Sustainable Vat Photopolymerization-Based 3D-Printing through Dynamic Covalent Network Photopolymers.

Vat photopolymerization (VPP) based three-dimensional (3D) printing, including stereolithography (SLA) and digital light projection (DLP), is known for producing intricate, high-precision prototypes with superior mechanical properties. However, the challenge lies in the non-recyclability of covalently crosslinked thermosets used in these printing processes, limiting the sustainable utilization of printed prototypes. This review paper examines the recently explored avenue of VPP 3D-printed dynamic covalent network (DCN) polymers, which enable reversible crosslinks and allow for the reprocessing of printed prototypes, promoting sustainability. These reversible crosslinks facilitate the rearrangement of crosslinked polymers, providing printed polymers with chemical/physical recyclability, self-healing capabilities, and degradability. While various mechanisms for DCN polymer systems are explored, this paper focuses solely on photocurable polymers to highlight their potential to revolutionize the sustainability of VPP 3D printing.

4D-Printed Hydrogel Actuators through Diffusion-Path Architecture Design.

Recently, smart hydrogels have garnered considerable attention as biomedical devices, and several approaches have been introduced for their fabrication, including the incorporation of stimulus-responsive additives, utilization of molecular imprinting techniques, and application of multilayered hydrogels. However, the nonuniform properties resulting from these approaches limit the practical applications of hydrogels by causing inconsistent performance and behavior. In this study, we propose a novel approach to manipulating the swelling kinetics of hydrogels by engineering their diffusion-path architecture. By simply adjusting the diffusion path length within the hydrogel, we achieved a significant change in swelling kinetics. This approach enables precise control over the diffusion and transport processes within the hydrogel, resulting in enhanced swelling kinetics when reducing the diffusion path length. Furthermore, by strategically designing the diffusion-path architecture of a 3D-printed hydrogel specimen, we can fabricate smart hydrogel actuators that exhibit reversible shape transformations during swelling and deswelling through a nonequilibrium differential swelling. The proposed approach eliminates the need to modify the spatial properties of hydrogel structures such as cross-linking density, polymer, or additive compositions, thereby achieving uniform properties throughout the hydrogel and creating new possibilities for the development of advanced 4D-printed biomedical devices.

Toughened Hydrogels for 3D Printing of Soft Auxetic Structures.

Materials with negative Poisson's ratio have attracted considerable attention and offered high potential applications as biomedical devices due to their ability to expand in every direction when stretched. Although negative Poisson's ratio has been obtained in various base materials such as metals and polymers, there are very limited works on hydrogels due to their intrinsic brittleness. Herein, we report the use of methacrylated cellulose nanocrystals (CNCMAs) as a macro-cross-linking agent in poly(2-hydroxyethyl methacrylate) (pHEMA) hydrogels for 3D printing of auxetic structures. Our developed CNCMA-pHEMA hydrogels exhibit significant improvements in mechanical properties, which is attributed to the coexistence of multiple chemical and physical interactions between the pHEMA and CNCMAs. Structures printed by using CNCMA-pHEMA hydrogels show auxetic behavior with greatly enhanced toughness and stretchability compared to the hydrogel with a traditional cross-linking agent. Such strong and tough auxetic hydrogels would contribute toward establishing advanced flexible implantable devices such as biodegradable oesophageal self-expandable stents.

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning.

Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets.