Mechanism of Structural Colors in Binary Mixtures of Nanoparticle-based Supraballs

TL;DR

This paper presents a computational approach combining structure reconstruction from scattering data and optical modeling to predict and engineer structural colors in nanoparticle mixtures, inspired by avian coloration.

Contribution

It introduces a novel reverse-engineering and FDTD simulation method to accurately predict colors in complex nanoparticle assemblies, enabling targeted material design.

Findings

Successfully predicted experimental colors in nanoparticle mixtures

Demonstrated influence of segregated nanoparticle layers on color

Provided a versatile computational tool for color engineering

Abstract

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce non-iridescent, saturated colors using nanoparticle assemblies. Mixtures of nanoparticles varying in particle chemistry (or complex refractive indices) and particle size have additional emergent properties that impact the color produced. For such complex multi-component systems, an understanding of assembled structure along with a robust optical modeling tool can empower scientists to perform intensive structure-color relationship studies and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments (CREASE) method and then use the reconstructed structure in finite-difference time-domain (FDTD) calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing melanin nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach presented in this work is useful for engineering synthetic materials with desired colors without laborious trial and error experiments.