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Materials from renewable sources: Learning from Nature, Martin-Martinez, Jin, Barreiro, Buehler, ACS Nano, 2018



Overviewing the field with an insightful outlook, the article proposes/discusses a sustainable route of learning from nature: using biomass-derived nanostructures, multiscale modeling, and biomass processing techniques to create novel bioinspired materials. This would better mimic the natural paradigms with important nanoscale features, improve materials design and manufacturing efficiency, and facilitate a double-win circular economy.

While specifying the two bottlenecks (nanoscale features and sustainability), nanoscale building blocks with essential features, precise control in organizing them hierarchically into macroscale structure by several techniques, sustainable routes using biomass-derived materials, and multiscale modeling and/with intelligent algorithms are highlighted through representative, leading studies in e.g., silk, chitin, wood in each specific discussed areas.         

Major points (useful info.):

1. Biological materials feature hierarchical structures that bridge atomistic/nanoscale constituents/building blocks with macroscale functional and mechanical properties;

2. Nano-science and technologies produce hierarchical nanostructured materials with unique properties different from those of bulk counterparts.

3. Two bottlenecks: organizing nanoscale building blocks hierarchically into macroscale with precise control, and the sustainability of resources and manufacturing.

4. Natural hierarchical materials such as silk, wood, show common features at nanoscale (nanoconfinement, nanofibril orientation, helicoidal stacking, nanocrystal alignment) that are crucial for superior mechanical properties and functionalities.

5. Biomass-derived materials as building blocks (with important nanostructures) through advanced biomass-processing technologies and multiscale modeling constitute a sustainable production of novel, bioinspired materials. For example, biomass-derived (i.e., from sugar, glucose, fructose,) carbon materials by hydrothermal conversion are promising in photovoltaics, catalysis, fuel cells, etc.

6. Wood and chitin represent two typical biological materials that produce important derived nanomaterials for various applications (medical, energy, structural).

7. Nanotechnologies for synthesis and characterization provide multiscale control of assembly and self-organization (1D, 2D, 3D), allowing the replication and expansion of the properties of biomaterials; the nanoconfinement is related to the electric, mechanical properties; the ability to combine different building blocks into complex architectures facilitates diverse applications (biomedicine, infrastructure, water desalination, etc.).

8. In making macroscopic fibers from nanoscale fibrils, the key of how includes to enhance alignment (e.g., by varying ionic concentration, oxidizing surface to increase repulsion and fibril mobility) and appropriate techniques (spinning, self-assembly, microfluidic, 3D printing). Also wood-derived directly well-aligned cellulose-based materials (as biomass-derived) show exceptional mechanical performance.

9. Multiscale modeling integrates designs at the atomic, the nano-meso-microscale, and further macro, continuum level, and aids in the synthesis and depolymerization of biomass-derived materials, e.g., roles of the special arrangement, hydrogen bonding in lignocellulosic biomass’s mechanical performance, describing the conversion of biomass-derived materials into usable materials.  

New computational methodologies are being developed, such as QM/MM; machine learning/artificial intelligence is also potential in this field.


Many natural materials (silk, wood, chitin) possess functionalities with superior mechanical properties resulted from their hierarchical structure of nanofibrils self-assembled under mild conditions. Learning from nature includes the precise control of nanoscale building blocks organizing hierarchically into macroscale and the sustainable synthesis route (renewable raw materials and green processing). Through combining natural and/or non-natural building blocks and developing synthesis techniques, materials beyond pure imitating natural ones can be pursued, while challenges such as the sustainability requiring green chemical processes that are transferable to scaling-up exist.

Multiscale computational modeling aids in this endeavor, understanding the mechanics and the processing, designing materials structures, and developing more efficient processing routes. New methodologies, high-throughput computational screening, and artificial intelligence algorithms will benefit the understanding, design/discovery, and synthesis of new materials from essential nanostructures. Despite the challenges, the future of developing bioinspired materials via controlling hierarchical nanostructures from sustainable sources with multiscale modeling is promising and exciting.  

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