Search

The study of biological materials and bio‐inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open‐source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer‐reviewed articles in the field of structural biological and bio‐inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval‐Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio‐inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains. [ABSTRACT FROM AUTHOR]

Two frontier crystalline porous framework materials, namely, metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), have been widely explored owing to their outstanding physicochemical properties. While each type of framework has its own intrinsic advantages and shortcomings for specific applications, combining the complementary properties of the two materials allows the engineering of new classes of hybrid porous crystalline materials with properties superior to the individual components. Since the first report of MOF/COF hybrid in 2016, it has rapidly evolved as a novel platform for diverse applications. The state-of-art advances in the various synthetic approaches of MOF/COF hybrids are hereby summarized, together with their applications in different areas. Perspectives on the main challenges and future opportunities are also offered in order to inspire a multidisciplinary effort toward the further development of chemically diverse, multi-functional hybrid porous crystalline materials.

Abstract The study of biological materials and bio‐inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open‐source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer‐reviewed articles in the field of structural biological and bio‐inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval‐Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio‐inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

3D printing of fiber‐reinforced composites is expected to be the forefront technology for the next‐generation high‐strength, high‐toughness, and lightweight structural materials. The intrinsic architecture of 3D‐printed composites closely represents biomimetic micro/macrofibril‐like hierarchical structure composed of intermediate filament assembly among the micron‐sized reinforcing fibers, and thus contributes to a novel mechanism to simultaneously improve mechanical properties and structural features. Notably, it is found that an interfacial heterogeneity between numerous inner interfaces in the hierarchical structure enables an exceptional increase in the toughness of composites. The strong interfacial adhesion between the fibers and matrix, with accompanying the inherently weak interfacial adhesion between intermediate filaments and the resultant interfacial voids, provide a close representation of the toughness behavior of natural architectures relying on the localized heterogeneity. Given the critical embedment length of fiber reinforcement, extraordinary improvement has been attained not only in the strength but also in toughness taking advantage of the synergy effect from the aforementioned nature‐inspired features. Indeed, the addition of a small amount of short fiber to the brittle bio‐filaments results in a noticeable increase of more than 200% in the tensile strength and modulus with further elongation increment. This article highlights the inherent structural hierarchy of 3D‐printed composites and the relevant sophisticated mechanism for anomalous mechanical reinforcement. [ABSTRACT FROM AUTHOR]

Abstract 3D printing of fiber‐reinforced composites is expected to be the forefront technology for the next‐generation high‐strength, high‐toughness, and lightweight structural materials. The intrinsic architecture of 3D‐printed composites closely represents biomimetic micro/macrofibril‐like hierarchical structure composed of intermediate filament assembly among the micron‐sized reinforcing fibers, and thus contributes to a novel mechanism to simultaneously improve mechanical properties and structural features. Notably, it is found that an interfacial heterogeneity between numerous inner interfaces in the hierarchical structure enables an exceptional increase in the toughness of composites. The strong interfacial adhesion between the fibers and matrix, with accompanying the inherently weak interfacial adhesion between intermediate filaments and the resultant interfacial voids, provide a close representation of the toughness behavior of natural architectures relying on the localized heterogeneity. Given the critical embedment length of fiber reinforcement, extraordinary improvement has been attained not only in the strength but also in toughness taking advantage of the synergy effect from the aforementioned nature‐inspired features. Indeed, the addition of a small amount of short fiber to the brittle bio‐filaments results in a noticeable increase of more than 200% in the tensile strength and modulus with further elongation increment. This article highlights the inherent structural hierarchy of 3D‐printed composites and the relevant sophisticated mechanism for anomalous mechanical reinforcement.

Abstract Two frontier crystalline porous framework materials, namely, metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs), have been widely explored owing to their outstanding physicochemical properties. While each type of framework has its own intrinsic advantages and shortcomings for specific applications, combining the complementary properties of the two materials allows the engineering of new classes of hybrid porous crystalline materials with properties superior to the individual components. Since the first report of MOF/COF hybrid in 2016, it has rapidly evolved as a novel platform for diverse applications. The state‐of‐art advances in the various synthetic approaches of MOF/COF hybrids are hereby summarized, together with their applications in different areas. Perspectives on the main challenges and future opportunities are also offered in order to inspire a multidisciplinary effort toward the further development of chemically diverse, multi‐functional hybrid porous crystalline materials.

Abstract The direct assembly of functional nanoparticles into a highly crystalline mesoporous semiconductor with oriented configurations is challenging but of significance. Herein, an evaporation induced oriented co‐assembly strategy is reported to incorporate SnO2 nanocrystals (NCs) into a 3D branched mesoporous TiO2 framework by using poly(ethylene oxide)‐block‐polystyrene (PEO‐b‐PS) as the template, SnO2 NCs as the direct tin source, and titanium butoxide (TBOT) as the titania precursor. Owing to the combined properties of ultrasmall particle size (3–5 nm), excellent dispersibility and presence of abundant hydroxyl groups, SnO2 NCs can easily interact with PEO block of the template through hydrogen bonding and co‐assemble with hydrolyzed TBOT to form a novel hierarchical branched mesoporous structure (SHMT). After calcination, the obtained composites exhibit a unique 3D flower‐like structure, which consists of numerous mesoporous rutile TiO2 branches with uniform cylindrical mesopores (≈9 nm). More importantly, the SnO2 NCs are homogeneously distributed in the mesoporous TiO2 matrix, forming numerous n–n heterojunctions. Due to the unique textual structures, the SHMT‐based gas sensors show excellent gas sensing performance with fast response/recovery dynamics, high sensitivity, and selectivity toward ethanol.

The direct assembly of functional nanoparticles into a highly crystalline mesoporous semiconductor with oriented configurations is challenging but of significance. Herein, an evaporation induced oriented co‐assembly strategy is reported to incorporate SnO2 nanocrystals (NCs) into a 3D branched mesoporous TiO2 framework by using poly(ethylene oxide)‐block‐polystyrene (PEO‐b‐PS) as the template, SnO2 NCs as the direct tin source, and titanium butoxide (TBOT) as the titania precursor. Owing to the combined properties of ultrasmall particle size (3–5 nm), excellent dispersibility and presence of abundant hydroxyl groups, SnO2 NCs can easily interact with PEO block of the template through hydrogen bonding and co‐assemble with hydrolyzed TBOT to form a novel hierarchical branched mesoporous structure (SHMT). After calcination, the obtained composites exhibit a unique 3D flower‐like structure, which consists of numerous mesoporous rutile TiO2 branches with uniform cylindrical mesopores (≈9 nm). More importantly, the SnO2 NCs are homogeneously distributed in the mesoporous TiO2 matrix, forming numerous n–n heterojunctions. Due to the unique textual structures, the SHMT‐based gas sensors show excellent gas sensing performance with fast response/recovery dynamics, high sensitivity, and selectivity toward ethanol. [ABSTRACT FROM AUTHOR]