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The placenta plays a vital role in pregnancy by regulating selective exchange between maternal and fetal circulations and producing essential hormonal signals. Here, we present an in vitro placenta-on-a-chip platform that leverages 3D bioprinting to replicate the structural and functional features of the human placental barrier. This microengineered system utilizes digital light processing 3D bioprinting to fabricate the microfluidic mold and to construct 3D encapsulated cell cultures within a biomimetic hydrogel scaffold, enabling co-culture of three human cell types, including two derived from primary placental tissue. We demonstrate excellent cell viability, high metabolic activity, placental hormone secretion, and native-like selective barrier transport properties within the model. This system offers a versatile platform for experimental perturbations to explore mechanisms of normal placental function and identify contributors to placental dysfunction. 

Living tissues with high cell density and high resolution can be 3D printed. 

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. 

Green chemistry-based non-isocyanate polyurethanes (NIPU) are synthesized and 3D-printed via rapid, projection photopolymerization into compliant mechanisms of 3D structure with spatially-localized material properties. Trimethylolpropane allyl ether-cyclic carbonate is used to couple the unique properties of two types of reaction chemistry: (1) primary diamine-cyclic carbonate ring-opening conjugation for supplanting conventional isocyanate-polyol reactions in creating urethane groups, with the additional advantage of enabling modular segment interchangeability within the diurethane prepolymers; and (2) thiol–ene (click) conjugation for non-telechelic, low monodispersity, quasi-crystalline-capable, and alternating step-growth co-photopolymerization. Fourier transform infrared spectroscopy is used to monitor the functional group transformation in reactions, and to confirm these process-associated molecular products. The extent of how these processes utilize molecular tunability to affect material properties were investigated through measurement-based comparison of the various polymer compositions: frequency-related dynamic mechanical analysis, tension-related elastic-deformation mechanical analysis, and material swelling analysis. Stained murine myoblasts cultured on NIPU slabs were evaluated via fluorescent microscopy for “green-chemistry” affects on cytocompatibility and cell adhesion to assess potential biofouling resistance. 3D multi-material structures with micro-features were printed, thus demonstrating the capability to spatially pattern different NIPU materials in a controlled manner and build compliant mechanisms.