用作铝合金防腐蚀添加剂的新型咪唑基离子液体:实验、DFT/MD 模拟和软计算相结合的方法

IF 7.5 Q1 CHEMISTRY, PHYSICAL Applied Surface Science Advances Pub Date : 2024-02-01 DOI:10.1016/j.apsadv.2024.100578
Daniel Iheanacho Udunwa , Okechukwu Dominic Onukwuli , Simeon Chukwudozie Nwanonenyi , Chinyere Blessing Ezekannagha
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引用次数: 0

摘要

本研究探讨了新型 1-丁基-3-甲基咪唑鎓四氯茚酸盐离子液体([C4MIM][InCl4] (IL))在 303-343 K 的 1mole (M) 氢氧化钾 (KOH) 电解液中对铝硅钛(Al-Si-Ti)基铝合金的防腐蚀效果。为此,我们采用了失重、电化学研究、密度泛函理论(DFT)/分子动力学模拟(MD-simulation)、扫描电子显微镜(SEM)和扫描力显微镜(SFM)等标准方法,仔细研究了[C4MIM][InCl4]在 KOH 溶液中对铝合金的防腐效果。研究结果表明,离子液体可减轻 Al-Si-Ti 铝合金的腐蚀,随着离子液体浓度的提高,抑制效率(IE%)也随之提高。在 0.8 g/L [C4MIM][InCl4]浓度下,重量法、电位极化(PDP)法和电化学阻抗谱(EIS)法得到的抑制率分别为 88.46%、82% 和 82.35%。电位极化结果表明,[C4MIM][InCl4] 是一种阴极占优势的混合型抑制剂。SEM/SFM 检测证明,离子液体在金属合金表面形成了一层屏蔽层。热力学探究表明,[C4MIM][InCl4] 分子是通过物理吸附机制吸附在 Al-Si-Ti 铝合金表面的,最符合弗鲁姆金吸附等温线模型。DFT/MD 模拟程序证实了 [C4MIM][InCl4]分子在气相和水相的吸附构型和取向与实验结果一致。模拟神经网络(SNN)和自适应神经模糊推理系统(ANFIS)被用于对输入参数和预期反馈的交互影响进行稳健的训练、预测和建模、采用了均方误差 (MSE)、混合分数误差函数 (HYBRID%)、绝对平均相对误差 (AARE)、马夸特百分比标准偏差 (MPSED%) 和 r 平方 (R2) 等统计指标来评估模型的能力。ANN 和 ANFIS 预测的最佳 IE% 分别为 88.4842% 和 89.0643%。根据计算出的 ANN 和 ANFIS 参数的数值,ANFIS 模型因其高精度和鲁棒性而比 ANN 模型更受认可。本研究的结果为系统性腐蚀缓解计划提供了更多信息,并为合理使用 [C4MIM][InCl4] 作为受碱性溶液威胁的 Al-Si-Ti 铝合金的防腐蚀添加剂提供了有用的指导。
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Novel imidazole based ionic liquid as anti-corrosion additive for aluminum alloy: Combined experimental, DFT/MD simulation and soft computing approach

The anti-corrosion effectiveness of novel 1‑butyl‑3-methylimidazolium tetrachloroindate ionic liquid ([C4MIM][InCl4] (IL)) for aluminum-silicon-titanium (Al-Si-Ti) based aluminum alloy in 1mole (M) potassium hydroxide (KOH) electrolyte at 303–343 K was explored in the current study. To realize this, standard methods such as weight loss, electrochemical investigation, density functional theory (DFT)/molecular dynamics simulation (MD-simulation), scanning electron microscope (SEM), and scanning force microscopy (SFM), were employed to scrutinize the anti-corrosion successfulness of [C4MIM][InCl4] for aluminum alloy in KOH solution. From our findings, the ionic liquid mitigated the corrosion of Al-Si-Ti aluminum alloy, and the inhibition efficiency (IE%) is enhanced with improved ionic liquid concentration. The inhibition efficiencies obtained at 0.8 g/L [C4MIM][InCl4] concentration were 88.46%, 82%, and 82.35%, for gravimetric, potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) procedures, respectively. PDP result disclosed [C4MIM][InCl4] performed like a mixed-type inhibitor of a cathodic predominance. The SEM/SFM examination proved that the ionic liquid developed a shield coat on the metal alloy surface. The thermodynamic probe disclosed [C4MIM][InCl4] molecules fastened onto Al-Si-Ti aluminum alloy surface by physisorption mechanism and best fitted the Frumkin adsorption isotherm model. The DFT/MD-simulation procedure confirmed the adsorption configuration and orientation of [C4MIM][InCl4] molecules in gas and aqueous phase which is in harmony with the experimental discovering. Simulated neural network (SNN), and the adaptive neuro-fuzzy inference system (ANFIS) were deployed for a robust training, forecast and modeling of the interactive effects of the input parameters and the expected feedback, Herein, training via the ANN and ANFIS designs without (GA), as well as computing the statistical indices such as the mean squared error (MSE), hybrid fractional error function (HYBRID%), absolute average relative error (AARE), Marquardt's percentage standard deviation (MPSED%) and r-squared (R2) were employed to appraise the models capability. The optimal IE% forecasted was 88.4842% and 89.0643%, for the ANN and ANFIS, respectively. Based on the numerical values of the ANN and ANFIS parameters calculated much acceptance was accorded to the ANFIS model over the ANN due its high degree of precision and robustness. The aftermath of this study furnishes additional information on systematic plan of corrosion mitigation, and proffer useful instructions for the logical use of [C4MIM][InCl4] as anti-corrosion additive for Al-Si-Ti aluminum alloy threatened by alkaline solution.

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1.60%
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66 days
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