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运用人工神经网络反传算法研究了异喹啉及其衍生物电子结构与缓蚀性能之间的关系。以缓蚀剂分子氮净电荷、自由价、亲核前沿电荷密度等量子化学参数为输入变量,考察了异喹啉及其羟基、羧基衍生物在30℃,1.0mo1·dm~(-3)HCl溶液中对铁的缓蚀效率,建立了相应的预测模型。对于研究这类缓蚀剂分子在电极表面的吸附模型和缓蚀行为及定量预测同类新分子的缓蚀性能有一定价值。
The relationship between the electronic structure of isoquinoline and its derivatives and corrosion inhibition performance was studied by artificial neural network back propagation algorithm. The quantum chemical parameters such as net nitrogen molecular charge, free valence number and nucleophilic front charge density of corrosion inhibitor were taken as input variables. The effects of isoquinolone and its hydroxyl and carboxyl derivatives on the activity of 1.0mo1 · dm ~ (-3) HCl solution of iron corrosion inhibition efficiency, the establishment of a corresponding prediction model. For the study of such corrosion inhibitor molecules in the electrode surface adsorption model and corrosion inhibition behavior and quantitative prediction of the corrosion resistance of similar new molecules have some value.