Imaging and analysis of a three-dimensional spider web architecture

A self-adapting woven net trap based on the evolution mechanism of orb-web topology

The development of structures that can adapt spontaneously to achieve desired functions in complex environments is crucial for new unmanned countermeasures, such as prey capture or net-recovery. Conventional structural optimization methods based on a singular net-like configuration may lead to functional limitations and fail to achieve specific objectives. In this study, we utilized an evolutionary algorithm that incorporated mechanical features and biological corrections to construct spider threads with advanced properties capable of efficient and reliable trapping behavior in arbitrary boundary conditions. We employed distinct thread types in different components, which achieved distinguished stiffness and strength that could not be accomplished by a single kind of thread. By assembling prestress reinforcement threads, we developed an orb-web-like trap that demonstrated effective trapping performance in experiments. The adaptive evolutionary method could be applied to design intelligent intercepting devices suited to particular functions and extreme environments, with wide application prospects in net-recovery system of UAV. STATEMENT OF SIGNIFICANCE: Structures that adapt spontaneously to perform desired functions in difficult environments are crucial for rising unmanned countermeasures. Conventional structural optimization methods based on a singular net-like configuration may lead to functional limitations and fail to achieve specific objectives. We used an evolutionary algorithm that combined mechanical features and biological corrections to create spider threads in arbitrary boundary circumstances in this work. The adaptive evolutionary method could be applied to design intelligent intercepting devices suited to particular functions and extreme environments, with wide application prospects in net-recovery system of UAV.

Biomimetic Silk Architectures Outperform Animal Horns in Strength and Toughness

Structural biomimicry is an intelligent approach for developing lightweight, strong, and tough materials (LSTMs). Current fabrication technologies, such as 3D printing and two-photon lithography often face challenges in constructing complex interlaced structures, such as the sinusoidal crossed herringbone structure that contributes to the ultrahigh strength and fracture toughness of the dactyl club of peacock mantis shrimps. Herein, bioinspired LSTMs with laminated or herringbone structures is reported, by combining textile processing and silk fiber "welding" techniques. The resulting biomimetic silk LSTMs (BS-LSTMs) exhibit a remarkable combination of lightweight with a density of 0.6-0.9 g cm-3 , while also being 1.5 times stronger and 16 times more durable than animal horns. These findings demonstrate that BS-LSTMs are among the toughest natural materials made from silk proteins. Finite element simulations further reveal that the fortification and hardening of BS-LSTMs arise primarily from the hierarchical organization of silk fibers and mechanically transferable meso-interfaces. This study highlights the rational, cost-effective, controllable mesostructure, and transferable strategy of integrating textile processing and fiber "welding" techniques for the fabrication of BS-LSTMs with advantageous structural and mechanical properties. These findings have significant implications for a wide range of applications in biomedicine, mechanical engineering, intelligent textiles, aerospace industries, and beyond.

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.

Methods for Silk Property Analyses across Structural Hierarchies and Scales

Silk from silkworms and spiders is an exceptionally important natural material, inspiring a range of new products and applications due to its high strength, elasticity, and toughness at low density, as well as its unique conductive and optical properties. Transgenic and recombinant technologies offer great promise for the scaled-up production of new silkworm- and spider-silk-inspired fibres. However, despite considerable effort, producing an artificial silk that recaptures the physico-chemical properties of naturally spun silk has thus far proven elusive. The mechanical, biochemical, and other properties of pre-and post-development fibres accordingly should be determined across scales and structural hierarchies whenever feasible. We have herein reviewed and made recommendations on some of those practices for measuring the bulk fibre properties; skin-core structures; and the primary, secondary, and tertiary structures of silk proteins and the properties of dopes and their proteins. We thereupon examine emerging methodologies and make assessments on how they might be utilized to realize the goal of developing high quality bio-inspired fibres.

