共 11 条
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
来源:
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.
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页数:13
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