Understanding Plant Biomass via Computational Modeling

Plant Cell Wall-Like Soft Materials: Micro- and Nanoengineering, Properties, and Applications

Plant cell wall (CW)-like soft materials, referred to as artificial CWs, are composites of assembled polymers containing micro-/nanoparticles or fibers/fibrils that are designed to mimic the composition, structure, and mechanics of plant CWs. CW-like materials have recently emerged to test hypotheses pertaining to the intricate structure-property relationships of native plant CWs or to fabricate functional materials. Here, research on plant CWs and CW-like materials is reviewed by distilling key studies on biomimetic composites primarily composed of plant polysaccharides, including cellulose, pectin, and hemicellulose, as well as organic polymers like lignin. Micro- and nanofabrication of plant CW-like composites, characterization techniques, and in silico studies are reviewed, with a brief overview of current and potential applications. Micro-/nanofabrication approaches include bacterial growth and impregnation, layer-by-layer assembly, film casting, 3-dimensional templating microcapsules, and particle coating. Various characterization techniques are necessary for the comprehensive mechanical, chemical, morphological, and structural analyses of plant CWs and CW-like materials. CW-like materials demonstrate versatility in real-life applications, including biomass conversion, pulp and paper, food science, construction, catalysis, and reaction engineering. This review seeks to facilitate the rational design and thorough characterization of plant CW-mimetic materials, with the goal of advancing the development of innovative soft materials and elucidating the complex structure-property relationships inherent in native CWs.

PREDIG: Web application to model and predict the enzymatic saccharification of plant cell wall

Enzymatic digestion of lignocellulosic plant biomass is a key step in bio-refinery approaches for the production of biofuels and other valuable chemicals. However, the recalcitrance of this material in conjunction with its variability and heterogeneity strongly hampers the economic viability and profitability of biofuel production. To complement both academic and industrial experimental research in the field, we designed an advanced web application that encapsulates our in-house developed complex biophysical model of enzymatic plant cell wall degradation. PREDIG (https://predig.cs.hhu.de/) is a user-friendly, free, and fully open-source web application that allows the user to perform in silico experiments. Specifically, it uses a Gillespie algorithm to run stochastic simulations of the enzymatic saccharification of a lignocellulose microfibril, at the mesoscale, in three dimensions. Such simulations can for instance be used to test the action of distinct enzyme cocktails on the substrate. Additionally, PREDIG can fit the model parameters to uploaded experimental time-course data, thereby returning values that are intrinsically difficult to measure experimentally. This gives the user the possibility to learn which factors quantitatively explain the recalcitrance to saccharification of their specific biomass material.

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.

Therapeutic Modulation of Cell Morphology and Phenotype of Diseased Human Cells towards a Healthier Cell State Using Lignin

Despite lignin's global abundance and its use in biomedical studies, our understanding of how lignin regulates disease through modulation of cell morphology and associated phenotype of human cells is unknown. We combined an automated high-throughput image cell segmentation technique for quantitatively measuring a panel of cell shape descriptors, droplet digital Polymerase Chain Reaction for absolute quantification of gene expression and multivariate data analyses to determine whether lignin could therapeutically modulate the cell morphology and phenotype of inflamed, degenerating diseased human cells (osteoarthritic (OA) chondrocytes) towards a healthier cell morphology and phenotype. Lignin dose-dependently modified all aspects of cell morphology and ameliorated the diseased shape of OA chondrocytes by inducing a less fibroblastic healthier cell shape, which correlated with the downregulation of collagen 1A2 (COL1A2, a major fibrosis-inducing gene), upregulation of collagen 2A1 (COL2A1, a healthy extracellular matrix-inducing gene) and downregulation of interleukin-6 (IL-6, a chronic inflammatory cytokine). This is the first study to show that lignin can therapeutically target cell morphology and change a diseased cells' function towards a healthier cell shape and phenotype. This opens up novel opportunities for exploiting lignin in modulation of disease, tissue degeneration, fibrosis, inflammation and regenerative medical implants for therapeutically targeting cell function and outcome.

Understanding Nanocellulose-Water Interactions: Turning a Detriment into an Asset

Modern technology has enabled the isolation of nanocellulose from plant-based fibers, and the current trend focuses on utilizing nanocellulose in a broad range of sustainable materials applications. Water is generally seen as a detrimental component when in contact with nanocellulose-based materials, just like it is harmful for traditional cellulosic materials such as paper or cardboard. However, water is an integral component in plants, and many applications of nanocellulose already accept the presence of water or make use of it. This review gives a comprehensive account of nanocellulose-water interactions and their repercussions in all key areas of contemporary research: fundamental physical chemistry, chemical modification of nanocellulose, materials applications, and analytical methods to map the water interactions and the effect of water on a nanocellulose matrix.

Emerging Engineered Wood for Building Applications

The building sector, including building operations and materials, was responsible for the emission of ∼11.9 gigatons of global energy-related CO₂ in 2020, accounting for 37% of the total CO₂ emissions, the largest share among different sectors. Lowering the carbon footprint of buildings requires the development of carbon-storage materials as well as novel designs that could enable multifunctional components to achieve widespread applications. Wood is one of the most abundant biomaterials on Earth and has been used for construction historically. Recent research breakthroughs on advanced engineered wood products epitomize this material's tremendous yet largely untapped potential for addressing global sustainability challenges. In this review, we explore recent developments in chemically modified wood that will produce a new generation of engineered wood products for building applications. Traditionally, engineered wood products have primarily had a structural purpose, but this review broadens the classification to encompass more aspects of building performance. We begin by providing multiscale design principles of wood products from a computational point of view, followed by discussion of the chemical modifications and structural engineering methods used to modify wood in terms of its mechanical, thermal, optical, and energy-related performance. Additionally, we explore life cycle assessment and techno-economic analysis tools for guiding future research toward environmentally friendly and economically feasible directions for engineered wood products. Finally, this review highlights the current challenges and perspectives on future directions in this research field. By leveraging these new wood-based technologies and analysis tools for the fabrication of carbon-storage materials, it is possible to design sustainable and carbon-negative buildings, which could have a significant impact on mitigating climate change.

