Understanding the shape-memory mechanism of thermoplastic polyurethane by investigating the phase-separated morphology: A dissipative particle dynamics study

被引:1
|
作者
Park, Sungwoo [1 ]
Lee, Jeong-ha [2 ]
Cho, Maenghyo [1 ,3 ]
Lee, Yun Seog [1 ,3 ]
Chung, Hayoung [4 ]
Yang, Seunghwa [5 ]
机构
[1] Seoul Natl Univ, Republ Korea Inst Adv Machines & Design, Dept Mech & Aerosp Engn, Seoul, South Korea
[2] Rice Univ, Dept Mat Sci & Nanoengn, Houston, TX USA
[3] Seoul Natl Univ, Inst Adv Machines & Design, Seoul, South Korea
[4] Ulsan Natl Inst Sci & Technol UNIST, Dept Mech Engn, Ulsan 44919, South Korea
[5] Chung Ang Univ, Sch Energy Syst Engn, Mech Engn Div, 84 Heukseok Ro, Seoul 06974, South Korea
基金
新加坡国家研究基金会;
关键词
Shape-memory polyurethane; Phase separation; Dissipative particle dynamics; Mesoscale simulation; Solubility parameter; Phase morphology; Shape-memory mechanism; SEGMENTED POLYURETHANES; MOLECULAR-DYNAMICS; BLOCK-COPOLYMERS; X-RAY; SIMULATION; POLYMERS; BEHAVIOR; COMPOSITES; SCATTERING; STRATEGY;
D O I
10.1016/j.polymertesting.2024.108531
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Shape-memory polyurethanes (SMPUs) are promising materials that change shape in response to external heat. These polymers have a dual-segment structure: a hard segment for netpoint and a soft segment for molecular switch. Understanding the molecular behavior of each segment and microphase-separated morphology is crucial for comprehending the shape-memory mechanism. This study aimed to understand the shape-memory behavior by observing the phase separation of SMPU using mesoscale models based on dissipative particle dynamics (DPD) simulations. The SMPU copolymer was modeled using 4,4 '-diphenylmethane diisocyanate (MDI, hard segment) and poly(ethylene oxide) (PEO, soft segment). By calculating segment solubility and repulsion parameters, we found that the hard-segment domain changes from isolated form to a lamellar and interconnected structure and eventually to a continuous form as its content increases. Combining these insights with shape-memory performance models can enhance our understanding of better SMPU design and contribute significantly to the optimization of smart stimuli-responsive materials.
引用
收藏
页数:13
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