Graphene Oxide/Reduced Graphene Oxide Enhanced Noniridescent Structural Colors Based on Silica Photonic Spray Paints with Improved Mechanical Robustness

Enhancing color saturation in photonic glasses through optimized absorption

Photonic glasses, isotropically assembled nanoparticles with short-range correlation, can produce angle independent structural colors. They show broader reflectance spectra and lower saturated colors, compared to photonic crystals. Low color saturation creates barriers for photonic glasses to be used for coatings, cosmetics, and colors. Broadband absorbing materials are commonly used to absorb incoherently scattered light to enhance the saturation. However, there is limited understanding on how the absorption quantitatively affects the colors of photonic glasses. To this end, we here use a validated Monte Carlo-based multiple scattering model to investigate how absorption impacts the reflectance spectra in photonic glasses. We show that the color saturation can be maximized with an optimal level of absorption regardless of sample thickness or refractive index contrast between particles and matrix. We quantitatively demonstrate that the multiple scattering is largely reduced with the optimal absorption level and the reflectance is dominantly contributed by the single scattering. The optimal absorption occurs when the sample absorption mean free path is comparable to the transport mean free path, which offers a guidance on how much absorbing material is needed for creating highly saturated photonic glasses. This work will not only pave ways for pushing applications of angle-independent structural colors, but also improve our understanding of light scattering and absorption in short-range correlated disordered systems.

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles' structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.