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Effect of Chain Architecture on Phase Behavior of Giant Surfactant Constructed from Nanoparticle Monotethered by Single Diblock Copolymer Chain
- Wang, Yingying, Cui, Jie, Han, Yuanyuan, Jiang, Wei
- Langmuir 2018 v.35 no.2 pp. 468-477
- composite polymers, density functional theory, entropy, guidelines, mathematical theory, nanoparticles, physical phases, separation, surfactants
- The phase behaviors of the giant surfactant constructed from a nanoparticle (NP) monotethered by a single AB diblock copolymer chain were investigated by combining self-consistent field theory and density functional theory. Three types of giant surfactants with different chain architectures were constructed via changing the location of NP on the diblock copolymer chain. The simulation results show that the introduction of the NP can induce phase separation of the originally disordered AB diblock copolymers, and phase diagrams as a function of the chain length ratio of A block and the attraction between A block and NP were constructed for the three giant surfactant systems. Via changing the location of NP from the end of B block to the AB-junction point and to the end of A block, the conformational entropies of the systems gradually decrease, leading to a significant difference in phase behaviors. When the NP is tethered to the end of B block, the giant surfactant system has the smallest phase-separation region in the phase diagram, and the resulting ordered structures have the smallest feature sizes. However, when the NP is tethered to the end of A block, the giant surfactant system has the largest phase-separation region, as well as the largest feature sizes of ordered structures. Moreover, the distributions of the NPs within microphase-separated domain can be well tailored by changing the chain length ratio of A block or the attraction between A block and NP in all of the three giant surfactant systems. These findings provide the guideline for the preparation of polymer–nanoparticle composites with controllable morphologies, desirable feature sizes, and precise NP distributions in experiments.