Multifunctional in situ imaging of biological materials under load: A synchrotron-based platform for large-scale image-guided failure analysis

Biological materials achieve remarkable mechanical performance through hierarchical structuring across multiple scales, yet their damage mechanisms remain poorly understood. High-resolution imaging, particularly Synchrotron Radiation-based micro-Computed Tomography (SR-μCT), enables non-destructive 3D visualization at the microscale. However, the lack of integrated in situ mechanical testing systems limits the ability to capture real-time damage evolution under loading. This study presents a novel, versatile multifunctional in situ testing system specifically designed for Image-Guided Failure Assessment (IGFA) of biological materials using SR-μCT. The system enables micro-tensile, micro-compression, and micro-torsion testing under displacement or rotation angle control. Its automation, geometric adaptability, and compatibility with diverse biological specimens enhance experimental precision and efficiency. The system performance is demonstrated through three case studies: compression-based IGFA of trabecular bone, tension-based IGFA of mycelium-covered fibers, and torsion-based IGFA of vertebral bone. To analyze the complex interactions between microstructure and mechanical performance, an artificial intelligence (AI)-driven computational framework is proposed and integrated into this study, suggesting novel pathways for decoding micro-architectural contributions to damage mechanisms. This synergy of high-resolution imaging, in situ testing, and AI-driven analysis provides a powerful tool for advancing the understanding of biological material behavior, with broad implications for biomaterials design, tissue engineering, and orthopedic research.