Demonstration of a multiscale modeling technique: prediction of the stress–strain response of light activated shape memory polymers

• Published in: Smart materials and structures 2010-09, Vol.19 (9), p.094012-094012
• Main Authors: Beblo, Richard V, Weiland, Lisa Mauck
• Format: Article
• Language: English
• Subjects: Activated; Adaptation; Chains (polymeric); Construction; Empirical analysis; Exact sciences and technology; General equipment and techniques; Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Mathematical models; Modulus of elasticity; Physics; Polymers; Shape memory; Transducers
• ISSN: 0964-1726; 1361-665X
• DOI: 10.1088/0964-1726/19/9/094012

Presented is a multiscale modeling method applied to light activated shape memory polymers (LASMPs). LASMPs are a new class of shape memory polymer (SMPs) being developed for adaptive structures applications where a thermal stimulus is undesirable. LASMP developmental emphasis is placed on optical manipulation of Young's modulus. A multiscale modeling approach is employed to anticipate the soft and hard state moduli solely on the basis of a proposed molecular formulation. Employing such a model shows promise for expediting down-selection of favorable formulations for synthesis and testing, and subsequently accelerating LASMP development. An empirical adaptation of the model is also presented which has applications in system design once a formulation has been identified. The approach employs rotational isomeric state theory to build a molecular scale model of the polymer chain yielding a list of distances between the predicted crosslink locations, or r-values. The r-values are then fitted with Johnson probability density functions and used with Boltzmann statistical mechanics to predict stress as a function of the strain of the phantom polymer network. Empirical adaptation for design adds junction constraint theory to the modeling process. Junction constraint theory includes the effects of neighboring chain interactions. Empirical fitting results in numerically accurate Young's modulus predictions. The system is modular in nature and thus lends itself well to being adapted to other polymer systems and development applications.