Zhou Yan, Hu Lechuan, Wang Chengchao, Ma Lanxin
School of Energy and Power Engineering, Shandong University, Jinan 250061, China.
Optics & Thermal Radiation Research Center, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China.
Nanomaterials (Basel). 2022 Aug 7;12(15):2715. doi: 10.3390/nano12152715.
Silicon nanoparticles (SiNPs) with lowest-order Mie resonance produce non-iridescent and non-fading vivid structural colors in the visible range. However, the strong wavelength dependence of the radiation pattern and dielectric function makes it very difficult to design nanoparticle systems with the desired colors. Most existing studies focus on monodisperse nanoparticle systems, which are unsuitable for practical applications. This study combined the Lorentz-Mie theory, Monte Carlo, and deep neural networks to evaluate and design colored SiNP systems. The effects of the host medium and particle size distribution on the optical and color properties of the SiNP systems were investigated. A bidirectional deep neural network achieved accurate prediction and inverse design of structural colors. The results demonstrated that the particle size distribution flattened the Mie resonance peak and influenced the reflectance and brightness of the SiNP system. The SiNPs generated vivid colors in all three of the host media. Meanwhile, our proposed neural network model achieved a near-perfect prediction of colors with high accuracy of the designed geometric parameters. This work accurately and efficiently evaluates and designs the optical and color properties of SiNP systems, thus accelerating the design process and contributing to the practical production design of color inks, decoration, and printing.
具有最低阶米氏共振的硅纳米颗粒（SiNP）在可见光范围内产生非虹彩且不褪色的鲜艳结构色。然而，辐射模式和介电函数对波长的强烈依赖性使得设计具有所需颜色的纳米颗粒系统非常困难。大多数现有研究集中在单分散纳米颗粒系统上，而这种系统不适合实际应用。本研究结合了洛伦兹-米氏理论、蒙特卡罗方法和深度神经网络来评估和设计彩色SiNP系统。研究了主体介质和粒径分布对SiNP系统光学和颜色特性的影响。双向深度神经网络实现了结构色的精确预测和逆向设计。结果表明，粒径分布使米氏共振峰变平，并影响了SiNP系统的反射率和亮度。SiNP在所有三种主体介质中都产生了鲜艳的颜色。同时，我们提出的神经网络模型对颜色实现了近乎完美的预测，设计的几何参数具有很高的准确性。这项工作准确、高效地评估和设计了SiNP系统的光学和颜色特性，从而加速了设计过程，并有助于彩色墨水、装饰和印刷的实际生产设计。
