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.

Deep learning approach to assess damage mechanics of bone tissue.

Machine learning methods have the potential to transform imaging techniques and analysis for healthcare applications with automation, making diagnostics and treatment more accurate and efficient, as well as to provide mechanistic insights into tissue deformation and fracture in physiological and pathological conditions. Here we report an exploratory investigation for the classification and prediction of mechanical states of cortical and trabecular bone tissue using convolutional neural networks (CNNs), residual neural networks (ResNet), and transfer learning applied to a novel dataset derived from high-resolution synchrotron-radiation micro-computed tomography (SR-microCT) images acquired in uniaxial continuous compression in situ. We present the systematic optimization of CNN architectures for classification of this dataset, visualization of class-defining features detected by the CNNs using gradient class activation maps (Grad-CAMs), comparison of CNN performance with ResNet and transfer learning models, and perhaps most critically, the challenges that arose from applying machine learning methods to an experimentally-derived dataset for the first time. With optimized CNN architectures, we obtained trained models that classified novel images between failed and pristine classes with over 98% accuracy for cortical bone and over 90% accuracy for trabecular bone. Harnessing a pre-trained ResNet with transfer learning, we further achieved over 98% accuracy on the cortical dataset, and 99% on the trabecular dataset. This demonstrates that powerful classifiers for high-resolution SR-microCT images can be developed even with few unique training samples and invites further development through the inclusion of more data and training methods to move towards novel, fundamental, and machine learning-driven insights into microstructural states and properties of bone.

Conductive Silk-Based Composites Using Biobased Carbon Materials.

There is great interest in developing conductive biomaterials for the manufacturing of sensors or flexible electronics with applications in healthcare, tracking human motion, or in situ strain measurements. These biomaterials aim to overcome the mismatch in mechanical properties at the interface between typical rigid semiconductor sensors and soft, often uneven biological surfaces or tissues for in vivo and ex vivo applications. Here, the use of biobased carbons to fabricate conductive, highly stretchable, flexible, and biocompatible silk-based composite biomaterials is demonstrated. Biobased carbons are synthesized via hydrothermal processing, an aqueous thermochemical method that converts biomass into a carbonaceous material that can be applied upon activation as conductive filler in composite biomaterials. Experimental synthesis and full-atomistic molecular dynamics modeling are combined to synthesize and characterize these conductive composite biomaterials, made entirely from renewable sources and with promising applications in fields like biomedicine, energy, and electronics.

Subtle changes in surface-tethered groups on PEGylated DNA nanoparticles significantly influence gene transfection and cellular uptake.

PEGylation strategy has been widely used to enhance colloidal stability of polycation/DNA nanoparticles (NPs) for gene delivery. To investigate the effect of polyethylene glycol (PEG) terminal groups on the transfection properties of these NPs, we synthesized DNA NPs using PEG-g-linear polyethyleneimine (lPEI) with PEG terminal groups containing alkyl chains of various lengths with or without a hydroxyl terminal group. For both alkyl- and hydroxyalkyl-decorated NPs with PEG grafting densities of 1.5, 3, or 5% on lPEI, the highest levels of transfection and uptake were consistently achieved at intermediate alkyl chain lengths of 3 to 6 carbons, where the transfection efficiency is significantly higher than that of nonfunctionalized lPEI/DNA NPs. Molecular dynamics simulations revealed that both alkyl- and hydroxyalkyl-decorated NPs with intermediate alkyl chain length exhibited more rapid engulfment than NPs with shorter or longer alkyl chains. This study identifies a new parameter for the engineering design of PEGylated DNA NPs.