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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
Presentation Title Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
Author(s) Aaron Michelson, Brian Minevich, Hamed Emamy, Xiaojing Huang, Yong Chu, Hanfei Yan, Oleg Gang
On-Site Speaker (Planned) Aaron Michelson
Abstract Scope 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.
Proceedings Inclusion? Undecided
Keywords Characterization,


Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly
Designing Nano-architectured Materials with a Machine-learning Augmented Framework
Discovery of Nanocomposite Phase Change Memory Materials via Closed-loop Autonomous Combinatorial Experimentation
Intelligent Design of Additively Manufactured Architected Materials
Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites
Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
“Big Data” Characterization of Material Properties and High Temperature Kinetics

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