Assessment reveals improved hydrogel print quality with specific printing parameters using machine learning, indicating biocompatibility is crucial in bioprinting.
Bioprinting continues to expand as a preferred fabrication technology as new biomaterials emerge. These biomaterials must be biocompatible, in addition to exhibiting suitable printing characteristics. Printability is usually gauged by metrics such as extrudability, fiber morphology, and shape fidelity, and optimized via design of experiments or machine learning methods. However, these optimization techniques often overlook dynamic variables such as time-dependent behavior and batch-to-batch variability that can significantly affect the quality of printed structures. In this study, we 3D-printed colloidal hydrogels to investigate the effects of printing parameters and elapsed time on overall hydrogel print quality.
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Perez et al. (2025) studied this question.
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