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.

Performance of Screened-Exchange Functionals for Band Gaps and Lattice Constants of Crystals

Kohn-Sham density functional theory is the most widely used method for electronic structure calculations of solid-state systems. The screened-exchange functionals developed following the influential work of Scuseria and co-workers in 2003-2006 have significantly improved the accuracy of the predictions of solid-state properties. This work assesses six screened-exchange density functionals for the prediction of 60 band gaps (database BG60) and 68 lattice constants (database LC68). The band gaps are calculated with both consistently calculated lattice constants and experimental lattice constants. Results for the nonlocal screened-exchange functionals are compared with those for six widely used or recently developed local functionals. The results show that all the screened-exchange functionals have smaller mean absolute errors (MAEs) than any of the local functionals. All the functionals except HLE17 overestimate (on average) the lattice constants, and M06-SX gives the best performance among the compared functionals, with a MAE of 0.051 Å. All the functionals underestimate (on average) the band gaps, and M06-SX outperforms all other functionals, with a MAE of 0.47 eV. M06-SX also has the lowest root-mean-squared error for both LC68 and BG60. For the subdatabases of BG60, M06-SX shows better performance for ionic crystals and systems with large band gaps, while HSE12s gives better results for semiconductors and systems with small band gaps. Overall, M06-SX shows the best performance for solid-state systems, followed by N12-SX and HSE12s. The best-performing local functionals are M06-L, revM06-L, and HLE17 for band gaps and M06-L and revM06-L for lattice constants. We found that M06-SX, revM06-L, and N12-SX not only are well optimized for a broad array of chemical properties but also have very good performance for the databases in this paper, making them well-suited for applications involving heterogeneous chemistry.

Nanocellulosics in Transient Technology

Envisage a world where discarded electrical/electronic devices and single-use consumables can dematerialize and lapse into the environment after the end-of-useful life without constituting health and environmental burdens. As available resources are consumed and human activities build up wastes, there is an urgency for the consolidation of efforts and strategies in meeting current materials needs while assuaging the concomitant negative impacts of conventional materials exploration, usage, and disposal. Hence, the emerging field of transient technology (Green Technology), rooted in eco-design and closing the material loop toward a friendlier and sustainable materials system, holds enormous possibilities for assuaging current challenges in materials usage and disposability. The core requirements for transient materials are anchored on meeting multicomponent functionality, low-cost production, simplicity in disposability, flexibility in materials fabrication and design, biodegradability, biocompatibility, and environmental benignity. In this regard, biorenewables such as cellulose-based materials have demonstrated capacity as promising platforms to fabricate scalable, renewable, greener, and efficient materials and devices such as membranes, sensors, display units (for example, OLEDs), and so on. This work critically reviews the recent progress of nanocellulosic materials in transient technologies toward mitigating current environmental challenges resulting from traditional material exploration, usage, and disposal. While spotlighting important fundamental properties and functions in the material selection toward practicability and identifying current difficulties, we propose crucial research directions in advancing transient technology and cellulose-based materials in closing the loop for conventional materials and sustainability.

Nanoscale Mechanism of Moisture-Induced Swelling in Wood Microfibril Bundles

Understanding nanoscale moisture interactions is fundamental to most applications of wood, including cellulosic nanomaterials with tailored properties. By combining X-ray scattering experiments with molecular simulations and taking advantage of computed scattering, we studied the moisture-induced changes in cellulose microfibril bundles of softwood secondary cell walls. Our models reproduced the most important experimentally observed changes in diffraction peak locations and widths and gave new insights into their interpretation. We found that changes in the packing of microfibrils dominate at moisture contents above 10-15%, whereas deformations in cellulose crystallites take place closer to the dry state. Fibrillar aggregation is a significant source of drying-related changes in the interior of the microfibrils. Our results corroborate the fundamental role of nanoscale phenomena in the swelling behavior and properties of wood-based materials and promote their utilization in nanomaterials development. Simulation-assisted scattering analysis proved an efficient tool for advancing the nanoscale characterization of cellulosic materials.

Cellulose-Based Fibrous Materials From Bacteria to Repair Tympanic Membrane Perforations

Perforation is the most common illness of the tympanic membrane (TM), which is commonly treated with surgical procedures. The success rate of the treatment could be improved by novel bioengineering approaches. In fact, a successful restoration of a damaged TM needs a supporting biomaterial or scaffold able to meet mechano-acoustic properties similar to those of the native TM, along with optimal biocompatibility. Traditionally, a large number of biological-based materials, including paper, silk, Gelfoam®, hyaluronic acid, collagen, and chitosan, have been used for TM repair. A novel biopolymer with promising features for tissue engineering applications is cellulose. It is a highly biocompatible, mechanically and chemically strong polysaccharide, abundant in the environment, with the ability to promote cellular growth and differentiation. Bacterial cellulose (BC), in particular, is produced by microorganisms as a nanofibrous three-dimensional structure of highly pure cellulose, which has thus become a popular graft material for wound healing due to a number of remarkable properties, such as water retention, elasticity, mechanical strength, thermal stability, and transparency. This review paper provides a comprehensive overview of the current experimental studies of BC, focusing on the application of BC patches in the treatment of TM perforations. In addition, computational approaches to model cellulose and TM are summarized, with the aim to synergize the available tools toward the best design and exploitation of BC patches and scaffolds for TM repair and regeneration.