs associated with vacancies, inclusions, screw dislocations, and grain boundaries that reveal stark similarities between nanoparticle assemblies and their counterparts in atomic crystals. Through a volumetric particle-by-particle probing of superlattices, this study sheds light on the relationship between a systems design, assembly process, and a resulting 3D nanoparticle organization. In this talk we will highlight the developed method, and its application to revealing 3D structure of nanoparticle-based materials.