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Shape‐morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human–machine interfaces, biomimetic robotics, and tools for biological systems. To achieve 3D programmable shape morphing (PSM), the deployment of array‐based actuators is essential. However, a critical knowledge gap in 3D PSM is controlling the complex systems formed by these soft actuator arrays to mimic the morphology of the target shapes. This study, for the first time, represents the configuration of shape‐morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. Shape Morphing Net (SMNet), a method that realizes the regression from point cloud to high‐dimensional control input vectors, is proposed. It has been applied to 3D PSM devices with three different actuator mechanisms, demonstrating its universal applicability to inversely reproduce the target shapes. Further, applied to previous 2D PSM devices, SMNet significantly enhances control precision from 82.23% to 97.68%. In the demonstrations of morphology mimicking, 3D PSM devices successfully replicate arbitrary target shapes obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable. 

In the field of biomechanics, customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology, primarily due to the intricate coupling and nonlinear actuation of actuator arrays, which complicates the precise control of strain fields. This paper introduces a bioreactor designed with a 9 × 9 array of independently controllable dielectric elastomer actuators (DEAs), addressing this challenge. We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control. To generate training data, a finite element analysis (FEA) simulation model was developed. In the FEA, the device was prestretched, followed by the random assignment of voltages to each pixel, yielding 10,000 distinct output strain field images for the training set. For inverse control, a multilayer perceptron (MLP) is utilized to predict control inputs from images, whereas, for forward control, MLP maps control inputs to low-resolution images, which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network (SRGAN). Demonstrations include inputting biomechanically significant strain fields, where the method successfully replicated the intended fields. Additionally, by using various tumor–stroma interfaces as inputs, the bioreactor demonstrated its ability to customize strain fields accordingly, showcasing its potential as an advanced testbed for tumor biomechanics research. 

Abstract Dielectric elastomer actuators (DEAs) exhibit fast actuation and high efficiencies, enabling applications in optics, wearable haptics, and insect-scale robotics. However, the non-uniformity and high sheet resistance of traditional soft electrodes based on nanomaterials limit the performance and operating frequency of the devices. In this work, we computationally investigate electrodes composed of arrays of stiff fiber electrodes. Aligning the fibers along one direction creates an electrode layer that exhibits zero stiffness in one direction and is predicted to possess high and uniform sheet resistance. A comprehensive parameter study of the fiber density and dielectric thickness reveals that the fiber density primary determines the electric field localization while the dielectric thickness primarily determines the unit cell stiffness. These trends identify an optimal condition for the actuation performance of the aligned electrode DEAs. This work demonstrates that deterministically designed electrodes composed of stiff materials could provide a new paradigm with the potential to surpass the performance of traditional soft planar electrodes. 

Objective: Adherent cell behavior is influ- enced by a complex interplay of factors, including chemical and mechanical signals. In vitro experiments that mimic the mechanical environment experienced by cells in vivo are crucial for understanding cellular behavior and the progression of disease. In this study, we developed and validated a low-cost pneumatically-controlled cell stretcher with independent control of strain in two directions of a membrane, enabling unequal biaxial stretching and real- time microscopy during actuation. Methods: The stretch- ing was achieved by two independent pneumatic channels controlled by electrical signals. We used finite element simulations to compute the membrane’s strain field and particle tracking algorithms based on image processing techniques to validate the strain fields and measure the cell orientation and morphology. Results: The device can supply uniaxial, equibiaxial, and unequal biaxial stretching up to 15% strain in each direction at a frequency of 1Hz, with a strain measurement error of less than 1%. Through live cell imaging, we determined that distinct stretching patterns elicited differing responses and alterations in cell orientation and morphology, particularly in terms of cell length and area. Conclusion: The device successfully pro- vides a large, uniform, and variable strain field for cell experiments, while also enabling real-time, live cell imag- ing. Significance: This scalable, low-cost platform provides mechanical stimulation to cell cultures by independently controlling strains in two directions. This could contribute to a deeper understanding of cellular response to bio- realistic strains and could be useful for future in vitro drug testing platforms. 

ABSTRACT: Covalent adaptive networks combine the advantages of cross-linked elastomers and dynamic bonding in a single system. In this work, we demonstrate a simple one-pot method to prepare thiol−ene elastomers that exhibit reversible photoinduced switching from an elastomeric gel to fluid state. This behavior can be generalized to thiol−ene cross-linked elastomers composed of different backbone chemistries (e.g., polydimethylsiloxane, polyethylene glycol, and polyurethane) and vinyl groups (e.g., allyl, vinyl ether, and acrylate). Photoswitching from the gel to fluid state occurs in seconds upon exposure to UV light and can be repeated over at least 180 cycles. These thiol−ene elastomers also exhibit the ability to heal, remold, and serve as reversible adhesives. KEYWORDS: covalent adaptive network, elastomer chemistry, click chemistry, self-healing, photoresponsive materials, adhesives 

Abstract The dissemination of sensors is key to realizing a sustainable, ‘intelligent’ world, where everyday objects and environments are equipped with sensing capabilities to advance the sustainability and quality of our lives—e.g. via smart homes, smart cities, smart healthcare, smart logistics, Industry 4.0, and precision agriculture. The realization of the full potential of these applications critically depends on the availability of easy-to-make, low-cost sensor technologies. Sensors based on printable electronic materials offer the ideal platform: they can be fabricated through simple methods (e.g. printing and coating) and are compatible with high-throughput roll-to-roll processing. Moreover, printable electronic materials often allow the fabrication of sensors on flexible/stretchable/biodegradable substrates, thereby enabling the deployment of sensors in unconventional settings. Fulfilling the promise of printable electronic materials for sensing will require materials and device innovations to enhance their ability to transduce external stimuli—light, ionizing radiation, pressure, strain, force, temperature, gas, vapours, humidity, and other chemical and biological analytes. This Roadmap brings together the viewpoints of experts in various printable sensing materials—and devices thereof—to provide insights into the status and outlook of the field. Alongside recent materials and device innovations, the roadmap discusses the key outstanding challenges pertaining to each printable sensing technology. Finally, the Roadmap points to promising directions to overcome these challenges and thus enable ubiquitous sensing for a sustainable, ‘intelligent’ world.