(545d) Multi-Scale Approach to Predict Tunable Organic-Inorganic Nanoparticle Self-Assembly

Inspired by naturally occurring hierarchical materials, numerous beautiful and functional mesoscale architectures have been constructed in labs using solid-state nanoscale building blocks and bio- or biomimetic molecules via self-assembly. Their structures and assembly dynamics are tightly regulated by the system's convoluted energetics and entropic information, which may even respond to a minuscule change. For example, we recently reported a pH-regulated self-assembly of silica nanoparticles facilitated by an engineered bifunctional silica-binding protein sfGFP::Car9-Car9. The assembly and disassembly can be reversibly and cyclically modulated by alternating the solution pH between 7.5 and 8.5. In another study, we observed that an increment of the peptoid (i.e., the ligand) length by two monomers at a time would change the arrangement of a CdS quantum dot superlattice from close packing to square, further to monoclinic. However, the complexity of the nanoscale colloidal system often makes it difficult to probe the underlying physics via experimental means. Alternatively, we can construct suitable multi-scale theoretical toolkits by carefully selecting and combining theories and simulation algorithms from different levels (i.e., from ab initio to coarse-grained models) to connect the scales and gain physical insights into the assembly mechanisms. In this talk, I will elaborate on how we used multi-scale theoretical frameworks to reveal the key driving forces of self-assembly in these two cases. The knowledge we gained on the distinctive roles of organic ligands has further inspired new design principles in future studies