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Modeling of Swelling Behaviors of Acrylamide-Based Polymeric Hydrogels by Intelligent System

机译:基于智能系统的丙烯酰胺基聚合物水凝胶溶胀行为建模

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摘要

Hydrogels based on acrylamide (AAm) were synthesized by free radical polymerization in an aqueous solution using N, N'-methylenebisacrylamide (MBAAm) as crosslinker. To obtain anionic hydrogels, 2-acrylamido-2-methylpropanesulfonic acid sodium salt (AMPS) and acrylic acid (AAc) were used as comonomers. The swelling behaviors of all hydrogel systems were modeled using an artificial neural network (ANN) and compared with a multivariable least squares regression (MLSR) model and phenomenal model. The predictions from the ANN model, which associated input parameters, including the amounts of crosslinker (MBA) and comonomer, and swelling values with time, produce results that show excellent correlation with experimental data. The parameters of swelling kinetics and water diffusion mechanisms of the hydrogels were calculated using the obtained experimental data. Model analysis indicated that the ANN models could accurately describe complex swelling behaviors of highly swellable hydrogels.
机译:使用N,N'-亚甲基双丙烯酰胺(MBAAm)作为交联剂,通过水溶液中的自由基聚合反应合成了基于丙烯酰胺(AAm)的水凝胶。为了获得阴离子水凝胶,将2-丙烯酸酰胺基-2-甲基丙烷磺酸钠盐(AMPS)和丙烯酸(AAc)用作共聚单体。使用人工神经网络(ANN)对所有水凝胶系统的溶胀行为进行建模,并与多变量最小二乘回归(MLSR)模型和现象模型进行比较。来自ANN模型的预测将输入参数(包括交联剂(MBA)和共聚单体的数量)以及溶胀度随时间的变化与输入参数相关联,得出的结果与实验数据具有极好的相关性。利用获得的实验数据计算了水凝胶的溶胀动力学参数和水扩散机理。模型分析表明,人工神经网络模型可以准确描述高溶胀水凝胶的复杂溶胀行为。

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