From the iridescent wings of a blue morpho butterfly to the bright red feathers of a scarlet macaw, “structural colors” are evident across the natural world. Synthetic versions of these biomimetic materials, unlike pigment-based hues, are a result of the three-dimensional structure and arrangement within the materials themselves. However, the design and fabrication of materials with specific structural colors has been challenging, in part because structure-color relationships can only be established by probing the underlying structures of biomimetic materials, which is difficult to do
experimentally.
Now, a new paper in Science Advances describes an approach for identifying the relationship between a material’s structure and its color. Led by researchers from the University of Delaware and the University of Akron, this work demonstrates how colors made by mixtures of strongly absorbing, melanin- mimicking nanoparticles can be predicted using computational reverse-engineering methods. These findings can be used in the future to more efficiently fabricate materials that have custom, robust colorations.
The computational reverse-engineering method used in this study was developed by the lab of co-corresponding author Arthi Jayaraman, Centennial Term Professor for Excellence in Research and Education in UD’s Department of Chemical & Biomolecular Engineering. Known as Computational
Reverse-Engineering Analysis for Scattering Experiments’ (CREASE), this computational technique allows experimentalists to determine the 3D, molecular-level spatial arrangement within materials using small-angle scattering measurements, which were used in the Science Advances paper.
Since this study was completed, the CREASE method has been further refined (see “Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions” published in ACS Central Science) and applied to even more complex materials (see “Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (“P(q) and S(q) CREASE”)” in JACS Au).
The Jayaraman research group uses a combination of computational approaches, including modeling, molecular simulations, theory, and machine learning, to design new types of soft materials. Their group collaborates with experimentalists to validate their computational results as well as develop new computational methods for experimental data analysis, with the end goal of designing new materials for applications in energy, optics, photonics, biomedicine and more.
Jayaraman is available to speak about the CREASE method, its application in this and other material systems, and the importance of computational tools for the field of chemistry and materials science.
Science Advances
Mechanism of structural colors in binary mixtures of nanoparticle-based Supraballs