Mechanical metamaterials offer exotic properties based on local control of cell geometry and their global configuration into structures and mechanisms. Historically, these have been made as continuous, monolithic structures with additive manufacturing, which affords high resolution and throughput, but is inherently limited by process and machine constraints. To address this issue, we present a construction system for mechanical metamaterials based on discrete assembly of a finite set of parts, which can be spatially composed for a range of properties such as rigidity, compliance, chirality, and auxetic behavior. This system achieves desired continuum properties through design of the parts such that global behavior is governed by local mechanisms. We describe the design methodology, production process, numerical modeling, and experimental characterization of metamaterial behaviors. This approach benefits from incremental assembly, which eliminates scale limitations, best-practice manufacturing for reliable, low-cost part production, and interchangeability through a consistent assembly process across part types.

The direct electrostatic printing of highly viscous thermoplastic polymers onto movable collectors, a process known as melt electrospinning writing (MEW), has significant potential as an additive biomanufacturing (ABM) technology. MEW has the hitherto unrealized potential of fabricating three-dimensional (3D) porous interconnected fibrous mesh-patterned scaffolds in conjunction with cellular-relevant fiber diameters and interfiber distances without the use of cytotoxic organic solvents. However, this potential cannot be readily fulfilled owing to the large number and complex interplay of the multivariate independent parameters of the melt electrospinning process. To overcome this manufacturing challenge, dimensional analysis is employed to formulate a “Printability Number” (NPR), which correlates with the dimensionless numbers arising from the nondimensionalization of the governing conservation equations of the electrospinning process and the viscoelasticity of the polymer melt. This analysis suggests that the applied voltage potential (Vp), the volumetric flow rate (Q), and the translational stage speed (UT) are the most critical parameters toward efficient printability. Experimental investigations using a poly(ε-caprolactone) (PCL) melt reveal that any perturbations arising from an imbalance between the downstream pulling forces and the upstream resistive forces can be eliminated by systematically tuning Vp and Q for prescribed thermal conditions. This, in concert with appropriate tuning of the translational stage speed, enables steady-state equilibrium conditions to be achieved for the printing of microfibrous woven meshes with precise and reproducible geometries.

Tuning cell shape by altering the biophysical properties of biomaterial substrates on which cells operate would provide a potential shape-driven pathway to control cell phenotype. However, there is an unexplored dimensional scale window of three-dimensional (3D) substrates with precisely tunable porous microarchitectures and geometrical feature sizes at the cell’s operating length scales (10–100 μm). This paper demonstrates the fabrication of such high-fidelity fibrous substrates using a melt electrowriting (MEW) technique. This advanced manufacturing approach is biologically qualified with a metrology framework that models and classifies cell confinement states under various substrate dimensionalities and architectures. Using fibroblasts as a model cell system, the mechanosensing response of adherent cells is investigated as a function of variable substrate dimensionality (2D vs. 3D) and porous microarchitecture (randomly oriented, “non-woven” vs. precision-stacked, “woven”). Single-cell confinement states are modeled using confocal fluorescence microscopy in conjunction with an automated single-cell bioimage data analysis workflow that extracts quantitative metrics of the whole cell and sub-cellular focal adhesion protein features measured. The extracted multidimensional dataset is employed to train a machine learning algorithm to classify cell shape phenotypes. The results show that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures with the woven MEW substrates promoting the highest cell shape homogeneity compared to non-woven fibrous substrates. The technology platform established here constitutes a significant step towards the development of integrated additive manufacturing—metrology platforms for a wide range of applications including fundamental mechanobiology studies and 3D bioprinting of tissue constructs to yield specific biological designs qualified at the single-cell level.