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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.01912 |
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| _version_ | 1866917740862767104 |
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| author | Huang, Changhuang Bai, Kechun Zhu, Yanyan Andelman, David Man, Xingkun |
| author_facet | Huang, Changhuang Bai, Kechun Zhu, Yanyan Andelman, David Man, Xingkun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01912 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Design and Fabrication of Nano-Particles with Customized Properties using Self-Assembly of Block-Copolymers Huang, Changhuang Bai, Kechun Zhu, Yanyan Andelman, David Man, Xingkun Soft Condensed Matter Applied Physics 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. |
| title | Design and Fabrication of Nano-Particles with Customized Properties using Self-Assembly of Block-Copolymers |
| topic | Soft Condensed Matter Applied Physics |
| url | https://arxiv.org/abs/2408.01912 |