Xiaofan Luo

Conductive shape memory nanocomposites for high speed electrical actuation

A new shape memory nanocomposite that exhibits rapid electrical actuation capabilities is fabricated by incorporating continuous, non-woven carbon nanofibers (CNFs) into an epoxy based SMP matrix. The fiber morphology and nanometre size provide a percolating conductive network with a large interfacial area. This not only gives high electrical conductivity but also simultaneously enhances heat transfer and recovery stress.

A functionally graded shape memory polymer

In this article we describe the preparation and characterization of a functionally graded shape memory polymer (SMP) that, unlike conventional SMPs, has a range of transition temperatures that are spatially distributed in a gradient fashion within one single article. This is achieved by post-curing a pre-cured glassy SMP in a linear temperature gradient that imposes different vitrification temperature limits at different positions along the gradient. Utilizing indentation-based surface shape memory coupled with optical measurements of photoelastic response, the capability of this material to respond over a wide range of thermal triggers is examined and correlated with the graded glass transition behavior. The shape recovery response of the gradient SMP under a condition of continuous heating is demonstrated. This new class of SMP offers great potential for such applications as passive temperature sensing and precise control of shape evolution during a thermally triggered shape recovery.

Mechanisms of triple-shape polymeric composites due to dual thermal transitions

Shape memory polymers (SMPs) are a class of smart materials capable of fixing a temporary shape and recovering the permanent shape in response to environmental stimuli such as heat, electricity, irradiation, moisture, or magnetic field, among others. Recently, multi-shape SMPs, which are capable of fixing more than one temporary shape and recovering sequentially from one temporary shape to another and eventually to the permanent shape, have attracted increasing attention. In general, there are two approaches to achieve a multi-shape memory effect (m-SME): the first one requires the SMP to have a broad temperature range of thermomechanical transition, such as a broad glass transition. The second approach uses multiple transitions to achieve m-SME, most notably, using two distinct transition temperatures to obtain a triple-shape memory effect (t-SME). The recently reported approach for designing and fabricating triple-shape polymeric composites (TSPCs) provides a much larger degree of design flexibility by separately tuning the two functional components (matrix and fiber network) to achieve optimum control of properties. The triple-shape memory behavior demonstrated by a TSPC is studied in this paper. This composite is composed of an epoxy matrix, providing a rubber–glass transition to fix one temporary shape, and an interpenetrating crystallizable PCL fiber network providing the system the melt–crystal transition to fix a second temporary shape. A one-dimension (1D) model that combines viscoelasticity for amorphous shape memory polymers (the matrix) with a constitutive model for crystallizable shape memory polymers (the fiber network) is developed to describe t-SME. The model includes the WLF and Arrhenius equations to describe the glass transition of the matrix, and the kinetics of crystallization and melting of the fiber network. The assumption that the newly formed crystalline phase of the fiber network is initially in a stress-free state is used to model the mechanics of evolving crystallizable phases. Experiments including uniaxial tension, stress relaxation, and triple-shape memory testing were carried out for parameter identification. The model accurately captures t-SME exhibited in experiments. The stress and stored energy analysis during the shape memory cycle provides insight into the mechanisms of shape fixing for the two different temporary shapes, the nature of both recovery events, as well as a guidance on how to design transitions to achieve the desired behavior.

Shape memory miscible blends for thermal mending

We report on miscible blends comprised of linear-poly(ε-caprolactone) (l-PCL) and chemically crosslinked network- PCL (n-PCL). The blends demonstrate unique Shape Memory Assisted Self-Healing (SMASH) property, which is the materials ability to close local microscopic cracks and heal those cracks by bonding the crack surfaces. For Shape Memory (SM) characterization, temporary deformation of the networks was achieved at room temperature. Samples were temporarily fixed below their crystalline temperature (Tc) and shape recovery was triggered by a temperature above the blends melting temperature (Tm). Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) were used to study the thermal properties of the blends and Dynamic Mechanical Analysis (DMA) and small-scale tensile testing were used to obtain the mechanical properties and self-healing efficiencies of the blends.