High Performance Marine and Terrestrial Bioadhesives and the Biomedical Applications They Have Inspired

This study has reviewed the naturally occurring bioadhesives produced in marine and freshwater aqueous environments and in the mucinous exudates of some terrestrial animals which have remarkable properties providing adhesion under difficult environmental conditions. These bioadhesives have inspired the development of medical bioadhesives with impressive properties that provide an effective alternative to suturing surgical wounds improving closure and healing of wounds in technically demanding tissues such as the heart, lung and soft tissues like the brain and intestinal mucosa. The Gecko has developed a dry-adhesive system of exceptional performance and has inspired the development of new generation re-usable tapes applicable to many medical procedures. The silk of spider webs has been equally inspiring to structural engineers and materials scientists and has revealed innovative properties which have led to new generation technologies in photonics, phononics and micro-electronics in the development of wearable biosensors. Man made products designed to emulate the performance of these natural bioadhesive molecules are improving wound closure and healing of problematic lesions such as diabetic foot ulcers which are notoriously painful and have also found application in many other areas in biomedicine. Armed with information on the mechanistic properties of these impressive biomolecules major advances are expected in biomedicine, micro-electronics, photonics, materials science, artificial intelligence and robotics technology.

Design, manufacture, and testing of customized sterilizable respirator

The respirator as one of the personal protective equipment is essential for industrial activities (e.g., mining, painting, woodcutting, manufacturing) for protection from contaminants in the air and during the Covid-19 pandemic to protect the wearer from infection. The respirators nowadays are commonly made of rigid plastic. They are expensive, cumbersome, and not comfortable to wear. The many components with complex structures prevent it from cleaning and reusing. We develop a practical and scalable strategy to create customized respirators with durability using computational modeling and 3D printing. It is shown that by morphing the shape according to the user's photo, the respirator is designed to fit a user's face without air leaks. Using a printing-mold-casting method, this respirator can be manufactured by silicone rubber with accuracy, which is highly durable, with its mechanics primarily not affected by sterilization. These features provide the current respirator adaptivity and convenience in carrying and storing, as well as more comfort for long-time wearing.

In situ three-dimensional spider web construction and mechanics

Spiders are nature's engineers that build lightweight and high-performance web architectures often several times their size and with very few supports; however, little is known about web mechanics and geometries throughout construction, especially for three-dimensional (3D) spider webs. In this work, we investigate the structure and mechanics for a Tidarren sisyphoides spider web at varying stages of construction. This is accomplished by imaging, modeling, and simulations throughout the web-building process to capture changes in the natural web geometry and the mechanical properties. We show that the foundation of the web geometry, strength, and functionality is created during the first 2 d of construction, after which the spider reinforces the existing network with limited expansion of the structure within the frame. A better understanding of the biological and mechanical performance of the 3D spider web under construction could inspire sustainable robust and resilient fiber networks, complex materials, structures, scaffolding, and self-assembly strategies for hierarchical structures and inspire additive manufacturing methods such as 3D printing as well as inspire artistic and architectural and engineering applications.

Understanding Plant Biomass via Computational Modeling

Plant biomass, especially wood, has been used for structural materials since ancient times. It is also showing great potential for new structural materials and it is the major feedstock for the emerging biorefineries for building a sustainable society. The plant cell wall is a hierarchical matrix of mainly cellulose, hemicellulose, and lignin. Herein, the structure, properties, and reactions of cellulose, lignin, and wood cell walls, studied using density functional theory (DFT) and molecular dynamics (MD), which are the widely used computational modeling approaches, are reviewed. Computational modeling, which has played a crucial role in understanding the structure and properties of plant biomass and its nanomaterials, may serve a leading role on developing new hierarchical materials from biomass in the future.

Studies on the Geometrical Design of Spider Webs for Reinforced Composite Structures

Spider silk is an astonishingly tough biomaterial that consists almost entirely of large proteins. Studying the secrets behind the high strength nature of spider webs is very challenging due to their miniature size. In spite of their complex nature, researchers have always been inspired to mimic Nature for developing new products or enhancing the performance of existing technologies. Accordingly, the spider web can be taken as a model for optimal fiber orientation for composite materials to be used in critical structural applications. In this study an attempt is made to analyze the geometrical characteristics of the web construction building units such as spirals and radials. As a measurement tool, we have used a developed MATLAB algorithm code for measuring the node to node of rings and radials angle of orientation. Spider web image samples were collected randomly from an ecological niche with black background sample collection tools. The study shows that the radial angle of orientation is 12.7 degrees with 5 mm distance for the spirals’ mesh size. The extracted geometrical numeric values from the spider web show moderately skewed statistical data. The study sheds light on spider web utilization to develop an optimized fiber orientation reinforced composite structure for constructing, for instance, shell structures, pressure vessels and fuselage cones for the aviation industry.

