Abstract:Artificial neural network has been demonstrated the capability of identifying the chemical composition from various analytical methods. This study tested the feasibility of using a single network to calculate data obtained from polarography, linear scanning voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). Computer generated data were used for network learning and testing. The network property of a single layer network was calculated at various learning cycles, learning rates, and β values o… Show more
“…Moreover, the ion permeability of the membranes was also demonstrated using machine learning, specifically the artificial neural network due to its versatility and high performance. 30,31 The prediction has been succeeded and well correlated with the experimental data, which overcome the limitation on the preparation of thin graphene membranes. The distinct ion rejection behavior of graphene membranes with a wide range of thicknesses was elucidated based on the calculations.…”
This work succeeded in the preparation of graphene-based membranes with ultrahigh stability in water, high salt concentration and seawater, and also studied the machine leaning-based ion permeability.
“…Moreover, the ion permeability of the membranes was also demonstrated using machine learning, specifically the artificial neural network due to its versatility and high performance. 30,31 The prediction has been succeeded and well correlated with the experimental data, which overcome the limitation on the preparation of thin graphene membranes. The distinct ion rejection behavior of graphene membranes with a wide range of thicknesses was elucidated based on the calculations.…”
This work succeeded in the preparation of graphene-based membranes with ultrahigh stability in water, high salt concentration and seawater, and also studied the machine leaning-based ion permeability.
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