Crumpling-based soft metamaterials: The effects of sheet pore size and porosity

Crumpled-based materials are relatively easy to fabricate and show robust mechanical properties for practical applications, including meta-biomaterials design aimed for improved tissue regeneration. For such requests, however, the structure needs to be porous. We introduce a crumpled holey thin sheet as a robust bio-metamaterial and measure the mechanical response of a crumpled holey thin Mylar sheet as a function of the hole size and hole area fraction. We also study the formation of patterns of crease lines and ridges. The area fraction largely dominated the crumpling mechanism. We also show, the crumpling exponents slightly increases with increasing the hole area fraction and the total perimeter of the holes. Finally, hole edges were found to limit and guide the propagation of crease lines and ridges

Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials

Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of multiple materials within those geometries. Varying the spatial distribution of multiple materials gives rise to many interesting and potentially unique combinations of anisotropic elastic properties. While the availability of a design approach to cover a large portion of all possible combinations of elastic properties is interesting in itself, it is even more important to find the extremely rare designs that lead to highly unusual combinations of material properties (e.g., double-auxeticity and high elastic moduli). Here, we used a random distribution of a hard phase and a soft phase within a regular lattice to study the resulting anisotropic mechanical properties of the network in general and the abovementioned rare designs in particular. The primary challenge to take up concerns the huge number of design parameters and the extreme rarity of such designs. We, therefore, used computational models and deep learning algorithms to create a mapping from the space of design parameters to the space of mechanical properties, thereby (i) reducing the computational time required for evaluating each designand (ii) making the process of evaluating the different designs highly parallelizable. Furthermore, we selected ten designs to be fabricated using polyjet multi-material 3D printing techniques, mechanically tested them, and characterized their behavior using digital image correlation (DIC, 3 designs) to validate the accuracy of our computational models. The results of our simulations show that deep learning-based algorithms can accurately predict the mechanical properties of the different designs, which match the various deformation mechanisms observed in the experiments.Comment: 28 pages, 4 figure

Spiral Honeycomb Microstructured Bacterial Cellulose for Increased Strength and Toughness.

Natural materials, such as nacre and silk, exhibit both high strength and toughness due to their hierarchical structures highly organized at the nano-, micro-, and macroscales. Bacterial cellulose (BC) presents a hierarchical fibril structure at the nanoscale. At the microscale, however, BC nanofibers are distributed randomly. Here, BC self-assembles into a highly organized spiral honeycomb microstructure giving rise to a high tensile strength (315 MPa) and a high toughness value (17.8 MJ m-3), with pull-out and de-spiral morphologies observed during failure. Both experiments and finite-element simulations indicate improved mechanical properties resulting from the honeycomb structure. The mild fabrication process consists of an in situ fermentation step utilizing poly(vinyl alcohol), followed by a post-treatment including freezing-thawing and boiling. This simple self-assembly production process is highly scalable, does not require any toxic chemicals, and enables the fabrication of light, strong, and tough hierarchical composite materials with tunable shape and size

Shape-matching soft mechanical metamaterials

Architectured materials with rationally designed geometries could be used to create mechanical metamaterials with unprecedented or rare properties and functionalities. Here, we introduce "shape-matching" metamaterials where the geometry of cellular structures comprising auxetic and conventional unit cells is designed so as to achieve a pre-defined shape upon deformation. We used computational models to forward-map the space of planar shapes to the space of geometrical designs. The validity of the underlying computational models was first demonstrated by comparing their predictions with experimental observations on specimens fabricated with indirect additive manufacturing. The forward-maps were then used to devise the geometry of cellular structures that approximate the arbitrary shapes described by random Fourier's series. Finally, we show that the presented metamaterials could match the contours of three real objects including a scapula model, a pumpkin, and a Delft Blue pottery piece. Shape-matching materials have potential applications in soft robotics and wearable (medical) devices

Synthetic Polymers Provide a Robust Substrate for Functional Neuron Culture

Substrates for neuron culture and implantation are required to be both biocompatible and display surface compositions that support cell attachment, growth, differentiation, and neural activity. Laminin, a naturally occurring extracellular matrix protein is the most widely used substrate for neuron culture and fulfills some of these requirements, however, it is expensive, unstable (compared to synthetic materials), and prone to batch-to-batch variation. This study uses a high-throughput polymer screening approach to identify synthetic polymers that supports the in vitro culture of primary mouse cerebellar neurons. This allows the identification of materials that enable primary cell attachment with high viability even under “serum-free” conditions, with materials that support both primary cells and neural progenitor cell attachment with high levels of neuronal biomarker expression, while promoting progenitor cell maturation to neurons.Biomaterials & Tissue Biomechanic

The response of human macrophages to 3D printed titanium antibacterial implants does not affect the osteogenic differentiation of hMSCs

Macrophage responses following the implantation of orthopaedic implants are essential for successful implant integration in the body, partly through intimate crosstalk with human marrow stromal cells (hMSCs) in the process of new bone formation. Additive manufacturing (AM) and plasma electrolytic oxidation (PEO) in the presence of silver nanoparticles (AgNPs) are promising techniques to achieve multifunctional titanium implants. Their osteoimmunomodulatory properties are, however, not yet fully investigated. Here, we studied the effects of implants with AgNPs on human macrophages and the crosstalk between hMSCs and human macrophages when co-cultured in vitro with biofunctionalised AM Ti6Al4V implants. A concentration of 0.3 g/L AgNPs in the PEO electrolyte was found to be optimal for both macrophage viability and inhibition of bacteria growth. These specimens also caused a decrease of the macrophage tissue repair related factor C-C Motif Chemokine Ligand 18 (CCL18). Nevertheless, co-cultured hMSCs could osteogenically differentiate without any adverse effects caused by the presence of macrophages that were previously exposed to the PEO (±AgNPs) surfaces. Further evaluation of these promising implants in a bony in vivo environment with and without infection is highly recommended to prove their potential for clinical use.</p

Rational design of soft mechanical metamaterials: Independent tailoring of elastic properties with randomness

The elastic properties of mechanical metamaterials are direct functions of their topological designs. Rational design approaches based on computational models could, therefore, be used to devise topological designs that result in the desired properties. It is of particular importance to independently tailor the elastic modulus and Poisson's ratio of metamaterials. Here, we present patterned randomness as a strategy for independent tailoring of both properties. Soft mechanical metamaterials incorporating various types of patterned randomness were fabricated using an indirect additive manufacturing technique and mechanically tested. Computational models were also developed to predict the topology-property relationship in a wide range of proposed topologies. The results of this study show that patterned randomness allows for independent tailoring of the elastic properties and covering a broad area of the elastic modulus-Poisson's ratio plane. The uniform and homogenous topologies constitute the boundaries of the covered area, while topological designs with patterned randomness fill the enclosed area