Fractal Web Design of a Hemispherical Photodetector Array with Organic-Dye-Sensitized Graphene Hybrid Composites

The vision system of arthropods consists of a dense array of individual photodetecting elements across a curvilinear surface. This compound-eye architecture could be a useful model for optoelectronic sensing devices that require a large field of view and high sensitivity to motion. Strategies that aim to mimic the compound-eye architecture involve integrating photodetector pixels with a curved microlens, but their fabrication on a curvilinear surface is challenged by the use of standard microfabrication processes that are traditionally designed for planar, rigid substrates (e.g., Si wafers). Here, a fractal web design of a hemispherical photodetector array that contains an organic-dye-sensitized graphene hybrid composite is reported to serve as an effective photoactive component with enhanced light-absorbing capabilities. The device is first fabricated on a planar Si wafer at the microscale and then transferred to transparent hemispherical domes with different curvatures in a deterministic manner. The unique structural property of the fractal web design provides protection of the device from damage by effectively tolerating various external loads. Comprehensive experimental and computational studies reveal the essential design features and optoelectronic properties of the device, followed by the evaluation of its utility in the measurement of both the direction and intensity of incident light.

3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning

The latest advances in technical characteristics of unmanned aerial systems (UAS) and their onboard sensors opened the way for smart flying vehicles exploiting new application areas and allowing to perform missions seemed to be impossible before. One of these complicated tasks is the 3D reconstruction and monitoring of large-size, complex, grid-like structures as radio or television towers. Although image-based 3D survey contains a lot of visual and geometrical information useful for making preliminary conclusions on construction health, standard photogrammetric processing fails to perform dense and robust 3D reconstruction of complex large-size mesh structures. The main problem of such objects is repeated and self-occlusive similar elements resulting in false feature matching. This paper presents a method developed for an accurate Multi-View Stereo (MVS) dense 3D reconstruction of the Shukhov Radio Tower in Moscow (Russia) based on UAS photogrammetric survey. A key element for the successful image-based 3D reconstruction is the developed WireNetV2 neural network model for robust automatic semantic segmentation of wire structures. The proposed neural network provides high matching quality due to an accurate masking of the tower elements. The main contributions of the paper are: (1) a deep learning WireNetV2 convolutional neural network model that outperforms the state-of-the-art results of semantic segmentation on a dataset containing images of grid structures of complicated topology with repeated elements, holes, self-occlusions, thus providing robust grid structure masking and, as a result, accurate 3D reconstruction, (2) an advanced image-based pipeline aided by a neural network for the accurate 3D reconstruction of the large-size and complex grid structured, evaluated on UAS imagery of Shukhov radio tower in Moscow.

Wave Propagation and Energy Dissipation in Collagen Molecules

Collagen is the key protein of connective tissue (i.e., skin, tendons and ligaments, and cartilage, among others), accounting for 25-35% of the whole-body protein content and conferring mechanical stability. This protein is also a fundamental building block of bone because of its excellent mechanical properties together with carbonated hydroxyapatite minerals. Although the mechanical resilience and viscoelasticity have been studied both in vitro and in vivo from the molecular to tissue level, wave propagation properties and energy dissipation have not yet been deeply explored, in spite of being crucial to understanding the vibration dynamics of collagenous structures (e.g., eardrum, cochlear membranes) upon impulsive loads. By using a bottom-up atomistic modeling approach, here we study a collagen peptide under two distinct impulsive displacement loads, including longitudinal and transversal inputs. Using a one-dimensional string model as a model system, we investigate the roles of hydration and load direction on wave propagation along the collagen peptide and the related energy dissipation. We find that wave transmission and energy-dissipation strongly depend on the loading direction. Also, the hydrated collagen peptide can dissipate five times more energy than dehydrated one. Our work suggests a distinct role of collagen in term of wave transmission of different tissues such as tendon and eardrum. This study can step toward understanding the mechanical behavior of collagen upon transient loads, impact loading and fatigue, and designing biomimetic and bioinspired materials to replace specific native tissues such as the tympanic membrane.

