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

Functional nanoparticles (NPs) have gained significant attention as promising applications in various fields, including sensor, smart coating, drug delivery, and more. Here, a novel mechanism assisted by machine-learning workflow is proposed 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, onion-like and mesoporous NPs in neutral environment and hamburger-like NPs in selective environment are obtained. Such novel phenomena are obtained only by tailoring the topology of a miktoarm star BCP chain architecture without the need for any further treatment. Moreover, it is demonstrated that the BCP chain architecture can be used as a new strategy for tuning the lamellar asymmetry of NPs. It is shown 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, an extended region of onion-like structure is found in the phase diagram of A-selective environment, as well as the emergence of an inverse onion-like structure in the B-selective one. The 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. SCFT-assisted machine-learning is proposed to construct the phase diagram of the structure of nanoparticles (NPs) formed by A1(A2B)3$\rm {A}_{1}(A_{2}B)_{3}$ miktoarm BCP. The structural phase transition of NPs induced by BCP chain architecture in both neutral and selective solution is revealed. By designing BCP chain architecture, this strategy provides a novel way to obtain NPs with novel structure and tailor the inner structure of NPs. image