Robust myco-composites: a biocomposite platform for versatile hybrid-living materials.

Fungal mycelium, a living network of filamentous threads, thrives on lignocellulosic waste and exhibits rapid growth, hydrophobicity, and intrinsic regeneration, offering a potential means to create next-generation sustainable and functional composites. However, existing hybrid-living mycelium composites (myco-composites) are tremendously constrained by conventional mold-based manufacturing processes, which are only compatible with simple geometries and coarse biomass substrates that enable gas exchange. Here we introduce a class of structural myco-composites manufactured with a novel platform that harnesses high-resolution biocomposite additive manufacturing and robust mycelium colonization with indirect inoculation. We leverage principles of hierarchical composite design and selective nutritional provision to create a robust myco-composite that is scalable, tunable, and compatible with complex geometries. To illustrate the versatility of this platform, we characterize the impact of mycelium colonization on mechanical and surface properties of the composite. We found that our method yields the strongest mycelium composite reported to date with a modulus of 160 MPa and tensile strength of 0.72 MPa, which represents over a 15-fold improvement over typical mycelium composites, and further demonstrate unique applications with fabrication of foldable bio-welded containers and flexible mycelium textiles. This study bridges the gap between biocomposite and hybrid-living materials research, opening the door to advanced structural mycelium applications and demonstrating a novel platform for development of diverse hybrid-living materials.

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics.

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

An automated biomateriomics platform for sustainable programmable materials discovery.

Recently, the potential to create functional materials from various forms of organic matter has received increased interest due to its potential to address environmental concerns. However, the process of creating novel materials from biomass requires extensive experimentation. A promising means of predicting the properties of such materials would be the use of machine-learning models trained on or integrated into self-learned experimental data and methods. We outline an automated system for the discovery and characterization of novel, sustainable, and functional materials from input biomass. Artificial intelligence provides the capacity to examine experimental data, draw connections between composite composition and behavior, and design future experiments to expand the system's understanding of the studied materials. Extensions to the system are described that could further accelerate the discovery of sustainable composites, including the use of interpretable machine-learning methods to expand the insights gleaned from to human-readable materiomic insights about material process-structure-functional relationships.