Crosslinker energy landscape effects on dynamic mechanical properties of ideal polymer hydrogels.

Reversible crosslinkers can enable several desirable mechanical properties, such as improved toughness and self-healing, when incorporated in polymer networks for bioengineering and structural applications. In this work, we performed coarse-grained molecular dynamics to investigate the effect of the energy landscape of reversible crosslinkers on the dynamic mechanical properties of crosslinked polymer network hydrogels. We report that, for an ideal network, the energy potential of the crosslinker interaction drives the viscosity of the network, where a stronger potential results in a higher viscosity. Additional topographical analyses reveal a mechanistic understanding of the structural rearrangement of the network as it deforms and indicate that as the number of defects increases in the network, the viscosity of the network increases. As an important validation for the relationship between the energy landscape of a crosslinker chemistry and the resulting dynamic mechanical properties of a crosslinked ideal network hydrogel, this work enhances our understanding of deformation mechanisms in polymer networks that cannot easily be revealed by experiment and reveals design ideas that can lead to better performance of the polymer network at the macroscale.

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing.

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here, we provide a detailed analysis of the heterogeneous graph structures of spider webs and use deep learning as a way to model and then synthesize artificial, bioinspired 3D web structures. The generative models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) an analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation; 2) a discrete diffusion model with full neighbor representation; and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bioinspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose an algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles toward integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Computational methods for the characterization of Apis mellifera comb architecture.

The architecture of honey bee combs embodies a range of expressions associated with swarm intelligence, emergent behaviors, and social organization, which has drawn scientists to study them as a model of collective construction processes. Until recently, however, the development of models to characterize comb-building behavior has relied heavily on laborious manual observations and measurements. The use of high-throughput multi-scale analyses to investigate the geometric features of Apis mellifera comb therefore has the potential to vastly expand our understanding of comb-building processes. Inspired by this potential, here we explore connections between geometry and behavior by utilizing computational methods for the detailed examination of hives constructed within environments designed to observe how natural building rule sets respond to environmental perturbations. Using combs reconstructed from X-ray micro-computed tomography source data, we introduce a set of tools to analyze geometry and material distributions from these scans, spanning from individual cells to whole-hive-level length scales. Our results reveal relationships between cell geometry and comb morphology, enable the generalization of prior research on build direction, demonstrate the viability of our methods for isolating specific features of comb architecture, and illustrate how these results may be employed to investigate hive-level behaviors related to build-order and material distributions.

Sequential Multimaterial Additive Manufacturing of Functionally Graded Biopolymer Composites.

Cellulose, chitin, and pectin are three of the most abundant natural materials on Earth. Despite this, large-scale additive manufacturing with these biopolymers is used only in limited applications and frequently relies on extensive refinement processes or plastic additives. We present novel developments in a digital fabrication and design approach for multimaterial three-dimensional printing of biopolymers. Specifically, our computational and digital fabrication workflow-sequential multimaterial additive manufacturing-enables the construction of biopolymer composites with continuously graded transitional zones using only a single extruder. We apply this method to fabricate structures on length scales ranging from millimeters to meters. Transitional regions between materials created using these methods demonstrated comparable mechanical properties with homogenous mixtures of the same composition. We present a computational workflow and physical system support a novel and flexible form of multimaterial additive manufacturing with a diverse array of potential applications.