Design and Fabrication of Nano-Particles with Customized Properties using Self-Assembly of Block-Copolymers

Functional nanoparticles (NPs) have gained significant attention as a promising application in various fields, including sensor, smart coating, drug delivery, and more. Here, we propose a novel mechanism assisted by machine-learning workflow to accurately predict phase diagram of NPs, which elegantly achieves tunability of shapes and internal structures of NPs using self-assembly of block-copolymers (BCP). Unlike most of previous studies, we obtain onion-like and mesoporous NPs in neutral environment and hamburger-like NPs in selective environment. Such novel phenomenon is obtained only by tailoring the topology of a miktoarm star BCP chain architecture without the need for any further treatment. Moreover, we demonstrate that the BCP chain architecture can be used as a new strategy for tuning the lamellar asymmetry of NPs. We show that the asymmetry between A and B lamellae in striped ellipsoidal and onion-like particles increases as the volume fraction of the A-block increases, beyond the level reached by linear BCPs. In addition, we find an extended region of onion-like structure in the phase diagram of A-selective environment, as well as the emergence of an inverse onion-like structure in the B-selective one. Our findings provide a valuable insight into the design and fabrication of nanoscale materials with customized properties, opening up new possibilities for advanced applications in sensing, materials science, and beyond.