In situ three-dimensional spider web construction and mechanics

Bionic Spider Web Flexible Strain Sensor Based on CF-L and Machine Learning

At present, the preparation of laser-induced graphene (LIG) has become an important technology in sensor manufacturing. In the conventional preparation process, the CO2 laser is widely used; however, its experimental period is long and its efficiency needs to be improved. We propose an innovative strategy to improve the experimental efficiency. We use the machine learning method to accurately predict the preparation parameters of LIG, so as to optimize the experimental process. Different structures can lead to different sensor performances. The structure constructed by the CO2 laser is rough and has a large size, which can affect the performance of the sensor. Therefore, we propose for the first time an innovative method for intramembrane structure construction that combines the advantages of the CO2 laser and fiber laser (CF-L). With this CF-L method, we have successfully prepared a biomimetic, flexible strain sensor. This sensor not only maintains a high degree of sensitivity, but also has a more refined and optimized structure. The manufacturing process of the whole sensor is simple, economical, and durable and can be prepared in large quantities and can be used to detect the extension and bending of human joints.

3D Printing of Ionogels with Complementary Functionalities Enabled by Self-Regulating Ink

Shaping soft and conductive materials into sophisticated architectures through 3D printing is driving innovation in myriad applications, such as robotic counterparts that emulate the synergic functions of biological systems. Although recently developed multi-material 3D printing has enabled on-demand creation of intricate artificial counterparts from a wide range of functional viscoelastic materials. However, directly achieving complementary functionalities in one ink design remains largely unexplored, given the issues of printability and synergy among ink components. In this study, an easily accessible and self-regulating tricomponent ionogel-based ink design to address these challenges is reported. The resultant 3D printed objects, based on the same component but with varying ratios of ink formulations, exhibit distinct yet complementary properties. For example, their Young's modulus can differ by three orders of magnitude, and some structures are rigid while others are ductile and viscous. A theoretical model is also employed for predicting and controlling the printing resolution. By integrating complementary functionalities, one further demonstrates a representative bioinspired prototype of spiderweb, which mimics the sophisticated structure and multiple functions of a natural spiderweb, even working and camouflaging underwater. This ink design strategy greatly extends the material choice and can provide valuable guidance in constructing diverse artificial systems by 3D printing.

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.

New Method for Rapid 3D Reconstruction of Semi-Transparent Underwater Animals and Structures

Morphological features are the primary identifying properties of most animals and key to many comparative physiological studies, yet current techniques for preservation and documentation of soft-bodied marine animals are limited in terms of quality and accessibility. Digital records can complement physical specimens, with a wide array of applications ranging from species description to kinematics modeling, but options are lacking for creating models of soft-bodied semi-transparent underwater animals. We developed a lab-based technique that can live-scan semi-transparent, submerged animals, and objects within seconds. To demonstrate the method, we generated full three-dimensional reconstructions (3DRs) of an object of known dimensions for verification, as well as two live marine animals-a siphonophore and an amphipod-allowing detailed measurements on each. Techniques like these pave the way for faster data capture, integrative and comparative quantitative approaches, and more accessible collections of fragile and rare biological samples.