基于机器学习的含磷配体Li+和Na+金属络合萃取盐水中Li+的研究

IF 2.8 Q2 ENGINEERING, CHEMICAL ChemEngineering Pub Date : 2023-05-01 DOI:10.3390/chemengineering7030041
N. Kireeva, V. Baulin, A. Tsivadze
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引用次数: 1

摘要

随着锂(Li)资源高需求技术的发展,需要技术解决方案来获得锂供应,以保持工艺的可持续性。本研究的目的是使用基于机器学习的搜索含磷的podandic配体,可能选择性地从盐水中提取锂。基于4∶1 THF:CHCl3条件下含Li+和Na+阳离子的含磷有机配体稳定性常数的实验数据,提出了候选双聚体配体,并利用机器学习方法(ML)评估了Li+和Na+的稳定性常数(logK)以及相应的选择性值。模型对Li+和Na+阳离子的稳定性常数值以及Li+/Na+选择性值的预测,其决定系数R2分别为0.75、0.87和0.83,均方根误差RMSE分别为0.485、0.449和0.32,具有较好的预测效果。利用HostDesigner软件对金属配体1:1配合物的主客体互补性进行初步估计,补充了基于ml的分析。
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A Machine Learning-Based Study of Li+ and Na+ Metal Complexation with Phosphoryl-Containing Ligands for the Selective Extraction of Li+ from Brine
The growth of technologies concerned with the high demand in lithium (Li) sources dictates the need for technological solutions garnering Li supplies to preserve the sustainability of the processes. The aim of this study was to use a machine learning-based search for phosphoryl-containing podandic ligands, potentially selective for lithium extraction from brine. Based on the experimental data available on the stability constant values of phosphoryl-containing organic ligands with Li+ and Na+ cations at 4:1 THF:CHCl3, candidate di-podandic ligands were proposed, for which the stability constant values (logK) with Li+ and Na+ as well as the corresponding selectivity values were evaluated using machine learning methods (ML). The modelling showed a reasonable predictive performance with the following statistical parameters: the determination coefficient R2= 0.75, 0.87 and 0.83 and root-mean-square error RMSE = 0.485, 0.449 and 0.32 were obtained for the prediction of the stability constant values with Li+ and Na+ cations and Li+/Na+ selectivity values, respectively. This ML-based analysis was complemented by the preliminary estimation of the host–guest complementarity of metal–ligand 1:1 complexes using the HostDesigner software.
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来源期刊
ChemEngineering
ChemEngineering Engineering-Engineering (all)
CiteScore
4.00
自引率
4.00%
发文量
88
审稿时长
11 weeks
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