Structural Color Production in Melanin‐Based Disordered Colloidal Nanoparticle Assemblies in Spherical Confinement

Abstract

Melanin is a ubiquitous natural pigment that exhibits broadband absorption and high refractive index. Despite its widespread use in structural color production, how the absorbing material, melanin, affects the generated color is unknown. Using a combined molecular dynamics and finite‐difference time‐domain computational approach, this paper investigates structural color generation in one‐component melanin nanoparticle‐based supraparticles (called supraballs) as well as binary mixtures of melanin and silica (nonabsorbing) nanoparticle‐based supraballs. Experimentally produced one‐component melanin and one‐component silica supraballs, with thoroughly characterized primary particle characteristics using neutron scattering, produce reflectance profiles similar to the computational analogs, confirming that the computational approach correctly simulates both absorption and multiple scattering from the self‐assembled nanoparticles. These combined approaches demonstrate that melanin's broadband absorption increases the primary reflectance peak wavelength, increases saturation, and decreases lightness factor. In addition, the dispersity of nanoparticle size more strongly influences the optical properties of supraballs than packing fraction, as evidenced by the production of a larger range of colors when size dispersity is varied versus packing fraction. For binary melanin and silica supraballs, the chemistry‐based stratification allows for more diverse color generation and finer saturation tuning than does the degree of mixing/demixing between the two chemistries.

Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions

“…39 The above limitations of existing analytical models motivate the development of a computational method that is more broadly applicable without requiring the choice of a specific analytical model to fit the scattering data or significant a priori characterization. Further, it would be ideal if the computational method also provides a representative 3D structural reconstruction that could then be used as an input for other calculations (e.g., finite-difference time-domain method for optical properties 40 and resistor network model calculation for electrical conductivity 41 ).…”

“…Directed or self-assembly of nanoparticles in solution or near surfaces/interfaces is used to engineer functional materials for optical, biomedical, catalytic, and electronic applications. − For example, assembly of nanoparticles in thin film or droplets (e.g., in emulsion assembly) into ordered three-dimensional (3D) nanostructures − is used in photonic applications where the noniridescent structural color can be adjusted by tuning the nanoparticle size and structural order. ,− In these applications, the assembled structure directly affects the resulting macroscopic properties, making structural characterization an important step during development of such functional materials.…”

Modeling Structural Colors from Disordered One-Component Colloidal Nanoparticle-Based Supraballs Using Combined Experimental and Simulation Techniques

“…Inspired by the chemistry and arrangement of melanosomes (melanin-containing organelles) in bird feathers, researchers have used absorbing nanoparticles such as melanin , to produce saturated colors. For the most part, the design of these colors has been based on semiempirical methods by controlling nanoparticle structure (size, dispersity, and packing) and optical properties (complex refractive index). ,,− A quantitative approach to model and predict color generation from disordered colloidal assemblies requires knowledge of the internal structure and a robust optical modeling method that handles multiple scattering, large refractive index contrast, and high broadband absorption.…”

“…For quantitative optical modeling of highly absorbing materials, like melanin, and its dense nanoparticle assemblies, a more direct first-principle technique is needed. The finite-difference time-domain (FDTD) method , has been shown to predict structural color generation in colloidal nanoparticle assemblies with large refractive index contrasts, high broadband absorption, and dense packing of nanoparticles . However, the use of FDTD requires spatial coordinates of all the nanoparticles within the self-assembled structure.…”

“…The computed and experimental reflectance spectra are largely indistinguishable within error. The slight reflectance mismatch particularly at low wavelength may be caused by the ordered supraball surface (see inset in Figure C) compared to the less ordered, bulk CREASE reconstruction Figure B provides both quantitative and visual comparisons for the colors obtained from the experimental measurement and optical modeling of the CREASE output structures.…”