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 l...

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Bibliographic Details
Published inChemEngineering Vol. 7; no. 3; p. 41
Main Authors Kireeva, Natalia, Baulin, Vladimir E., Tsivadze, Aslan Yu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Analysis
Batteries
Brines
Cations
Chemical engineering
cheminformatics
Continuous casting
Datasets
Founding
gradient boosting trees
Green technology
Ions
Ligands
Lithium
lithium extraction
Machine learning
Membrane separation
metal complexation
Performance prediction
phosphoryl group
Root-mean-square errors
Selectivity
Sodium
Software
Solvent extraction processes
Stability
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Summary: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.
ISSN:2305-7084
2305-7084
DOI:10.3390/chemengineering7030041