ABSTRACT
Smart hydrogels are a promising candidate for the development of next-generation soft materials due to their stimuli-responsiveness, deformability, and biocompatibility. However, it remains challenging to enable hydrogels to actively adapt to various environmental conditions like living organisms. In this work, supramolecular additives are introduced to the hydrogel matrix to confer environmental adaptiveness. Specifically, their microstructures, swelling behaviors, mechanical properties, and transparency can adapt to external environmental conditions. Moreover, the presence of hydrogen bonding provides the hydrogel with applicable rheological properties for 3D extrusion printing, thus allowing for the facile preparation of thickness-dependent camouflage and multistimuli responsive complex. The environmentally adaptive hydrogel developed in this study offers new approaches for manipulating supramolecular interactions and broadens the capability of smart hydrogels in information security and multifunctional integrated actuation.
ABSTRACT
Analyzing hydrogel microstructure through scanning electron microscopy (SEM) images is crucial in understanding hydrogel properties. However, the analysis of SEM images in hydrogel research heavily relies on the intuition of individual researchers and is constrained by the limited size of the dataset. To address this, we propose SEMPro, a data-driven solution using web-scraping and deep learning (DL) to compile and analyze the structure-property relationships of hydrogels through SEM images. It accurately predicts the elastic modulus from SEM images within the same order of magnitude and displays a learned extraction of modulus-relevant features in SEM images as seen through the nontrivial activation mapping and transfer learning. By employing Explainable AI through activation map exposure, SEMPro validates the model predictions. SEMPro represents a closed-loop data collection and analysis pipeline, providing critical insights into hydrogels and soft materials. This innovative approach has the potential to revolutionize hydrogel research, offering high-dimensional insights for further advancements.
ABSTRACT
A method coupling microgel jamming and temperature-responsive capillary networking is developed to manipulate the rheological properties of microgel-capillary suspensions by varying microgel sizes, volume fraction of capillary solution, and temperature after polymerization and photo-crosslinking. This approach allows for the 3D extrusion of this suspension to print complex structures that can be readily scaled up and applied to biomedical fields and soft material-based actuation.
ABSTRACT
Liquid crystalline elastomers (LCEs) are polymer networks exhibiting anisotropic liquid crystallinity while maintaining elastomeric properties. Owing to diverse polymeric forms and self-alignment molecular behaviors, LCEs have fascinated state-of-the-art efforts in various disciplines other than the traditional low-molar-mass display market. By patterning order to structures, LCEs demonstrate reversible high-speed and large-scale actuations in response to external stimuli, allowing for close integration with 4D printing and architectures of digital devices, which is scarcely observed in homogeneous soft polymer networks. In this review, we collect recent advances in 4D printing of LCEs, with emphases on synthesis and processing methods that enable microscopic changes in the molecular orientation and hence macroscopic changes in the properties of end-use objects. Promising potentials of printed complexes include fields of soft robotics, optics, and biomedical devices. Within this scope, we elucidate the relationships among external stimuli, tailorable morphologies in mesophases of liquid crystals, and programmable topological configurations of printed parts. Lastly, perspectives and potential challenges facing 4D printing of LCEs are discussed.
