Title Page
Abstract
Contents
Chapter 1. Introduction 21
1.1. Shape-memory polyurethane (SMPU) nanocomposites 21
1.2. Issues and Chanllenges of conventional computaional models on design of SMPU nanocomposites 25
1.3. Mesoscale-based multiscale analysis strategy 29
1.4. Thesis Outline 32
Chapter 2. Shape-memory behaviors of SMPU nanocomposites 34
2.1. Coarse-grained (CG) modeling of SMPU-Silica nanocomposites 34
2.1.1. Iterative Boltzmann inversion (IBI) method 35
2.1.2. CG modeling for thermoplastic polyurethane copolymer 38
2.1.3. CG modeling for Silica nanoparticle 44
2.2. Effect of molecular compositions on thermo-mechanical behaviors of pristine SMPU 51
2.2.1. Crystallinity of the SMPU 52
2.2.2. Melting temperature 55
2.2.3. Mechanical behaviors 55
2.2.4. Shape-memory properties 61
2.3. Effect of incorporation of silica nanofiller 70
2.3.1. SMPU microstructure on silica surface 70
2.3.2. Effect of silica nanoparticle content 75
Chapter 3. Phase separation behaviors of SMPU 86
3.1. Dissipative particle dynamics (DPD) simulation 87
3.2. Derivation of solubility parameters for SMPU copolymer 90
3.3. Morphologies of phase-separated SMPU 94
Chapter 4. Multiple phase separation behaviors of SMPU - Silica nanocomposites 102
4.1. Morphologies of phase-separated SMPU nanocomposites 102
4.1.1. Mixing energy and Flory-Huggins parameters between silica nanoparticle and polymer 102
4.1.2. Nanoparticle clustering behaviors 106
4.2. Effect of silica nanoparticle surface treatment 113
4.3. Multiscale continuum model for mechanical properties 123
4.3.1. Multiscale two-phase homogenization and verification 123
4.3.2. Development of interphase for nanoparticular agglomeration effect 127
4.3.3. Reinforcement effect of anisotropic nanofiller 134
Conclusions 143
Bibliography 145
국문 요약 162
Table 2.1. Bond length CG potential energy coefficients for SMPU copolymer. 42
Table 2.2. Bending angle CG potential energy coefficients for SMPU copolymer. 42
Table 2.3. Bond length CG potential energy coefficients for silica nanoparticle. 48
Table 2.4. Bending angle CG potential energy coefficients for silica nanoparticle. 48
Table 2.5. Information on the mesoscale CG models with different HSCs. (All models have the same number of soft-segment beads=8320 beads) 52
Table 2.6. Shape-memory peformances and crystallinity during the shape-memory cycle. 66
Table 2.7. Modeling of the mesoscale CG SMPU/Silica nanocomposites. 75
Table 2.8. Shape-fixity ratio (Rf) of the SMPU/Silica nanocomposites accordint to HSC (15, 32, and 50 wt.%) and silica content (3, 6, 10, 15 wt.%).[이미지참조] 76
Table 2.9. Shape-recovery ratio (Rr) of the SMPU/Silica nanocomposites accordint to HSC (15, 32, and 50 wt.%) and silica content (3, 6, 10, 15 wt.%).[이미지참조] 76
Table 2.10. Fractional free volume (FFV) change during the shape-memory ther mo-mechanical cycle. 78
Table 2.11. Characterization of the probability distribution (Figure 2.31) of RSilicα−Silicα.[이미지참조] 82
Table 3.1. DPD bead modeling with similar molecular volume for each segment of the SMPU. 90
Table 3.2. Information on the AA MD unit cells of each segment component of the SMPU copolymer (see atomistic configuration in Figure 3.1). 91
Table 3.3. Solubility parameter (𝛿) of pure MDI and PEO system cacluated from AA MD simulations. 93
Table 3.4. Repulsion parameter (αij) between SMPU DPD beads.[이미지참조] 93
Table 3.5. Information on the reduced units of DPD simulation 94
Table 4.1. DPD bead modeling of silica nanoparticle system. 103
Table 4.2. Flory-Huggins parameter (χij) between DPD beads of SMPU/Silica nanocomposites.[이미지참조] 105
Table 4.3. Repulsion parameter (αij) between DPD beads of SMPU/Silica nanocomposites.[이미지참조] 106
Table 4.4. Flory-Huggins parameter (χij) for silica surface treatment materials.[이미지참조] 115
Table 4.5. Repulsion parameter (αij) for silica surface treatment materials[이미지참조] 115
Table 4.6. Young's modulus and shear modulus of SMPU/Silica nanocomposites with the Mori-Tanaka model. 126
