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Abstract The use of electrophoretic deposition on a metal surface was investigated for a novel way of predicting the properties of carbon nanotubes and graphene oxide. The selection of neural networks as predictive learning tools is based on many studies in the literature that identify neural networks as accurate interpretations of real-life processes. The use of a neural network model can reduce experiments with unpromising methods of system processing and preparation. The planned method of predictive learning of carbon nanomaterial properties is both simple and cost-effective, according to the results.
Source link: https://doi.org/10.1515/bams-2016-0025
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