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The majority of 3D‐printed biodegradable biomaterials are brittle, limiting their application to compliant tissues. Poly(glycerol sebacate) acrylate (PGSA) is a synthetic biocompatible elastomer and compatible with light‐based 3D printing. In this article, digital‐light‐processing (DLP)‐based 3D printing is employed to create a complex PGSA network structure. Nature‐inspired double network (DN) structures consisting of interconnected segments with different mechanical properties are printed from the same material in a single shot. Such capability has not been demonstrated by any other fabrication techniques so far. The biocompatibility of PGSA is confirmed via cell‐viability analysis. Furthermore, a finite‐element analysis (FEA) model is used to predict the failure of the DN structure under uniaxial tension. FEA confirms that the DN structure absorbs 100% more energy before rupture by using the soft segments as sacrificial elements while the hard segments retain structural integrity. Using the FEA‐informed design, a new DN structure is printed and tensile test results agree with the simulation. This article demonstrates how geometrically‐optimized material design can be easily and rapidly constructed by DLP‐based 3D printing, where well‐defined patterns of different stiffnesses can be simultaneously formed using the same elastic biomaterial, and overall mechanical properties can be specifically optimized for different biomedical applications.

 

Abstract The morphological architecture of photosynthetic corals modulates the light capture and functioning of the coral-algal symbiosis on shallow-water corals. Since corals can thrive on mesophotic reefs under extreme light-limited conditions, we hypothesized that microskeletal coral features enhance light capture under low-light environments. Utilizing micro-computed tomography scanning, we conducted a novel comprehensive three-dimensional (3D) assessment of the small-scale skeleton morphology of the depth-generalist coral Stylophora pistillata collected from shallow (4–5 m) and mesophotic (45–50 m) depths. We detected a high phenotypic diversity between depths, resulting in two distinct morphotypes, with calyx diameter, theca height, and corallite marginal spacing contributing to most of the variation between depths. To determine whether such depth-specific morphotypes affect coral light capture and photosynthesis on the corallite scale, we developed 3D simulations of light propagation and photosynthesis. We found that microstructural features of corallites from mesophotic corals provide a greater ability to use solar energy under light-limited conditions; while corals associated with shallow morphotypes avoided excess light through self-shading skeletal architectures. The results from our study suggest that skeleton morphology plays a key role in coral photoadaptation to light-limited environments. 

Abstract Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes. 

Abstract When using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures.