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Light-responsive liquid crystal elastomer networks (LCNs) have received significant interest due to their potential application in soft robotics and shape-morphing devices. Here, we present a systematic examination of light responsive LCNs prepared using a catalyst-free Diels–Alder cycloaddition and a new azobenzene functionalized monomer for main-chain incorporation. The networks have robust mechanical stiffness that can be reversibly modulated by 1 GPa by turning the UV light on and off. This study highlights the contribution of photothermal softening to reversibly control rheological properties of the newly developed LCNs and demonstrates the ability to tune the modulus on demand. We believe this work will guide future developments of light-responsive LCNs based on the newly developed Diels–Alder cycloaddition. 

Optofluidic devices that dynamically respond to light stimuli have the potential to impart modern adaptive optics with intrinsic optical logic without the need for external power sources or feedback control. While photo actuation is typically associated with low energy efficiency compared with alternative modes of actuation, fluid lenses can be tuned with minimal work by generating small differential pressures across the surface of the lens to drive a change in focal length. In this study, we developed a wide aperture (9.5 mm) photothermally actuated lens that leverages spatially and thermodynamically informed design principles developed for resistively heated thermo-pneumatically actuated lenses. Using experimentally validated models to describe the curvature of pressurized elastomer-bound interfaces, we demonstrated phototunable modulation of the focal length from 124 mm to 90 mm in real time using 233 mW of 405 nm light over 30 s of irradiation with an estimated 8.2 µJ of mechanical work (10⁻⁴% efficiency). The initial focal length recovered after 60 s in the dark over three consecutive cycles of actuation. Additionally, the photoactuated response is shown to correlate well with the light intensity. 

Donor–acceptor Stenhouse adduct (DASA) photoswitches have gained a lot of attention since their discovery in 2014. Their negative photochromism, visible light absorbance, synthetic tunability, and the large property changes between their photoisomers make them attractive candidates over other commonly used photoswitches for use in materials with responsive or adaptive properties. The development of such materials and their translation into advanced technologies continues to widely impact forefront materials research, and DASAs have thus attracted considerable interest in the field of visible-light responsive molecular switches and dynamic materials. Despite this interest, there have been challenges in understanding their complex behavior in the context of both small molecule studies and materials. Moreover, incorporation of DASAs into polymers can be challenging due to their incompatibility with the conditions for most common polymerization techniques. In this review, therefore, we examine and critically discuss the recent developments and challenges in the field of DASA-containing polymers, aiming at providing a better understanding of the interplay between the properties of both constituents (matrix and photoswitch). The first part summarizes current understanding of DASA design and switching properties. The second section discusses strategies of incorporation of DASAs into polymers, properties of DASA-containing materials, and methods for studying switching of DASAs in materials. We also discuss emerging applications for DASA photoswitches in polymeric materials, ranging from lightresponsive drug delivery systems, to photothermal actuators, sensors and photoswitchable surfaces. Last, we summarize the current challenges in the field and venture on the steps required to explore novel systems and expand both the functional properties and the application opportunities of DASA containing polymers. 

Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.