Ionic Liquids Curated by Machine Learning for Metal Extraction
Ionic Liquids Curated by Machine Learning for Metal Extraction
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DOI：
10.1021/acssuschemeng.2c03480
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
A. Fajar;A. D. Hartono;Rahman Md Moshikur;M. Goto
中科院分区：
文献类型：
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作者：
A. Fajar;A. D. Hartono;Rahman Md Moshikur;M. Goto
金属是现代设备的关键部件;然而，这些金属的可用资源是有限的。在这项研究中，我们使用机器学习（ML）来策划适合金属提取的离子液体（IL）。我们提出了分类和回归模型，以揭示IL结构及其特定属性之间的隐藏模式，即，金属选择性和生态毒性。使用交叉验证对ML模型进行的评估表明，该模型是可靠的，如准确度评分（0.82）和R2值（0.76）所示。模型还表明，离子液体的金属选择性取决于其阳离子和阴离子结构，其生态毒性水平主要受阳离子结构的影响。在训练模型预测的指导下，我们选择了三种IL（从我们最初提出的150种IL结构中），它们对铂、锂和钕具有提取选择性，并且生态毒性低。然后，我们在实验室中制备离子液体，并通过标准溶剂萃取评估其性能。实验表明，推荐的ML离子液体可以选择性地提取目标金属，具有高提取效率（>80%），这表明ML作为一种有前途的工具包的可行性，可以帮助加速金属提取的创新。
Metals are key components of modern devices; however, available resources of these metals are limited. In this study, we used machine learning (ML) to curate ionic liquids (ILs) that are suitable for metal extraction. We proposed classification and regression models to unravel hidden patterns between IL structures and their specific properties, i.e., metal selectivity and eco-toxicity. Evaluations of ML models using cross-validation indicate that the models were reliable, as described by the accuracy score (0.82) andR2value (0.76). The models also revealed that the metal selectivity of ILs was determined by the cation and anion structures, and the eco-toxicity level was primarily affected by the cation structures. Guided by predictions from the trained models, we selected three ILs (out of the 150 IL structures we initially proposed) that have extraction selectivity toward platinum, lithium, and neodymium as well as low eco-toxicity. We then prepared the ILs in the laboratory and assessed their performance by standard solvent extraction. The experiments indicate that the recommended ILs from ML could selectively extract the targeted metals with high extraction efficiency (>80%), which demonstrates the feasibility of ML as a promising toolkit that can help accelerate innovations in metal extraction.