Nature’s Way: Hierarchical Strengthening through Weakness

Guo, Kai; Buehler, Markus J.

doi:10.1016/j.matt.2019.07.011

Cited by 15 publications

(14 citation statements)

References 10 publications

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“…Composite materials are made of distinct components, each with different properties, that when combined form a material with properties that are different from those of the individual constituents. These materials are used in high-end applications owing to their optimal performance and as such, natural evolution might have followed a similar path 114 . In fact, many biological tissues easily fit within the above definition of composite materials 115 .…”

Section: [H2] Tissue Anisotropy or Composite Materialsmentioning

confidence: 99%

The stiffness of living tissues and its implications for tissue engineering

et al. 2020

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Section: [H2] Tissue Anisotropy or Composite Materialsmentioning

confidence: 99%

The stiffness of living tissues and its implications for tissue engineering

et al. 2020

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“…The aldehyde group (-CHO) can react with the amino group (-NH 2 ) [11] and the hydroxyl group (-OH) [33] at mild conditions, thus the biobased furfural (FF), hydroxymethyl furfural (HMF), saccharides (pectin, tapioca starch, and xylan), and vanillin and were tested here as additives for soy proteinbased adhesive. Inspired by biological materials, we also focused on weak interactions such as hydrogen bonds and metal coordinate bonds, [17][18][19][20][21] thus the biobased lignin, saccharides, organic acids (citric and maleic acid), and some metal ions were tested as additives as well.…”

Section: Effects Of Crosslinkers/additives and Condition Optimizationmentioning

confidence: 99%

“…[14][15][16] There is still work to be done to determine the reaction mechanisms between soy protein and crosslinkers and learn more about the interactions between the adhesive and fibers, since weak interactions such as hydrogen bonds and metal coordinate bonds could play key roles in the properties and performances of biobased materials. [17][18][19][20][21] Molecular dynamics (MD) simulation is a powerful tool to determine the material interactions and the origin of physical strength at molecular level. [1,20,22,23] It is particularly useful for biobased materials with weak interactions including silk, [24][25][26] cellulose, [27] and plant cell wall.…”

Section: Introductionmentioning

confidence: 99%

Bioadhesive Design Toward Renewable Composites: Adhesive Distribution and Molecular Adhesion

et al. 2023

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Improvements in renewable compositions of adhesives are key for advancing the existing wood industry and developing biocomposites. Building upon prior studies on fundamental interactions between model compounds of fiber and bioadhesives, more complex bioadhesives are investigated. Cold denaturation of soy protein isolate (SPI) in dilute sodium hydroxide provide sufficient physical strength for wood composites. Due to swelling, 5% epoxy resin is added to the SPI adhesive resulting in improved physical strength and water resistance. The SPI and epoxy are evenly distributed on the fiber surface, while additional SPI fills the gaps between fibers based on fluorescence microscopy and X‐ray photoelectron spectroscopy. Molecular dynamics (MD) simulations show that the physical strength of the composites increases with the content of SPI, with the number of hydrogen bonds (H‐bonds) as primary driver; in MD simulations, the composite strength is highest at 3% epoxy, though higher epoxy doses results in more H‐bonds. In ReaxFF MD simulations, other contributions (e.g., van der Waals energy) show no correlation. The developed bioadhesive and the interaction mechanism demonstrate here emphasize the weak physical interactions in these materials, though covalent bonds and other larger scale phenomena are important as well for macro performances of the bioadhesives.

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“…In this study, we use the effective crack length as an approximate measure for the overall toughness. By doing so, the toughness of a particular polycrystalline design can be measured by its crack length so that our framework can distinguish better or worse performance among different candidates by direction considering their crack patterns, reflecting a measure of tortuosity or effective crack length to characterize toughness (this is similar to the effects seen in hierarchical biomaterials like bone [24][25][26][27] ).…”

Section: Molecular Dynamics Data Representation and Machine Learninmentioning

confidence: 99%

“…This effective crack length is based on the concept of crack tortuosity (the more tortuous the crack path, the tougher and more resilient the material). [24][25][26][27] After the assessment, the new generation will be returned to the predictive neural network model to obtain their properties. By repeating the procedure of optimization and prediction iteratively, a superior crystalline composite for targeted properties can be achieved through this simulated evolution.…”

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confidence: 99%

Tuning Mechanical Properties in Polycrystalline Solids Using a Deep Generative Framework

Hsu

Buehler

2021

Adv Eng Mater

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Crystalline solids, which often possess distinguished mechanical properties among distinct orientations, may be potentially further recombined into various composite materials as an effective design strategy. [1][2][3][4][5][6] Among different types of composite design methods, better mechanical properties may be achieved by making use of the anisotropy and grain boundary effects seen in nanomaterials. [7][8][9] Considering any given space of a material, there is potential for microstructural design to improve its performance. However, in most cases, the cost of simulation or experiment will rapidly increase along with the daunting complexity of the sheer number of possible combinations within a given design space, and therefore the traditional process of composite design with experiment or simulation on crystalline solids is not capable of discovering new composite materials in a productive way. Here, the use of artificial intelligence (AI) methods [10,11] can be a powerful way to accelerate the discovery process [12][13][14][15][16][17][18][19][20][21] and, as we will show in this article, can also provide important mechanistic insights into the structure-property relationships. As such it complements other multiscale models in our quest to better understand how materials perform under extreme conditions.We adopt a framework that combines a deep learning (DL) model with a genetic algorithm (GA) [12,15,21] to rapidly search and generate composite materials on demand-simulating "nature's evolution paradigm" [19,22] -to achieve different mechanical properties, a process that enables us to realize "inverse design" through a continuous assessment of variations in design suggestions. Figure 1 shows a summary view of the method reported in this study. The deep learning model in this framework serves as a surrogate model for molecular dynamics (MD) simulation to help us obtain certain properties much faster, [15] offering a novel way to discover key mechanical feature of a material. As a result, the GA can perform a computationally efficient optimization of polycrystalline composites by using the DL-derived simulation results as feedback. Overall, the goal of this framework is to automatically determine optimized polycrystalline solid design

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Nature’s Way: Hierarchical Strengthening through Weakness

Abstract: Creating advanced properties out of simple building blocks remains a frontier in materials science. Two recent papers report advances to achieve toughening of graphene by mimicking a paradigm found in nature, realizing mechanical resiliency by mixing weak and strong bonding.

Cited by 15 publications

References 10 publications

The stiffness of living tissues and its implications for tissue engineering

The stiffness of living tissues and its implications for tissue engineering

Bioadhesive Design Toward Renewable Composites: Adhesive Distribution and Molecular Adhesion

Tuning Mechanical Properties in Polycrystalline Solids Using a Deep Generative Framework

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