Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/78050
Title: Theoretical studies of self-assembly behavior and property-morphology relationships in polystyrene-polyisoprene block copolymers
Other Titles: การศึกษาเชิงทฤษฎีของพฤติกรรมการรวมกลุ่มกันเองและความสัมพันธ์ระหว่างสมบัติ-สัณฐานวิทยาในโคพอลิเมอร์แบบบล็อกพอลิสไตรีน-พอลิไอโซพรีน
Authors: Natthiti Chiangraeng
Authors: Piyarat Nimmanpipug
Runglawan Somsunan
Kiattikhun Manokruang
Supat Jiranusornkul
Natthiti Chiangraeng
Issue Date: Apr-2023
Publisher: Chiang Mai : Graduate School, Chiang Mai University
Abstract: Polystyrene-block-polyisoprene (PS-b-PI) diblock copolymer is an inspiring representative diblock copolymer due to its prominent variety of distinct morphologies. The molecular dynamics (MD) simulations were used to prepare crucial parameters for coarse-grained simulations, while we performed dissipative particle dynamics (DPD) and mesoscopic dynamics (MesoDyn) simulations to explore a morphology and its transition. An extended coarse-grained model based on actual molecular weight of block copolymer (BCP) was developed at set of annealing temperatures. With this simulation, the morphologies at an equilibrium state were in line with the reported experimental evidence at the selected temperature. Not only the morphology was correctly predicted, but its morphological transition from a homogenous state to attaining equilibrium morphology have been demonstrated and explained. To provide superior insightful information on the self-assembly behavior of the PS-b-PI diblock copolymer, the effects of the model chain length (N), volume fraction of the components (f) and temperature (T) on the morphology formation were systematically investigated. The phase diagrams were also constructed based on simulated morphologies and they were in good agreement with the previously proposed theoretical phase diagram of diblock copolymers. Moreover, we proposed a new analysis method for morphological identification and classification using principal component analysis (PCA). We found that radial distribution function (RDF) and structure factor (S(k)) corresponding to each morphology are key characteristics for morphological identification and classification. With this method, all morphologies were statistically and effectively grouped, and the results are in good agreement with the conventional method.
URI: http://cmuir.cmu.ac.th/jspui/handle/6653943832/78050
Appears in Collections:SCIENCE: Theses

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