Fixler Dror, Tzur Chen, Zalevsky Zeev
Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, Israel.
Materials (Basel). 2019 May 31;12(11):1766. doi: 10.3390/ma12111766.
In this paper, we present our optimization tool for fluorophore-conjugated metal nanostructures for the purpose of designing novel contrast agents for multimodal bioimaging. Contrast agents are of great importance to biological imaging. They usually include nanoelements causing a reduction in the need for harmful materials and improvement in the quality of the captured images. Thus, smart design tools that are based on evolutionary algorithms and machine learning definitely provide a technological leap in the fluorescence bioimaging world. This article proposes the usage of properly designed metallic structures that change their fluorescence properties when the dye molecules and the plasmonic nanoparticles interact. The nanostructures design and evaluation processes are based upon genetic algorithms, and they result in an optimal separation distance, orientation angles, and aspect ratio of the metal nanostructure.
在本文中，我们展示了用于荧光团共轭金属纳米结构的优化工具，旨在设计用于多模态生物成像的新型造影剂。造影剂对生物成像至关重要。它们通常包含纳米元素，可减少对有害物质的需求并提高所捕获图像的质量。因此，基于进化算法和机器学习的智能设计工具无疑在荧光生物成像领域带来了技术飞跃。本文提出使用经过适当设计的金属结构，当染料分子与等离子体纳米颗粒相互作用时，这些结构会改变其荧光特性。纳米结构的设计和评估过程基于遗传算法，最终得出金属纳米结构的最佳分离距离、取向角和纵横比。
