Understanding the shape memory behavior of thermoplastic polyurethane elastomers with coarse-grained molecular dynamics simulations

MRS Advances ◽  
2017 ◽  
Vol 2 (6) ◽  
pp. 375-380 ◽  
Author(s):  
Md Salah Uddin ◽  
Jaehyung Ju
Keyword(s):  
Molecular Dynamics ◽  
Shape Memory ◽  
Shape Recovery ◽  
Thermoplastic Polyurethane ◽  
Coarse Grained ◽  
Polyurethane Elastomers ◽  
Weight Percentage ◽  
Shape Memory Behavior ◽  
Soft Segments ◽  
Hard Segments

ABSTRACTWe perform molecular dynamics (MD) simulations to understand thermally triggered shape memory behavior of a thermoplastic polyurethane (TPU) elastomer with an enhanced coarse-grained (CG) model. Hard and soft phases of shape memory polymers (SMPs) are known as fixed and reversible phase, respectively. Fixity depends on the content of hard segments due to their restricted mobility. On the contrary, recovery depends on the dynamic motion of the soft segments as well the degree of cross-linking, which is also affected by the quantity of hard segment. Several CG models of the TPU are constructed varying the weight percentage of soft segments to observe their effects on shape recovery and fixity. All of the models are equilibrated at 300K (above glass transition, Tg: 200-250 K) and deformed under uniaxial loading with NPT (isothermal-isobaric) ensembles. The deformed state is cooled to 100K (below Tg) and further equilibrated to estimate the shape fixity. Shape recovery is predicted by heating and equilibrating the structures back to 300K. By the end of this study, we may answer how much the shape fixities and recoveries are changed for varying concentration of hard segments from thermomechanical cycles with CGMD simulations.

Prediction of Hysteresis of a Thermoplastic Polyurethane Using Coarse-Grained Molecular Dynamics

2016 ◽  
Author(s):  
Md Salah Uddin ◽  
Jaehyung Ju
Keyword(s):  
Molecular Dynamics ◽  
Thermoplastic Polyurethane ◽  
Atmospheric Condition ◽  
Hysteresis Loss ◽  
Coarse Grained ◽  
Stress Strain ◽  
Hysteresis Losses ◽  
Rate Dependent ◽  
Hard Segments ◽  
Coarse Grained Molecular Dynamics

We predict hysteresis of a thermoplastic polyurethane (TPU) varying the configurations and weight % of hard segments from 34.90% to 62.30% using coarse-grained molecular dynamics (CGMD) simulations. Rate-dependent stress-strain responses of the molecular models are justified between energy equivalence constitutive modeling and atomic viral stresses. Uniaxial cyclic loading (tension/compression) of the coarse-grained (CG) models are performed using NPT ensembles (isothermal-isobaric) at the atmospheric condition to ensure no stresses in the other two directions except the loading directions. Engineering stresses are estimated from atomic viral stresses at different frequencies and up to various strain levels, whereas areas under the stress-strain curves give the hysteresis loss under cyclic deformations. We correlate the hysteresis losses of all of the models with their bulk moduli and Poisson’s ratios. By the end of the study, we may answer the following research questions: i) How much hysteresis loss increases due to increasing the weight% of hard segments from 34.90% to 62.30%? ii) How sensitive are the losses corresponding to strain amplitudes from 5% to 15% and frequencies from 1.67 × 1011 Hz to 5.0 × 1011 Hz? iii) In order to reduce the hysteresis loss, how much we have to compromise in bulk modulus and how much Poisson’s ratio will be increased corresponding to that compensation. This molecular simulation tool can be used to design new rubber materials with better mechanical properties and lower hysteresis losses without the trial and error based experimental work.

