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AbstractMycelium is crucial in decomposing biomass and cycling nutrients in nature. While various environmental factors can influence mycelium growth, the role of substrate mechanics is not yet clear. In this study, we investigate the effect of substrate stiffness on mycelium growth. We prepared agar substrates of different concentrations to grow the mycelium, but kept other environmental and chemical conditions consistent. We made a time-lapse recording of the growing history with minimum interruption. We repeated our tests for different species. Our results generally support that mycelium grows faster on a stiffer substrate,Ganoderma lucidumgives the highest growth rate andPleurotus eryngiiis most sensitive to substrate stiffness. We combined experimental characterization and computational simulation to investigate the mechanism and discovered that mycelium concentrates on the surface of a rigid substrate, but penetrates the soft one. Our Monte Carlo simulations illustrate that such a penetration allows mycelium to grow in the three-dimensional space, but effectively slows down the surface occupation speed. Our study provides insights into fungal growth and reveals that the mycelium growth rate can be tuned through substrate stiffness, thus reducing the time for producing mycelium-based composites. Impact statementWe used agar substrates and tuned its stiffness to culture mycelium and compared tune its stiffness to culture mycelium and compare its growth in a well-controlled condition. Our results revealed that mycelium grows faster on stiffer substrates, thus fully occupying the petri dish surface more quickly. We repeated our study several times by testing four species,P. eryngii,G. lucidum,Trametes versicolor,and Flammulina velutipes,and the stiffest substrate always gives the highest mean growing rate than others. TheG. lucidumshows the highest spreading rate that is obtained on the stiffest substrate as 39.1 ± 2.0 mm²/h. We found that the mycelium on a soft substrate will grow into the substrate instead of spreading on the stiffer surface. Our Monte Carlo simulations further show that once the fibers grow into a three-dimensional substrate, its growth is slower than growing on a two-dimensional surface, providing a microscopic mechanism of the substrate stiffness effect. This study’s analysis of how substrate stiffness impacts mycelium growth is new, bridging a critical knowledge gap in understanding the relationship between substrate mechanics and fungal ecology. The knowledge from this study has a potential in accelerating sustainable manufacturing of mycelium-based composite by adjusting substrate mechanics. Graphical Abstract 

Abstract Bamboo culm has been widely used in engineering for its high strength, lightweight, and low cost. Its outermost epidermis is a smooth and dense layer that contains cellulose, silica particles, and stomata and acts as a water and mechanical barrier. Recent experimental studies have shown that the layer has a higher mechanical strength than other inside regions. Still, the mechanism is unclear, especially for how the low silica concentration (<10%) can effectively reinforce the layer and prevent the inner fibers from splitting. Here, theoretical analysis is combined with experimental imaging and 3D printing to investigate the effect of the distribution of silica particles on composite mechanics. The anisotropic partial distribution function of silica particles in bamboo skin yields higher toughness (>10%) than randomly distributed particles. A generative artificial intelligence (AI) model inspired by bamboo epidermis is developed to generate particle‐reinforced composites. Besides the visual similarity, it is found that the samples by the generative model show failure processes and fracture toughness identical to the actual bamboo epidermis. This work reveals the micromechanics of the bamboo epidermis. It illustrates that generative AI can help design bio‐inspired composites of a complex structure that cannot be uniformly represented by a simple building block or optimized around local boundaries. It expands the design space of particle‐reinforced composites for enhanced toughness modulus, offering advantages in industries where mechanical reliability is critical. 

