We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reconstructed 3D structures of the sample. This new method is an improvement over our original computational reverse-engineering analysis for scattering experiments (CREASE) method (ACS Materials Au 2021, 1 (22), 140−156), which takes as input the experimental scattering profiles and outputs a 3D visualization and structural characterization (e.g., real space pair-correlation functions, domain sizes, and extent of mixing in binary nanoparticle mixtures) of the nanoparticle mixtures. The new gene-based CREASE method reduces the computational running time by >95% as compared to the original CREASE and performs better in scenarios where the original CREASE method performed poorly. Furthermore, the ML model linking features of nanoparticle solutions (e.g., concentration, nanoparticles' tendency to aggregate) to a computed scattering profile is generic enough to analyze scattering profiles for nanoparticle solutions at conditions (nanoparticle chemistry and size) beyond those that were used for the ML training. Finally, we demonstrate application of this new gene-based CREASE method for analysis of small-angle X-ray scattering results from a nanoparticle solution with unknown nanoparticle aggregation and small-angle neutron scattering results from a binary nanoparticle assembly with unknown mixing/segregation among the nanoparticles.

Melanin, with its high refractive index (RI) and broadband absorption, is an important biomaterial responsible for many of the vibrant structural colors observed in nature and for UV protection. Even though the RI plays an important role in the function of melanin, there is an ambiguity in its reported complex RI and a lack of understanding of whether and how the UV radiation, these materials are likely to experience under normal use, will affect the complex RI. Here, we measured the wavelength-dependent (360-1700 nm) complex RI of synthetic melanin films before and after in situ UV treatment using ellipsometry. We modeled the ellipsometric data using a modified Tauc-Lorentz dispersion model and measured the thickness independently using atomic force microscopy. The UV radiation reduces the film thickness. Interestingly, we find that both the real and imaginary terms of the RI increase upon UV radiation. These experiments provide accurate measurements of the optical properties of melanin and a surprising result that synthetic melanin absorbs more light ($25% increase in extinction coefficient) below 600 nm after UV exposure.

Use of colloidal suspensions to generate structural colors has the potential to reduce the use of toxic metals or organic pigments in inkjet printing, coatings, cosmetics, and other applications, and is a promising avenue to create large-scale nanostructures that produce long-lasting colors. However, expanded use of structural colors requires a reduction in coffee-ring effects during printing, which currently requires intricately patterned substrates or high particle concentrations, and diversification of colors to compete with conventional printing inks. Here, we treat substrate surfaces with cold plasma to facilitate spontaneous assembly of particles into colloidal nanostructures, reducing the need for highly concentrated particle suspensions. Moreover, by employing binary mixtures, we can tune the lightness of the hue produced or change the hue itself, allowing us to cover wider regions of color space. We demonstrate the use of this cold-plasma approach on a variety of substrates, favoring substrate diversity on which printing can be performed. This methodology enables creation of high-resolution, complex designs and opens a path for extending the limits of anticounterfeiting applications by using binary mixtures.

The diverse colours of bird feathers are produced by both pigments and nanostructures, and can have substantial thermal consequences. This is because reflectance, transmittance and absorption of differently coloured tissues affect the heat loads acquired from solar radiation. Using reflectance measurements and heating experiments on sunbird museum specimens, we tested the hypothesis that colour and their colour producing mechanisms affect feather surface heating and the heat transferred to skin level. As predicted, we found that surface temperatures were strongly correlated with plumage reflectivity when exposed to a radiative heat source and, likewise, temperatures reached at skin level decreased with increasing reflectivity. Indeed, nanostructured melanin-based iridescent feathers (green, purple, blue) reflected less light and heated more than unstructured melanin-based colours (grey, brown, black), as well as olives, carotenoid-based colours (yellow, orange, red) and non-pigmented whites. We used optical and heat modelling to test if differences in nanostructuring of melanin, or the bulk melanin content itself, better explains the differences between melanin-based feathers. These models showed that the greater melanin content and, to a lesser extent, the shape of the melanosomes explain the greater photothermal absorption in iridescent feathers. Our results suggest that iridescence can increase heat loads, and potentially alter birds' thermal balance.

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.

Nanostructured materials producing structural colors have great potential in replacing toxic metals or organic pigments. Electrophoretic deposition (EPD) is a promising method for producing these materials on a large scale, but it requires improvements in brightness, saturation, and mechanical stability. Herein, we use EPD assembly to codeposit silica (SiO 2 ) particles with precursors of synthetic melanin, polydopamine (PDA), to produce mechanically robust, wide-angle structurally colored coatings. We use spectrophotometry to show that PDA precursors enhance the saturation of structural colors and nanoscratch testing to demonstrate that they stabilize particles within the EPD coatings. Stabilization by PDA precursors allows us to coat flexible substrates that can sustain bending stresses, opening an avenue for electroprinting on flexible materials.

Bright, saturated structural colors in birds have inspired synthesis of self-assembled, disordered arrays of assembled nanoparticles with varied particle spacings and refractive indices. However, predicting colors of assembled nanoparticles, and thereby guiding their synthesis, remains challenging due to the effects of multiple scattering and strong absorption. Here, we use a computational approach to first reconstruct the nanoparticles' assembled structures from smallangle scattering measurements and then input the reconstructed structures to a finite-difference time-domain method to predict their color and reflectance. This computational approach is successfully validated by comparing its predictions against experimentally measured reflectance and provides a pathway for reverse engineering colloidal assemblies with desired optical and photothermal properties.

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce noniridescent, saturated colors using nanoparticle assemblies. Nanoparticle mixtures varying in particle chemistry and size have additional emergent properties that affect the color produced. For complex multicomponent systems, understanding the assembled structure and a robust optical modeling tool can empower scientists to identify structure-color relationships 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 method and use the reconstructed structure in finite-difference time-domain calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach that we present is useful for engineering synthetic materials with desired colors without laborious trial-and-error experiments.