Doubling the Mechanical Properties of Spider Silk by C60 Supersonic Molecular Beam Epitaxy

Spider silk is one of the most fascinating natural materials, owing to its outstanding mechanical properties. In fact, it is able to combine usually self-excluding properties, like strength and toughness that synthetic fibers fail to replicate. Here, we report a method to further enhance the already excellent mechanical properties of spider's silk, producing nanocomposite fibers where the matrix of spider silk is reinforced with C60 molecules. These are deposited by Supersonic Molecular Beam Epitaxy (SuMBE) and are able to efficiently interact with silk, as evidenced by XPS analysis. As a consequence, upon proper adjustment of the fullerene kinetic energy, the treated fibers show improved strength, Young's modulus and toughness

A hierarchical lattice spring model to simulate the mechanics of 2-D materials-based composites

It is known that structural biological materials such as bone or dentin show unprecedented damage tolerance, toughness, and strength. The common feature of these materials is their hierarchical heterogeneous structure, which contributes to increased energy dissipation before failure occurring at different scale levels. These structural properties are the key to achieve superior nanocomposites. Here, we develop a numerical model in order to simulate the mechanisms involved in damage progression and energy dissipation at different size scales in composites, which depend both on the heterogeneity of the material (defects or reinforcements) and on the type of hierarchical structure. Both these aspects have been incorporated into a 2-D model based on a lattice spring model approach, accounting for geometrical non-linearities and including statistically based fracture phenomena. The model has been validated by comparing numerical results to linear elastic fracture mechanics results as well as to finite elements simulations, and then employed to study how hierarchical structural aspects impact on composite material properties, which is the main novel feature of the approach. Results obtained with the numerical code highlight the dependence of stress distributions (and therefore crack propagation) on matrix properties and reinforcement dispersion, geometry, and properties, and how the redistribution of stresses after the failure of sacrificial elements is directly involved in the damage tolerance of the materia

Hierarchical physically based machine learning in material science: the case study of spider silk

Multiscale phenomena exhibit complex structure-function relationships, and predicting their macroscopic behavior requires deducing differential equations at different scales. The complexity of these equations and the number of essential parameters make developing effective, predictive models challenging. To overcome this, researchers explore leveraging advanced numerical techniques from artificial intelligence and machine learning. Here, we focus on a fundamental aspect in multiscale phenomena, i.e the recognition of the hierarchical role of variables. By adopting a Pareto front interpretation, we aim to deduce simple and accurate relations for material modeling, starting from experimental multiscale analyses. From a physical point of view, the aim is to deduce information at higher scales from lower scales data, possibly respecting their hierarchical order. A crucial aspect of the proposed approach is the deduction of causality relations among the different variables to be compared with the available theoretical notions and possibly new interpretations resulting by the data modelling. This result in a stepwise approximation going from data modelling to theoretical equations and back to data modelling. To demonstrate the key advantages of our multiscale numerical approach, compared to classical, non-physically based data modelling techniques, we consider the explicit example of spider silk, known for its exceptional properties and bioinspiration potential. Indeed, it presents a complex behavior resulting from mesostructures formed by the aggregation of amino acids at the molecular scale. We argue that, due to the generality of our results, our approach may represent a proof of concept in many fields where multiscale, hierarchical differential equations regulate the observed phenomenon

Mechanical Properties of Twisted Carbon Nanotube Bundles with Carbon Linkers from Molecular Dynamics Simulations

The manufacturing of high-modulus, high-strength fibers is of paramount importance for real-world, high-end applications. In this respect, carbon nanotubes represent the ideal candidates for realizing such fibers. However, their remarkable mechanical performance is difficult to bring up to the macroscale, due to the low load transfer within the fiber. A strategy to increase such load transfer is the introduction of chemical linkers connecting the units, which can be obtained, for example, using carbon ion-beam irradiation. In this work, we investigate, via molecular dynamics simulations, the mechanical properties of twisted nanotube bundles in which the linkers are composed of interstitial single carbon atoms. We find a significant interplay between the twist and the percentage of linkers. Finally, we evaluate the suitability of two different force fields for the description of these systems: the dihedral-angle-corrected registry-dependent potential, which we couple for non-bonded interaction with either the AIREBO potential or the screened potential ReboScr2. We show that both of these potentials show some shortcomings in the investigation of the mechanical properties of bundles with carbon linkers

Silk reinforced with graphene or carbon nanotubes spun by spiders

Here, we report the production of silk incorporating graphene and carbon nanotubes directly by spider spinning, after spraying spiders with the corresponding aqueous dispersions. We observe a significant increment of the mechanical properties with respect to the pristine silk, in terms of fracture strength, Young's and toughness moduli. We measure a fracture strength up to 5.4 GPa, a Young's modulus up to 47.8 GPa and a toughness modulus up to 2.1 GPa, or 1567 J/g, which, to the best of our knowledge, is the highest reported to date, even when compared to the current toughest knotted fibres. This approach could be extended to other animals and plants and could lead to a new class of bionic materials for ultimate applications

Vibrational calling signals improve the efficacy of pheromone traps to capture the brown marmorated stink bug

Halyomorpha halys (Stål, 1855), the brown marmorated stink bug (BMSB), is an invasive species that has become a key agricultural pest in its invaded range. Commercial traps available for BMSB monitoring rely on male produced aggregation pheromones as lure, with two possible shortcomings: trap spillover and low detection precision. In this study, we assessed if vibrational signals can increase the attractiveness of pheromone traps by testing the optimized vibration-based lure (Female Song 2, FS2) associated with a specifically designed trap (i.e., the vibrotrap). We evaluated the efficacy of this bimodal trap (i.e., pheromones + vibrations) on females, males and nymphs in controlled conditions (greenhouse) and in the field, in two sites at the margin of two commercial vineyards. In the field, bimodal vibrotraps were compared to three unimodal (i.e., only pheromone) trap types. Both experiments showed that the vibrotrap is highly attractive for BMSB, and the optimized FS2 signal significantly improved its effectiveness. Even though FS2 was selected to target males, the number of trapped females increased as well. Overall, the presented findings show a feasible improvement to future commercial BMSB traps through the synergic use of semiophysicals and semiochemicals. Further research is needed to evaluate the effectiveness of vibrotraps for both early detection and mass trapping

A soft robot structure with limbless resonant, stick and slip locomotion

We present a smart robot structure that exploits anisotropic friction to achieve stick-slip locomotion. The robot is made out of three components: a plastic beam, a planar dielectric elastomer actuator and four bristle pads with asymmetric rigid metallic bristles. We show that when the robot is electronically activated at increasing frequency, its structure exploits the resonance condition to reach the maximum locomotion speed. The fundamental frequency of the structure is estimated both analytically and numerically, allowing the range of frequencies in which the top locomotion speed was observed during the experiments to be identified. The locomotion speed of the robot as a function of the actuation frequency is estimated with a frequency response analysis performed on a discretised model of the structure, revealing good agreement with the experimental evidence