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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Advanced photonics research

سال: 2021

ISSN: ['2699-9293']

DOI: https://doi.org/10.1002/adpr.202100052