{"title":"L-Leucine Propyl Ester–Fatty Acid-Based Pseudo-Protic Ionic Liquids: Synthesis, Extraction Ability, and Ecotoxicity Prediction by Machine Learning","authors":"Ainul MAGHFIRAH, Adroit T.N. FAJAR, Rie WAKABAYASHI, Masahiro GOTO","doi":"10.15261/serdj.31.31","DOIUrl":null,"url":null,"abstract":"</p><p>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 R<sup>2</sup> 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. </p>\n<p></p>","PeriodicalId":21785,"journal":{"name":"Solvent Extraction Research and Development-japan","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Solvent Extraction Research and Development-japan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15261/serdj.31.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.