Surface Plasmon Resonance of Gold Nano‐Sea‐Urchins Controlled by Machine‐Learning‐Based Regulation in Seed‐Mediated Growth

Abstract

Gold sea‐urchin‐like nanoparticles (GSNPs) are promising candidates for cancer thermotherapy. Due to the complex growth of GSNPs and the need for a precise prediction of their surface plasmon wavelength, genetic‐algorithm‐based artificial neural networks (GANNs) are used to determine the relationship between synthesis parameters and the surface plasmon wavelength of GSNPs grown via seed‐mediated growth assisted by machine learning. Herein, a low‐data test is trained by varying the ratio and concentration of gold seeds, sodium citrate, hydroquinone, and HAuCl4. Then, a big data confirmation is conducted through massive parameter collection from over 684 samples. The well‐trained GANN can guide parameter selection for seed‐mediated growth to obtain the desired surface plasmon wavelength. An optimal model can be obtained after big data evolution to assist the growth method screening of the seed‐mediated growth of sea‐urchin‐like gold nanoparticles (SGNPs) to achieve a stronger electromagnetic field of the surface plasmons. Machine learning has an advantage over empirical method for seed‐mediated growth and surface plasmon wavelength prediction, which increases research efficiency and decreases cost. The performance of the grown SGNPs is substantially improved in the visible domain.