Extracting the Size Distribution of Gold Nanoparticles from UV-Visible Spectrum: an Artificial Neural Network Model
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A nondestructive detection of the size distribution of nanoparticles (NPs) is desired in various applications. The conventional method utilizes the dynamic light scattering from which the sizes of NPs are drawn by using the Stokes-Einstein relation. We propose a novel method to draw the size distribution of NPs from the UV-visible spectrum. Our method utilizes an artificial neural network (ANN) model trained against an extensive dataset generated by Mie theory. The promising performance of the present ANN model is demonstrated.
Abstract
A nondestructive detection of the size distribution of nanoparticles (NPs) is desired in various applications. The conventional method utilizes the dynamic light scattering from which the sizes of NPs are drawn by using the Stokes-Einstein relation. We propose a novel method to draw the size distribution of NPs from the UV-visible spectrum. Our method utilizes an artificial neural network (ANN) model trained against an extensive dataset generated by Mie theory. The promising performance of the present ANN model is demonstrated.