From Gantry Systems to Robotic Arms: A Versatile Hybrid 3D Bioprinting Nozzle With Real-Time UV Curing

Abstract

Abstract Although sophisticated features are offered by commercial 3D bioprinters, research possibilities are often constrained by their proprietary nature and limited customization options. Users are typically confined to a predetermined set of materials, printing parameters, and hardware setups within these closed systems, which can impede the development of new bioinks, novel printing methodologies, or the incorporation of specialized equipment. In contrast, greater adaptability is afforded to researchers by custom-built 3D bioprinters developed in-house, allowing modifications to be made according to specific research requirements. By eliminating the limitations imposed by off-the-shelf solutions, the frontiers of bioprinting technology can be expanded. As part of the long-term objective to develop a versatile, cost-effective, and research-oriented in-house 3D bioprinter, an innovative hybrid nozzle system has been engineered. This system has been designed to integrate concurrent material extrusion with customizable, realtime UV-curing functionality, thereby enhancing adaptability and expanding potential applications in bioprinting research. The nozzle system has been made partially universally adaptable, requiring minimal modifications to fit and function on any base FDM 3D printer that operates using G-code instructions. By utilizing an ABB robot, the operation of a gantry robot has been simulated, allowing for the combination of this system with the nozzle to enable autonomous robotic bioprinting. Through this novel system, instantaneous solidification of photosensitive biopolymers during extrusion has been made possible, thereby eliminating subsequent curing steps and enhancing the precision and accuracy of fabricated structures. Fine-tuned regulation of both UV light exposure duration and intensity has been successfully demonstrated, enabling the polymerization of a wide range of photosensitive materials. The synergistic combination of user-defined extrusion and localized in-situ UV-curing is expected to create new opportunities for exploring novel biomaterial formulations and printing strategies, thereby accelerating scientific progress in the field of bioprinting. Future research efforts will be directed toward the integration of mHealth solutions with advanced machine learning to develop a highly customizable, user-friendly bioprinting platform that adapts to the dynamic needs of researchers. Real-time optimization of printing parameters through machine learning and the incorporation of remote monitoring will be leveraged to enhance usability, scalability, and precision in bioprinting applications.