Predicting chiral nanostructures, lattices and superlattices in complex multicomponent nanoparticle self-assembly

Area of Science:

• Materials Science and Engineering
• Computational Materials Science
• Nanotechnology

Background:

• Bottom-up nanoparticle (NP) self-assembly offers promising routes to nanostructured materials for applications like energy storage and microelectronics.
• Predicting the self-assembled structures of complex, multicomponent NP systems, especially those with diverse interactions, remains a significant challenge beyond simplified models.

Purpose of the Study:

• To develop and apply a field theory approach for predicting the nanostructure of complex hybrid systems involving multiple types of nanoparticles (NPs) and interactions.
• To establish design criteria for controlling a wide range of NP-based nanomaterial structures.
• To explore the potential for creating novel nanostructures, such as chiral networks and NP superlattices, through theoretical prediction and design.

Main Methods:

• Utilized a field theory approach to model complex and multicomponent hybrid systems with various short- and long-range interactions.
• Proposed specific design criteria aimed at controlling the self-assembly of nanoparticles into desired nanomaterial structures.
• Validated theoretical predictions against recent experimental findings, particularly concerning NP/block copolymer assemblies.

Main Results:

• The theory successfully predicts chiral nanoparticle network structures in ABC triblock terpolymer directed assemblies with ligand-stabilized NPs, aligning with experimental observations.
• Predicted that long-range Coulomb interactions between NPs can induce superlattice formation within phase-separated block copolymer (BCP) nanostructures.
• Identified a novel strategy for NP superlattice formation in NP/BCP assemblies, which has not yet been experimentally realized.

Conclusions:

• The developed field theory approach is effective for predicting nanostructures in complex hybrid NP systems.
• The findings provide design criteria for controlling NP self-assembly and creating advanced nanomaterials.
• Predicted NP superlattices in NP/BCP assemblies represent a promising, yet unexplored, avenue for materials with potential applications in energy, metamaterials, and data storage.