A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence

We report a self-consistent method to translate amino acid sequences into audible sound, use the representation in the musical space to train a neural network, and then apply it to generate protein designs using artificial intelligence (AI). The sonification method proposed here uses the normal mode vibrations of the amino acid building blocks of proteins to compute an audible representation of each of the 20 natural amino acids, which is fully defined by the overlay of its respective natural vibrations. The vibrational frequencies are transposed to the audible spectrum following the musical concept of transpositional equivalence, playing or writing music in a way that makes it sound higher or lower in pitch while retaining the relationships between tones or chords played. This transposition method ensures that the relative values of the vibrational frequencies within each amino acid and among different amino acids are retained. The characteristic frequency spectrum and sound associated with each of the amino acids represents a type of musical scale that consists of 20 tones, the "amino acid scale". To create a playable instrument, each tone associated with the amino acids is assigned to a specific key on a piano roll, which allows us to map the sequence of amino acids in proteins into a musical score. To reflect higher-order structural details of proteins, the volume and duration of the notes associated with each amino acid are defined by the secondary structure of proteins, computed using DSSP and thereby introducing musical rhythm. We then train a recurrent neural network based on a large set of musical scores generated by this sonification method and use AI to generate musical compositions, capturing the innate relationships between amino acid sequence and protein structure. We then translate the de novo musical data generated by AI into protein sequences, thereby obtaining de novo protein designs that feature specific design characteristics. We illustrate the approach in several examples that reflect the sonification of protein sequences, including multihour audible representations of natural proteins and protein-based musical compositions solely generated by AI. The approach proposed here may provide an avenue for understanding sequence patterns, variations, and mutations and offers an outreach mechanism to explain the significance of protein sequences. The method may also offer insight into protein folding and understanding the context of the amino acid sequence in defining the secondary and higher-order folded structure of proteins and could hence be used to detect the effects of mutations through sound.

Analysis of the vibrational and sound spectrum of over 100,000 protein structures and application in sonification

We report a high-throughput method that enables us to automatically compute the vibrational spectra of more than 100,000 proteins available in the Protein Data Bank to date, in a consistent manner. Using this new algorithm we report a comprehensive database of the normal mode frequencies of all known protein structures, which has not been available before. We then use the resulting frequency spectra of the proteins to generate audible sound by overlaying the molecular vibrations and translating them to the audible frequency range using the music theoretic concept of transpositional equivalence. The method, implemented as a Max audio device for use in a digital audio workstation (DAW), provides unparalleled insights into the rich vibrational signatures of protein structures, and offers a new way for creative expression by using it as a new type of musical instrument. This musical instrument is fully defined by the vibrational feature of almost all known protein structures, making it fundamentally different from all the traditional instruments that are limited by the material properties of a few types of conventional engineering materials, such as wood, metals or polymers.

Multiscale Modeling of Silk and Silk-Based Biomaterials-A Review

Silk embodies outstanding material properties and biologically relevant functions achieved through a delicate hierarchical structure. It can be used to create high-performance, multifunctional, and biocompatible materials through mild processes and careful rational material designs. To achieve this goal, computational modeling has proven to be a powerful platform to unravel the causes of the excellent mechanical properties of silk, to predict the properties of the biomaterials derived thereof, and to assist in devising new manufacturing strategies. Fine-scale modeling has been done mainly through all-atom and coarse-grained molecular dynamics simulations, which offer a bottom-up description of silk. In this work, a selection of relevant contributions of computational modeling is reviewed to understand the properties of natural silk, and to the design of silk-based materials, especially combined with experimental methods. Future research directions are also pointed out, including approaches such as 3D printing and machine learning, that may enable a high throughput design and manufacturing of silk-based biomaterials.