Rheology of Thermoplastic Polyurethanes

2007 ◽  
Author(s):  
Chang Dae Han
Keyword(s):  
Rheological Behavior ◽  
Thermoplastic Polyurethane ◽  
Complete Characterization ◽  
Chain Extender ◽  
Reaction Sequence ◽  
Molecular Weights ◽  
Soft Segments ◽  
Hard Segments ◽  
A Chain ◽  
Long Chain

Thermoplastic polyurethane (TPU) has received considerable attention from both the scientific and industrial communities (Hepburn 1982; Oertel 1985; Saunders and Frish 1962). Applications for TPUs include automotive exterior body panels, medical implants such as the artificial heart, membranes, ski boots, and flexible tubing. Figure 10.1 gives a schematic that shows the architecture of TPU, consisting of hard and soft segments. Hard segments, which form a crystalline phase at service temperature, are composed of diisocyanate and short-chain diols as a chain extender, while soft segments, which control low-temperature properties, are composed of difunctional long-chain polydiols with molecular weights ranging from 500 to 5000. The soft segments form a flexible matrix between the hard domains. TPUs are synthesized by reacting difunctional long-chain diol with diisocyanate to form a prepolymer, which is then extended by a chain extender via one of two routes: (1) by a dihydric glycol chain extender or (2) by a diamine chain extender. The most commonly used diisocyanate is 4,4’-diphenylmethane diisocyanate (MDI), which reacts with a difunctional polyol forming soft segments, such as poly(tetramethylene adipate) (PTMA) or poly(oxytetramethylene) (POTM), to produce TPU, in which 1,4-butanediol (BDO) is used as a chain extender. There are two methods widely used to produce TPU: (1) one-shot reaction sequence and (2) two-stage reaction sequence. The reaction sequences for both methods are well documented in the literature (Hepburn 1982). It should be mentioned that MDI/BDO/PTMA produces ester-based TPU. One can also produce ether-based TPU when MDI reacts with POTM using BDO as a chain extender. TPUs are often referred to as “multiblock copolymers.” In order to have a better understanding of the rheological behavior of TPUs, one must first understand the relationships between the chemical structure and the morphology; thus, a complete characterization of the materials must be conducted. The rheological behavior of TPU depends, among many factors, on (1) the composition of the soft and hard segments, (2) the lengths of the soft and hard segments and the sequence length distribution, (3) anomalous linkages (branching, cross-linking), and (4) molecular weight.

scholarly journals Shape-Memory-Recovery Characteristics of Microcellular Foamed Thermoplastic Polyurethane

Polymers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 351
Author(s):  
Chang-Seok Yun ◽  
Joo Seong Sohn ◽  
Sung Woon Cha
Keyword(s):  
Shape Memory ◽  
Cell Density ◽  
Shape Recovery ◽  
Structural Changes ◽  
Thermoplastic Polyurethane ◽  
Testing Machine ◽  
Base Material ◽  
Memory Mechanism ◽  
Foaming Process ◽  
Dog Bone

We investigated the shape-recovery characteristics of thermoplastic polyurethane (TPU) with a microcellular foaming process (MCP). Additionally, we investigated the correlation between changes in the microstructure and the shape-recovery characteristics of the polymers. TPU was selected as the base material, and the shape-recovery characteristics were confirmed using a universal testing machine, by manufacturing dog-bone-type injection-molded specimens. TPUs are reticular polymers with both soft and hard segments. In this study, we investigated the shape-memory mechanism of foamed polymers by maximizing the shape-memory properties of these polymers through a physical foaming process. Toward this end, TPU specimens were prepared by varying the gas pressure, foaming temperature, and type of foaming gas in the batch MCP. The effects of internal structural changes were investigated. These experimental variables affected the microstructure and shape-recovery characteristics of the foamed polymer. The generated cell density changed, which affected the shape-recovery characteristics. In general, a higher cell density corresponded to a higher shape-recovery ratio.

scholarly journals Shape-Memory Properties of Segmented Polymers Containing Aramid Hard Segments and Polycaprolactone Soft Segments

Polymers ◽  
2010 ◽  
Vol 2 (2) ◽  
pp. 71-85 ◽  
Author(s):  
Christian Schuh ◽  
Kerstin Schuh ◽  
Maria C. Lechmann ◽  
Louis Garnier ◽  
Arno Kraft
Keyword(s):  
Shape Memory ◽  
Soft Segments ◽  
Shape Memory Properties ◽  
Hard Segments
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