Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.
Authors
Affiliations (6)
Affiliations (6)
- Department of Thoracic Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, People's Republic of China.
- Dpartment of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China.
- Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad, Pakistan.
- Department of Emergency Medicine, Shengjing Hospital of China Medical University, Tiexi District, No. 39 Huaxiang Road, Shenyang, Liaoning, 110000, People's Republic of China.
- Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, Liaoning, 110001, People's Republic of China.
Abstract
Composite biomaterials are central to biomedical engineering, where implants and scaffolds must simultaneously meet mechanical, biological, and functional demands across length scales. This review outlines a data-driven multiscale design paradigm that unites experiments, three-dimensional imaging, and computational modelling. We present hierarchical architectures in natural tissues, such as bone, and their implications for stiffness, toughness, and damage tolerance, which inspire the design of synthetic composites. Then we discuss multiscale mechanical and physicochemical characterization, including nanoindentation, bulk mechanical tests, dynamic mechanical analysis (DMA), rheology, and <i>in situ</i> X-ray micro-computed tomography that resolves internal damage and pore networks under load. These datasets feed image-based finite element models, continuum and mesoscale frameworks, and multiscale simulations that link local microstructure to macroscopic performance in terms of mechanics, mass transport, and degradation. Next, we discuss emerging machine learning (ML) approaches that learn structure-property relationships from images and high-throughput experiments, enabling surrogate models, inverse design, and generative microstructure design. Case studies in orthopaedic, cardiovascular, soft tissue, regenerative medicine, and drug-delivery applications illustrate how integrated experiment-model pipelines accelerate the optimization of scaffold architecture, composition, and release profiles. Finally, we highlight future directions toward digital twins of implants and virtual patients that couple patient-specific imaging with adaptive models to monitor performance and personalize therapies, as well as key challenges in data quality, model interpretability, and bridging biological time scales. Together, these advances point to a shift from empirical trial-and-error toward predictive, data-driven engineering of composite biomaterials tailored to complex clinical constraints.