Fengyi Zhou , Dingyi Shi , Wenbo Mu , Shao Wang , Zeyu Wang , Chenyang Wei , Ruiqi Li , Tiancheng Mu
{"title":"Machine learning models accelerate deep eutectic solvent discovery for the recycling of lithium-ion battery cathodes†","authors":"Fengyi Zhou , Dingyi Shi , Wenbo Mu , Shao Wang , Zeyu Wang , Chenyang Wei , Ruiqi Li , Tiancheng Mu","doi":"10.1039/d4gc01418a","DOIUrl":null,"url":null,"abstract":"<div><p>Deep eutectic solvents (DESs) have been widely applied to recover spent lithium-ion batteries (LIBs); however, developing effective and efficient systems for cathode leaching <em>via</em> the traditional trial-and-error method requires substantial efforts. This work aims to accelerate the discovery of novel promising DESs by leveraging the conditional Generative Adversarial Network (CGAN). Three databases were constructed: (i) DESs leaching cathodes, (ii) DESs leaching metal oxides, and (iii) DES properties. The absolute Spearman's rank correlation and agglomerative hierarchical clustering analysis ensured the selection of an optimal feature set for building predictive models. An XGBoost model was developed, achieving remarkable performance (<em>R</em><sup>2</sup> = 0.9702, MSE = 0.0007) in predicting cathode solubility in DESs. We employed the Shapley additive explanation (SHAP) method to quantify the importance of acidity, coordination, and reducibility of DESs and provide insights into further research. To accelerate time-consuming investigational procedures, a CGAN model was established, rapidly identifying promising DESs like ChCl : Glycolic acid, with excellent agreement between predictions and experimental results. This study offers a general data analysis framework for other metal oxides (<em>e.g.</em>, Cu<sub>x</sub>O, Fe<sub>x</sub>O<sub>y</sub>, ZnO) leaching using DESs, enabling accurate solubility prediction and deepening the understanding of cathode leaching mechanisms. The CGAN model significantly accelerates the development of a DES-based process for lithium-ion cathode recycling, saving development time and effort. Overall, this work facilitates the efficient discovery and development of effective DESs for the recovery of valuable metals from spent LIB cathodes.</p></div>","PeriodicalId":78,"journal":{"name":"Green Chemistry","volume":null,"pages":null},"PeriodicalIF":9.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Green Chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S146392622400596X","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Deep eutectic solvents (DESs) have been widely applied to recover spent lithium-ion batteries (LIBs); however, developing effective and efficient systems for cathode leaching via the traditional trial-and-error method requires substantial efforts. This work aims to accelerate the discovery of novel promising DESs by leveraging the conditional Generative Adversarial Network (CGAN). Three databases were constructed: (i) DESs leaching cathodes, (ii) DESs leaching metal oxides, and (iii) DES properties. The absolute Spearman's rank correlation and agglomerative hierarchical clustering analysis ensured the selection of an optimal feature set for building predictive models. An XGBoost model was developed, achieving remarkable performance (R2 = 0.9702, MSE = 0.0007) in predicting cathode solubility in DESs. We employed the Shapley additive explanation (SHAP) method to quantify the importance of acidity, coordination, and reducibility of DESs and provide insights into further research. To accelerate time-consuming investigational procedures, a CGAN model was established, rapidly identifying promising DESs like ChCl : Glycolic acid, with excellent agreement between predictions and experimental results. This study offers a general data analysis framework for other metal oxides (e.g., CuxO, FexOy, ZnO) leaching using DESs, enabling accurate solubility prediction and deepening the understanding of cathode leaching mechanisms. The CGAN model significantly accelerates the development of a DES-based process for lithium-ion cathode recycling, saving development time and effort. Overall, this work facilitates the efficient discovery and development of effective DESs for the recovery of valuable metals from spent LIB cathodes.
期刊介绍:
Green Chemistry is a journal that provides a unique forum for the publication of innovative research on the development of alternative green and sustainable technologies. The scope of Green Chemistry is based on the definition proposed by Anastas and Warner (Green Chemistry: Theory and Practice, P T Anastas and J C Warner, Oxford University Press, Oxford, 1998), which defines green chemistry as the utilisation of a set of principles that reduces or eliminates the use or generation of hazardous substances in the design, manufacture and application of chemical products. Green Chemistry aims to reduce the environmental impact of the chemical enterprise by developing a technology base that is inherently non-toxic to living things and the environment. The journal welcomes submissions on all aspects of research relating to this endeavor and publishes original and significant cutting-edge research that is likely to be of wide general appeal. For a work to be published, it must present a significant advance in green chemistry, including a comparison with existing methods and a demonstration of advantages over those methods.