Table 4.7. Young's modulus and shear modulus of neat SMPU and nanocomposites calculated from CG MD and Mori-Tanaka models. 128
Table 4.8. Properties of interphase region for different HSCs of the SMPU. 130
Figure 1.1. Mechanism of shape memory behavior of SMPU. 22
Figure 1.2. Mesoscale simulation-based strategy for multiscale analysis of SMPU nanocomposites. 32
Figure 2.1. Chemical structures and bead-mapping of the SMPU copolymer. 39
Figure 2.2. Atomistic and coarse-grained (CG) MD models of the SMPU reference unit cell. 40
Figure 2.3. Example of the bond length distribution obtained from the AA reference model at 300 K and corresponding CG potential energy curve derived from the... 41
Figure 2.4. Example of the bending angle distribution obtained from the AA reference model at 300 K and corresponding CG potential energy curve derived... 41
Figure 2.5. The process of RDF converging to the target RDF through the IBI equation. 43
Figure 2.6. Comparison between the target RDF obtained through the AA reference model and the RDF obtained through the CG model where the IBI process was completed. 44
Figure 2.7. Core-shell bead mapping strategy for CG model of silica nanoparticle. 45
Figure 2.8. AA and CG MD configurations of the referenc unit cells for deriving CG potentials (a) between SMPU matrix and silica nanoparticle, and (b) between... 46
Figure 2.9. Results of bond length distribution and derived CG potential energy of silica nanoparticle (Co-Co, Co-S, S-S). 47
Figure 2.10. Results of bending angle distributions and derived CG potential energies of silica nanoparticle. 47
Figure 2.11. Comparisons between the target RDFs and CG RDFs (IBI-PC completed) for matrix-naniparticle interactions obtained from the reference unit... 49
Figure 2.12. Comparison between the target RDFs and CG RDFs (IBI-PC completed) for interparticle interactions obtained from the reference unit cells... 50
Figure 2.13. A depiction of the reference axis and direction vector within the polymer, which were used to define the crystallinity in the CG model. 53
Figure 2.14. Crystallinity of the pristine SMPU copolymers with various hard segmentcontents (HSCs) during the 80 ns of relation at 300 K. 54
Figure 2.15. CG MD configurations of semi-crystalline SMPU with (a) HSC=15 wt.%, (b) HSC=15 wt.%, and (c) HSC=32 wt.% and their specific volume... 56
Figure 2.16. CG configurations of PU15 (HSC: 15 wt.%) undergoing uniaxial tensile deformation at (a) T=300 K and (b) 410 K. 57
Figure 2.17. (a) Crystallinity and orientational order parameter of PU15 (HSC: 15 wt.%) under the uniaxial tensile test at T=300 K and 410 K. (b) RDF of the hard... 59
Figure 2.18. Elastic modulus reulsts of the SMPU copolymers according to HSCs and external temperatures. 61
Figure 2.19. CG simulation process of the 4-step shape-memory thermo-mechanical cycle. 63
Figure 2.20. 3-D plot (strain-temperature-crystallinity) of the SMPU with HSC=15 wt.% during the 4-step thermo-mehcniacl shape memory cycles. 64
Figure 2.21. Shape-memory 3-D plots of the SMPU with three different HSCs (15, 45, and 70 wt.%). 65
Figure 2.22. Changes in the radius of gyration (Rg) of the hard segment during shape recovery.[이미지참조] 69
Figure 2.23. Changes in the mean square displacement (MSD) in the loading direaction (x-axis) of each segment of the SMPU copolymer during shape recovery. 69
Figure 2.24. CG configurations of the SMPU nanocomposites with differenct HSCs (15, 32, 50, 70 wt.%) containing single silica nanoparticles with a radius of 30 Å. 70
Figure 2.25. Radial density of the SMPU matrix and the high-density interphase region 71
Figure 2.26. S value of each bead of SMPU matrix in PU15/Silica nanocomposite model. The average S value of the total matrix is 0.451, and the average S value of... 73
Figure 2.27. Comparisions between radial crystallinity distributions of the SMPU from the center of unitcell for pristine SMPU and from the center of the... 74
Figure 2.28. Changes in fractional free volume (FFV) during shape-memory cycles for silica content 3 wt.% and 15 wt.% of the (a) PU15, (b) PU32, and (c) PU50 nanocomposites. 78
