Amplification in toughness and balance with stiffness and strength are fundamental characteristics of biological structural composites, and a long sought-after objective for engineering design. Nature achieves these properties through a combination of multiscale key features. Yet, emulating all these features into synthetic de novo materials is rather challenging. Here, we fine-tune manual lamination, to implement a newly designed bone-inspired structure into fiber-reinforced composites. An integrated approach, combining numerical simulations, ad hoc manufacturing techniques, and testing, yields a novel composite with enhanced fracture toughness and balance with stiffness and strength, offering an optimal lightweight material solution with better performance than conventional materials such as metals and alloys. The results also show how the new design significantly boosts the fracture toughness compared to a classic laminated composite, made of the same building blocks, also offering an optimal tradeoff with stiffness and strength. The predominant mechanism, responsible for the enhancement of fracture toughness in the new material, is the continuous deviation of the crack from a straight path, promoting large energy dissipation and preventing a catastrophic failure. The new insights resulting from this study can guide the design of de novo fiber-reinforced composites toward better mechanical performance to reach the level of synergy of their natural counterparts.

Learning from Nature and leveraging 3D printing, mechanical testing, and numerical modeling, this study aims to provide a deeper understanding of the structure–property relationship of crystal-lattice-inspired materials, starting from the study of single unit cells inspired by the cubic Bravais crystal lattices. In particular, here we study the simple cubic (SC), body-centered cubic (BCC), and face-centered cubic (FCC) lattices. Mechanical testing of 3D-printed structures is used to investigate the influence of different printing parameters. Numerical models, validated based on experimental testing carried out on single unit cells and embedding manufacturing-induced defects, are used to derive the scaling laws for each studied topology, thus providing guidelines for materials selection and design, and the basis for future homogenization and optimization studies. We observe no clear effect of the layer thickness on the mechanical properties of both bulk material and lattice structures. Instead, the printing direction effect, negligible in solid samples, becomes relevant in lattice structures, yielding different stiffnesses of struts and nodes. This phenomenon is accounted for in the proposed simulation framework. The numerical models of large arrays, used to define the scaling laws, suggest that the chosen topologies have a mainly stretching-dominated behavior—a hallmark of structurally efficient structures—where the modulus scales linearly with the relative density. By looking ahead, mimicking the characteristic microscale structure of crystalline materials will allow replicating the typical behavior of crystals at a larger scale, combining the hardening traits of metallurgy with the characteristic behavior of polymers and the advantage of lightweight architected structures, leading to novel materials with multiple functions.

Intelligent materials and devices are considered the key to fulfill the strict and challenging requirements given by a large set of applications in numerous fields. The synergistic interplay of additive manufacturing (AM) technologies, specifically 4D‐printing, and artificial intelligence (AI), have widened the design space and accelerated the design phase. 4D‐printing, intended as the combination of 3D‐printing with a fourth dimension (i.e., time), ensures the fabrication of complex topologies with materials that can be selectively triggered to change specific features (e.g., shape, color), whereas the use of smart inks or embedded sensors can bring additional functionalities (e.g., repairing, sensing, actuating) to fulfill the requirements of the targeted application. AI is the tool allowing scientists to train machines for developing human‐like capabilities to discern, predict, and represent the statistically significant behavior of a phenomenon. If applied to the design of smart structures, it can be a powerful and efficient tool capable of reducing the time‐to‐manufacturing, improving the traditional design approach. In this perspective, a viewpoint is provided on how the synergistic use of AI and AM can advance the design of intelligent systems, with successful applications ranging from the robotics to the bioengineering sectors.

The world population is increasingly aging, deeply affecting our society by challenging our healthcare systems and presenting an economic burden, thus turning the spotlight on aging-related diseases: exempli gratia, osteoporosis, a silent disease until you suddenly break a bone. The increase in bone fracture risk with age is generally associated with a loss of bone mass and an alteration in the skeletal architecture. However, such changes cannot fully explain increased fragility with age. To successfully tackle age-related bone diseases, it is paramount to comprehensively understand the fundamental mechanisms responsible for tissue degeneration. Aging mechanisms persist at multiple length scales within the complex hierarchical bone structure, raising the need for a multiscale and multidisciplinary approach to resolve them. This paper aims to provide an overarching analysis of aging processes in bone and to review the most prominent outcomes of bone aging. A systematic description of different length scales, highlighting the corresponding techniques adopted at each scale and motivating the need for combining diverse techniques, is provided to get a comprehensive description of the multi-physics phenomena involved.