Research

• Programming structure-dependent mechanoluminescence behaviors

Mechanoluminescence (ML) materials have great potential due to their ability to transform mechanical stimuli into optical signals. However, the ML-based systems have not been fully leveraged as restricted by the relatively simple geometry design, which inherently limits the programmability of the luminescence behaviors and mechanical properties. We demonstrate the visualization of stress distribution through various ML cellular structures composed of periodic truss arrays and study the structure-stress-luminescence relationships by developing granular ML-elastomer precursor ink. We work to gain fundamental and quantitative knowledge that supports the design of next-generation printable ML-based stress sensors and wearable devices.

Manipulating the spatial distribution and assembled structure of functional particles at the micro- or nanoscale is recognized as a critical barrier to the fabrication and design of miniaturized stimuli-responsive polymer-based actuators and shape reconfigurable matter. To overcome such challenges, we explore the fundamental mechanism of surface acoustic wave-driven spatiotemporal distributions of particles in polymer solutions capable of three-dimensional shape transformation after crosslinking by understanding associated principles in physics, mechanics, and dynamics. This fundamental understanding could allow precise particle manipulation in more complex patterns in stimuli-responsive polymer networks, thereby enabling programmable shape reconfiguration and motion at the sub-mm scale.

Shape morphing hydrogel hybrids in response to external controls or environmental stimuli are important candidates for soft robotics, automatic actuators, and biomedical applications. We work to incorporate various nanocomposite materials to introduce engineered local variations such as swelling/deswelling and stiffness, in order to program the shape deformation of the hydrogel hybrids. We focus on developing new chemical and physical approaches by understanding the underlying mechanisms shape reconfigurable hydrogel hybrids to achieve unconventional shape morphing and actuation systems that could address the remaining challenges.

Current stimuli-responsive materials lack a diversity of inputs and consequently respond with modest material outputs. The focus is to develop methods for integrating engineered living matter into polymeric materials, and address the questions arising from the living/non-living interface. For instance, we are interested in creating new living-composite materials that are responsive to diverse stimuli and capable of generating complex, genetically encoded material outputs. These outcomes are potentially useful in biosynthetic electronics, chemical threat decontamination, therapeutic synthesis/delivery, soft robotics, etc.

We explore the paradigm for machine learning-guided experiments for predicting the material properties, and developing self-evolved soft materials toward target shape transformation.