L-Leucine Propyl Ester–Fatty Acid-Based Pseudo-Protic Ionic Liquids: Synthesis, Extraction Ability, and Ecotoxicity Prediction by Machine Learning

Ainul MAGHFIRAH, Adroit T.N. FAJAR, Rie WAKABAYASHI, Masahiro GOTO
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Abstract

We synthesized low-toxicity L-leucine propyl ester linoleate and L-leucine propyl ester oleate pseudo-protic ionic liquids (ILs) for benign extraction of Ni(II), Co(II), and Mn(II). The extraction ability order for both ILs was Ni(II) > Co(II) > Mn(II). In addition, we developed a machine learning model with an eXtreme Gradient Boosting regressor algorithm to evaluate and predict the ecotoxicity level of the ILs. An evaluation of the proposed regression model by cross-validation indicates that the model is reliable, with an R2 value of 0.71. The prediction results indicate that the newly synthesized ILs are much less toxic than a commercially available IL (methyltrioctylammonium chloride) that is often used for metal extraction.

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基于 L-亮氨酸丙酯-脂肪酸的伪原生离子液体:通过机器学习预测合成、萃取能力和生态毒性
我们合成了低毒性的L-亮氨酸丙酯亚油酸盐和L-亮氨酸丙酯油酸盐伪原生离子液体(ILs),用于良性萃取Ni(II)、Co(II)和Mn(II)。两种离子液体的萃取能力顺序为 Ni(II) > Co(II) > Mn(II) 。此外,我们还利用梯度提升回归算法建立了一个机器学习模型,用于评估和预测惰性离子的生态毒性水平。通过交叉验证对所提出的回归模型进行的评估表明,该模型是可靠的,其 R2 值为 0.71。预测结果表明,新合成的惰性离子的毒性远低于市售的惰性离子(甲基三辛基氯化铵),后者常用于金属萃取。
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