Abstract Cis-peptide bonds are rare in proteins, and building blocks less favorable to the trans-conformer have been considered destabilizing. Although proline tolerates the cis-conformer modestly among all amino acids, for collagen, the most prevalent proline-abundant protein, all peptide bonds must be trans to form its hallmark triple-helix structure. Here, using host-guest collagen mimetic peptides (CMPs), we discover that surprisingly, even the cis-enforcing peptoid residues (N-substituted glycines) form stable triple-helices. Our interrogations establish that these peptoid residues entropically stabilize the triple-helix by pre-organizing individual peptides into a polyproline-II helix. Moreover, noting that the cis-demanding peptoid residues drastically reduce the folding rate, we design a CMP whose triple-helix formation can be controlled by peptoid cis-trans isomerization, enabling direct targeting of fibrotic remodeling in myocardial infarction in vivo. These findings elucidate the principles of peptoid cis-trans isomerization in protein folding and showcase the exploitation of cis-amide-favoring residues in building programmable and functional peptidomimetics. 

AbstractPolyvinyl alcohol (PVA) is a water-soluble synthetic polymer that can be used to make hydrogels for biomedical applications as well as biodegradable bags and films; however, compared to other plastics currently used for containers, it lacks mechanical strength, thermal stability, and can easily absorb water from humid environments. Although mechanical improvement has been observed by blending PVA with collagen in a hybrid hydrogel, there is a lack of fundamental understanding of the molecular mechanism, and it is not clear whether the improvement is limited to a hydrated state. Here, using classical molecular dynamics simulations based on fully atomistic models, we develop the equilibrated molecular structure of PVA with collagen and characterize its mechanics. We show that by interacting with a collagen molecule, PVA is equilibrated to a more ordered structure with each residue interacting with the near neighbors by forming more hydrogen bonds locally, making the structure stiffer than pure PVA. The structure shows higher thermal stability before melting, as well as higher rigidity in water. Our results provide the mechanism of the mechanical advantages of hybrid PVA-collagen polymer. The study demonstrates that the structure and mechanics of a synthetic polymer can be tuned by a tiny amount of a natural polymer at the molecular interface. Moreover, it may shed light on identifying a way to improve the mechanics of biodegradable polymer materials without adding much cost, which is crucial for environmental safety. Impact statementBlending natural and synthetic polymers (e.g., polyvinyl alcohol [PVA] and collagen in a hybrid hydrogel) has shown advantages in polymer mechanics, but there is a lack of fundamental understanding. Using molecular dynamics (MD) simulations based on fully atomistic models, we develop the equilibrated structure of the PVA with collagen and characterize its mechanics. We show that by interacting with a collagen molecule, PVA is equilibrated to a more ordered structure with each residue interacting with the near neighbors by forming more H-bonds locally and the structure is stiffer than pure PVA. Moreover, the structure shows a higher thermal stability before the melting point of PVA, as well as higher rigidity in water. Our results demonstrate that the structure and mechanics of a synthetic polymer can be tuned by a tiny amount of a natural polymer at the molecular interface. It provides the mechanism of the mechanical advantages as experimentally observed. This study paves the way for the multiscale modeling and mechanical design of the hybrid polymer material. It sheds light on identifying a way to improve the mechanics of biodegradable materials without adding much cost for both material functionality and environmental safety. Graphical abstract 

We synthesized and characterized the advanced multifunctional features of mycelium–coir-based composites as a replacement for fossil-based foams used in building insulation. 

2D materials such as graphene, monolayer MoS2 and MXene are highly functional for their unique mechanical, thermal and electrical features and are considered building blocks for future ultrathin, flexible electronics. However, they can easily fracture from flaws or defects and thus it is important to increase their toughness in applications. Here, inspired by natural layered composites and architected 3D printed materials of high toughness, we introduce architected defects to the 2D materials and study their fracture in molecular dynamics simulations. We find that the length of the defects in the shape of parallel bridges is crucial to fracture toughness, as long bridges can significantly increase the toughness of graphene and MoS2 but decrease the toughness of MXene, while short bridges show opposite effects. This strategy can increase the toughness of 2D materials without introducing foreign materials or altering the chemistry of the materials, providing a general method to improve their mechanics. 

The distribution of material phases is crucial to determine the composite's mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.