Unraveling the Mechanisms of the Apis mellifera Honeycomb Construction by 4D X-ray Microscopy

A Versatile Synthesis Platform Based on Polymer Cubosomes for a Library of Highly Ordered Nanoporous Metal Oxides Particles

Polymer cubosomes (PCs) have well-defined inverse bicontinuous cubic mesophases formed by amphiphilic block copolymer bilayers. The open hydrophilic channels, large periods, and robust physical properties of PCs are advantageous to many host-guest interactions and yet not fully exploited, especially in the fields of functional nanomaterials. Here, the self-assembly of poly(ethylene oxide)-block-polystyrene block copolymers is systematically investigated and a series of robust PCs is developed via a cosolvent method. Ordered nanoporous metal oxide particles are obtained by selectively filling the hydrophilic channels of PCs via an impregnation strategy, followed by a two-step thermal treatment. Based on this versatile PC platform, the general synthesis of a library of ordered porous particles with different pore structures3 ¯ $\bar{3}$ 3 ¯ $\bar{3}$ , tunable large pore size (18-78 nm), high specific surface areas (up to 123.3 m2 g-1 for WO3) and diverse framework compositions, such as transition and non-transition metal oxides, rare earth chloride oxides, perovskite, pyrochlore, and high-entropy metal oxides is demonstrated. As typical materials obtained via this method, ordered porous WO3 particles have the advantages of open continuous structure and semiconducting properties, thus showing superior gas sensing performances toward hydrogen sulfide.

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.

Advanced Composites Inspired by Biological Structures and Functions in Nature: Architecture Design, Strengthening Mechanisms, and Mechanical-Functional Responses

The natural design and coupling of biological structures are the root of realizing the high strength, toughness, and unique functional properties of biomaterials. Advanced architecture design is applied to many materials, including metal materials, inorganic nonmetallic materials, polymer materials, and so on. To improve the performance of advanced materials, the designed architecture can be enhanced by bionics of biological structure, optimization of structural parameters, and coupling of multiple types of structures. Herein, the progress of structural materials is reviewed, the strengthening mechanisms of different types of structures are highlighted, and the impact of architecture design on the performance of advanced materials is discussed. Architecture design can improve the properties of materials at the micro level, such as mechanical, electrical, and thermal conductivity. The synergistic effect of structure makes traditional materials move toward advanced functional materials, thus enriching the macroproperties of materials. Finally, the challenges and opportunities of structural innovation of advanced materials in improving material properties are discussed.