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- W3136306127 abstract "Abstract Artificial neural network was introduced for modeling and verifying the relative significance of operational variables to attain the best possible average pore diameter ( D p ) of the mesoporous TiO 2 ‐ZnO nanocrystalline synthesized by microwave‐assisted hydrothermal method. To optimize the system, the autonomous variables calcination temperature and concentrations of D ‐glucose, polyethylene glycol, and Zn‐Ti were employed as input factors, D p was designated as output. D p was achieved from the implementation of the experimental model of the variables by the Barrett‐Joyner‐Halenda method. The network was trained by IBP, BBP, LM, QP, and GA algorithms as a model. The BBP‐4‐7‐1 model provided the optimum numerical properties among four other algorithms. Furthermore, the calcination temperature acted as the most efficient variable whilst D ‐glucose concentration showed the least effect. By validating the microwave‐assisted hydrothermal conditions, in situ polymerization of poly(sodium 4‐styrene sulfonate) was introduced to activate mesopore walls by –SO 3 H functional groups used for ester production by transesterification of used cooking oil, resulting in a methyl ester content of 97.4 % and excellent reusability for ten sequential reactions without any further regeneration." @default.
- W3136306127 created "2021-03-29" @default.
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- W3136306127 date "2021-04-22" @default.
- W3136306127 modified "2023-09-25" @default.
- W3136306127 title "Porosity Estimation of Mesoporous TiO <sub>2</sub> ‐ZnO Nanocrystalline by Artificial Neural Network Modeling" @default.
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- W3136306127 doi "https://doi.org/10.1002/ceat.202000297" @default.
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