Self-organized biotectonics of termite nests

Design of nondeterministic architected structures via bioinspired distributed agents

Nature manufactures structures via decentralized processes involving groups of agents. This is fundamentally different from traditional manufacturing, where objects are produced via sequences of predefined steps. In this work, we explore the idea of using simulated "swarms" of simple agents to generate new designs for architected materials in a decentralized, bioinspired manner. Individual agents choose their own actions based solely on information in their immediate environment, with no centralized control. The structures that these processes produce are the result of the collective action of the individual agents, rather than a predetermined design. We build an integrated platform for determining "rule-structure-property" relationships, analogous to process-structure-property relationships in materials science. The platform simulates agent behaviors to show how different rules and different environments result in different structures. We then three-dimensional print these and perform finite element analysis to experimentally and numerically characterize mechanical properties, including tensile strength and energy dissipation.

Enhanced elastic stability of a topologically disordered crystalline metal-organic framework

By virtue of their open network structures and low densities, metal-organic frameworks (MOFs) are soft materials that exhibit elastic instabilities at low applied stresses. The conventional strategy for improving elastic stability is to increase the connectivity of the underlying MOF network, which necessarily increases the material density and reduces the porosity. Here we demonstrate an alternative paradigm, whereby elastic stability is enhanced in a MOF with an aperiodic network topology. We use a combination of variable-pressure single-crystal X-ray diffraction measurements and coarse-grained lattice-dynamical calculations to interrogate the high-pressure behaviour of the topologically aperiodic system TRUMOF-1, which we compare against that of its ordered congener MOF-5. We show that the topology of the former quenches the elastic instability responsible for pressure-induced framework collapse in the latter, much as irregularity in the shapes and sizes of stones acts to prevent cooperative mechanical failure in drystone walls. Our results establish aperiodicity as a counter-intuitive design motif in engineering the mechanical properties of framework structures that is relevant to MOFs and larger-scale architectures alike.

Substrate evaporation drives collective construction in termites

Termites build complex nests which are an impressive example of self-organization. We know that the coordinated actions involved in the construction of these nests by multiple individuals are primarily mediated by signals and cues embedded in the structure of the nest itself. However, to date there is still no scientific consensus about the nature of the stimuli that guide termite construction, and how they are sensed by termites. In order to address these questions, we studied the early building behavior of Coptotermes gestroi termites in artificial arenas, decorated with topographic cues to stimulate construction. Pellet collections were evenly distributed across the experimental setup, compatible with a collection mechanism that is not affected by local topography, but only by the distribution of termite occupancy (termites pick pellets at the positions where they are). Conversely, pellet depositions were concentrated at locations of high surface curvature and at the boundaries between different types of substrate. The single feature shared by all pellet deposition regions was that they correspond to local maxima in the evaporation flux. We can show analytically and we confirm experimentally that evaporation flux is directly proportional to the local curvature of nest surfaces. Taken together, our results indicate that surface curvature is sufficient to organize termite building activity and that termites likely sense curvature indirectly through substrate evaporation. Our findings reconcile the apparently discordant results of previous studies.

Feature-preserving synthesis of termite-mimetic spinodal nest morphology

Termite-built topology is complex due to group interactions and environmental feedback. Being interlinked with material characteristics and related to functionality, an accurate synthesis of termite mound topology has never been achieved. We scanned inner termite mound pieces via high-resolution micro-computed tomography. A wavelet scattering transform followed by optimization extracts features that are fed into a Gaussian Random Fields (GRFs) approach to synthesize termite-mimetic spinodal topology. Compared to natural structures the GRF topology is more regular. Irregularity is related to anisotropy, indicative of directionality caused by porous network connectivity of chambers and corridors. Since GRFs are related to diffusion, we assume that deterministic behavioral traits play a significant role in the development of these local differences. We pioneer a framework to reliably mimic termite mound spinodal features. Engineering termite-inspired structures will allow to inspect aspects of termite architectures and their behavior to manufacture novel material concepts with imprinted multi-functionality.

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.

Dynamic pathogen detection and social feedback shape collective hygiene in ants

Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense.

Manipulating nest architecture reveals three-dimensional building strategies and colony resilience in honeybees

Form follows function throughout the development of an organism. This principle should apply beyond the organism to the nests they build, but empirical studies are lacking. Honeybees provide a uniquely suited system to study nest form and function throughout development because we can image the three-dimensional structure repeatedly and non-destructively. Here, we tracked nest-wide comb growth in six colonies over 45 days (control colonies) and found that colonies have a stereotypical process of development that maintains a spheroid nest shape. To experimentally test if nest structure is important for colony function, we shuffled the nests of an additional six colonies, weekly rearranging the comb positions and orientations (shuffled colonies). Surprisingly, we found no differences between control and shuffled colonies in multiple colony performance metrics-worker population, comb area, hive weight and nest temperature. However, using predictive modelling to examine how workers allocate comb to expand their nests, we show that shuffled colonies compensate for these disruptions by accounting for the three-dimensional structure to reconnect their nest. This suggests that nest architecture is more flexible than previously thought, and that superorganisms have mechanisms to compensate for drastic architectural perturbations and maintain colony function.

Growth rules for irregular architected materials with programmable properties

Biomaterials display microstructures that are geometrically irregular and functionally efficient. Understanding the role of irregularity in determining material properties offers a new path to engineer materials with superior functionalities, such as imperfection insensitivity, enhanced impact absorption, and stress redirection. We uncover fundamental, probabilistic structure-property relationships using a growth-inspired program that evokes the formation of stochastic architectures in natural systems. This virtual growth program imposes a set of local rules on a limited number of basic elements. It generates materials that exhibit a large variation in functional properties starting from very limited initial resources, which echoes the diversity of biological systems. We identify basic rules to control mechanical properties by independently varying the microstructure's topology and geometry in a general, graph-based representation of irregular materials.

Submillimetre mechanistic designs of termite-built structures

Termites inhabit complex underground mounds of intricate stigmergic labyrinthine designs with multiple functions as nursery, food storage and refuge, while maintaining a homeostatic microclimate. Past research studied termite building activities rather than the actual material structure. Yet, prior to understanding how multi-functionality shaped termite building, a thorough grasp of submillimetre mechanistic architecture of mounds is required. Here, we identify for Nasutitermes exitiosus via granulometry and Fourier transform infrared spectroscopy analysis, preferential particle sizes related to coarse silts and unknown mixtures of organic/inorganic components. High-resolution micro-computed X-ray tomography and microindentation tests reveal wall patterns of filigree laminated layers and sub-millimetre porosity wrapped around a coarse-grained inner scaffold. The scaffold geometry, which is designed of a lignin-based composite and densely biocementitious stercoral mortar, resembles that of trabecula cancellous bones. Fractal dimension estimates indicate multi-scaled porosity, important for enhanced evaporative cooling and structural stability. The indentation moduli increase from the outer to the inner wall parts to values higher than those found in loose clays and which exceed locally the properties of anthropogenic cementitious materials. Termites engineer intricately layered biocementitious composites of high elasticity. The multiple-scales and porosity of the structure indicate a potential to pioneer bio-architected lightweight and high-strength materials.