Multimaterial vat photopolymerization: Computational optimization of slicing workflow for complex tissue geometries

Multi-material printing using digital light processing (DLP) has progressed from a niche laboratory method to a scalable technology capable of fabricating complex and functionally tissue constructs. However, current multi-material DLP workflows face significant limitations. Material changes typically require repeated washing and reloading steps, increasing print time, risking cross-contamination or layer misalignment, and ultimately constraining scaffold design complexity and biological relevance. To address these challenges, we present a computational pipeline that significantly improves the efficiency, precision, and usability of DLP for multimaterial bioprinting. Our system includes three key innovations: (1) a high-resolution segmentation and material-labeling method using computer graphics techniques for accurate material assignment in STL models; (2) a computer vision–based algorithm for real-time detection and correction of material interference or contamination; and (3) a GPU-accelerated layer sequencing method that supports rapid, precise material switching within single-layer projections. Experimental validation demonstrates improved print fidelity, reduced processing time, and higher material resolution. We further showcase the practical utility of our system by bioprinting a multimaterial tissue construct composed of a PEGDA-based scaffold integrated with a GelMA-based cell-laden microenvironment. This work represents a significant step toward enabling scalable, high-resolution, and biologically functional scaffold fabrication for advanced tissue engineering applications.