Figure 2.29. Formation of free volume by nanoparticles in semi-crystalline polymer chains. 80
Figure 2.30. Nanoparticle agglomeration according to silica content and consequent formation of vacancy zone. 80
Figure 2.31. Probability distribution of RSilicα−Silicα (the distance between silica nanoparticles) for silica content (a) 6 wt.% and (b) 15 wt.% in undeformed and...[이미지참조] 81
Figure 2.32. (a) Comparision between the original shape and recovered shape of PU15 and PU50 with 15 wt.% of silica. (b) Changes in the mean square... 84
Figure 3.1. Atomistic configurations of pure MDI and PEO MD unit cells. A detailed information is listed in Table 3.1. 91
Figure 3.2. Modelling of the SMPU copolymer chains for DPD simulations. 95
Figure 3.3. Phase separation morphology of PU15 according to time step by DPD simulation. Yellow bead represents MDI bead and green bead represents PEO bead.... 96
Figure 3.4. Hard domain connections and distributions of the SMPU according to the HSCs (15, 21, 32, 45, and 50 wt.%) in 2 x 2 x 2 periodic unit cells. 98
Figure 3.5. Summarization of the architectural evolution of phase morphologies according to HSC of the SMPU. 101
Figure 4.1. Molecular unit segments constituting each bead of DPD. 103
Figure 4.2. Multiple phase separation morphology of PU15/Silica nanocomposite according to time step by DPD simulation. Yellow bead represents MDI bead,... 108
Figure 4.3. Final snapshots of DPD simulation results for different HSCs and silica nanoparticle contents (Silica with only Core beads). 109
Figure 4.4. Phase domain morphologies of the SMPU matrix and silica nanoparticle distributions (10 wt.%) for HSC (a) 15 wt.%, (b) 32 wt.%, and (c) 50 wt.% in 2 x 2... 111
Figure 4.5. Clustering density results as a function of HSC at different silica contents. 113
Figure 4.6. Chemical structure of silica surface treatment materials (a) silanol groups (Shell bead), (b) polydimethylsiloxane (PDMS bead), (c) Octylsilane (OS... 114
Figure 4.7. DPD modeling of silica surface treatment of SMPU/silica nanocomposites. 117
Figure 4.8. Distributions of silica nanoparticles as a result of DPD simulation according to HSC (a) 15 wt.%, (b) 32 wt.%, and (c) 50 wt.% and silica surface... 118
Figure 4.9. Clustering density change according to nanoparticle surface treatment of SMPU/Silica nanocomposites. 119
Figure 4.10. Nanoparticle distribution improvement effect by optimal silica surface treatment in (a) PU15 and (b) PU50. 122
Figure 4.11. Configurations of DPD model of PU15/Silica nancomposites and equivalent finite element model: (a) silica 3 wt.%, (b) silica 6 wt.%, and (c) silica 10 wt.%. 124
Figure 4.12. Homogenized (a) Young's modulus and (b) shear modulus of neat SMPU and nanocomposites with the silica contents without considering interphase. 126
Figure 4.13. Equivalent finite element model for 3-phase (matrix, particle, and interphase) systems. 130
Figure 4.14. Changes of particle distribution and interphase area in 3-phase FEM model according to the surface treatment for (a) PU15, (b) PU32, and (c) PU50. 132
Figure 4.15. Homogenized Young's modulus of SMPU nanocomposites and volume fraction of interphase region considering interphase effct and silica surface... 133
Figure 4.16. DPD modeling of silica surface treatment of cylindrical nanofiller. 134
Figure 4.17. Clustering density change of cylindrical nanofiller. 136
Figure 4.18. Cylinder distribution improvement by optimal surface treatment in (a) PU15 and (b) PU50. 137
Figure 4.19. Distributions of cylindrical nanofillers, and corresponding volume fraction of interaphse (Vint) and mechanical properties for (a) PU15 and (b) PU50. 137
Figure 4.20. (a) Bridging architectural morphology of cylindrical nanofillers in lamellar structure, and (b) 3 x 2 x 2 periodic unit cells. 139
Figure 4.21. (a) Distributions of cylindrical nanofillers, and corresponding volume fraction of interaphse (Vint) and mechanical properties for PU32 nancomposites,...[이미지참조] 142