[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123965-en":3,"doc-seo-123965-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123965,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine learning approach for classification of REE/Fe-zeolite catalysts for fenton-like reaction - Article overview","Heterogeneous catalysts combining rare earth elements (REE) with iron supported on zeolites were prepared and evaluated using machine learning. Catalysts were produced by ion exchange or impregnation with lanthanum, praseodymium, or cerium using FAU or MFI zeolite supports, then tested in Fenton-like reactions for degradation of tartrazine and indigo carmine in aqueous solution. Strong performance was observed, with tartrazine degraded by over 80% and indigo carmine up to 95%. Unsupervised clustering and classification using PCA and K-Means, alongside supervised classifiers, were applied to identify patterns and categorize catalyst performance.","Chemical Engineering Science 285 (2024) 119571  \nContents lists available at ScienceDirect Chemical Engineering Science  \njournal [homepage: www.elsevier.com/locate/ces](homepage: www.elsevier.com/locate/ces)  \n| Machine learning approach for classification of REE/Fe-zeolite catalysts for   fenton-like reaction\u003Cbr>´Oscar Barros a, b, *, Pier Parpot a, b, Elisabetta Rombi c, Teresa Tavares a, d, Isabel C. Neves a, b\u003Cbr>a CEB-Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal\u003Cbr>b CQUM, Centre of Chemistry, Chemistry Department, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal c Dipartimento di Scienze Chimiche e Geologiche, University of Cagliari, Complesso Universitario di Monserrato, 09042 Monserrato, Italy d LABBELS – Associate Laboratory, Braga, Guimar˜aes, Portugal |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Rare earth elements Zeolite\u003Cbr>Fenton-like reaction Degradation Machine Learning |  | Various heterogeneous catalysts based on rare earth elements (REE) and iron supported on zeolites were selected and analyzed using machine learning approaches. REE were used in the preparation of multiple REE/Fe-zeolite catalysts with lanthanum, praseodymium or cerium obtained by ion exchange or impregnation methods, using FAU or MFI structures as supports. The efficiency of these REE/Fe-zeolite catalysts was examined in Fenton-like reaction, in the degradation of tartrazine (Tar) and indigo carmine (IC) as selected organic pollutants in the aqueous solution. The REE/Fe-zeolite catalysts demonstrated outstanding performance, with Tar being degraded by over 80% and IC 95%. Machine learning algorithms were employed for clustering and classification of the different catalysts, based on their performance. Unsupervised learning algorithms like Principal Component Analysis and K-Means were used for pattern recognition while supervised classifiers were employed to classify the heterogeneous catalysts, considering their ability to degrade dyes by Fenton reaction. |\n\n1. Introduction  \nRare earth elements (REE) are widely used due to their diversified properties such as optical, electrical, metallurgical, catalytic and magnetic (An et al., 2013; Çelik et al., 2015; Negrea et al., 2018; Unal Yesiller et al., 2013), allowing different daily applications (Balaram, 2019; Rostami et al., 2019; Zhao et al., 2016). Due to the extensive demand and price, their reutilization is important, but the recycling technology is still in the early stages (Barros et al., 2019; Guti´errez-Guti´errez et al., 2015; Lyman, and Palmer, 1993; Otto and Wojtalewicz-Kasprzak, 2014; Yang et al., 2013). The hardship in REE recycling is mainly due to the complexity of the process and to the very different amounts of those elements in the end products, ranging from mg to kg (Binnemans et al., 2013).  \nThe application of REE in catalysis is gaining attention, as there is a strong interest in designing and developing new heterogeneous catalysts, especially sustainable and cost-effective ones (Zheng et al., 2022). The definition of a heterogeneous catalyst loaded with recovered REE can be a key-factor for redox reactions applied in environment rehabilitation. Furthermore, these new catalysts can be applied in advanced oxidation processes (AOP) for wastewater treatment (Giannakis et al.,  \n2015; Miklos et al., 2018; Sievers, 2011; Zheng et al., 2022). Zeolites, which have been reported as suitable supports (Assila et al., 2023; Barros et al., 2019; Mosai et al., 2019; Mosai and Tutu, 2021), are inorganic crystalline microporous aluminosilicates (Li and Yu, 2021; Sable et al., 2021; Xu et al., 2007). They have been used in many catalytic reactions for the production of high-value chemicals (Xu et al., 2007), including Fenton reactions (Gonzalez-Olmos et al., 2012; Sable et al., 2021). The Fenton reaction is a process that utilizes hydrogen peroxide (H 2O2) and iron to degr","cbCaitfeHNLFVA8K","https://ap.wps.com/l/cbCaitfeHNLFVA8K","pdf",3261347,1,13,"English","en",105,"# Introduction\n## Motivation for REE in catalysis and recycling\n## Heterogeneous catalysts with recovered REE and AOP wastewater treatment\n## Role of zeolites and Fenton reaction mechanism\n## Organic dyes as pollutants: tartrazine and indigo carmine\n## Machine learning approach for catalyst analysis","[{\"question\":\"What catalysts were developed in this study?\",\"answer\":\"REE/Fe-zeolite heterogeneous catalysts were prepared using lanthanum, praseodymium, or cerium combined with iron on FAU or MFI zeolite supports via ion exchange or impregnation.\"},{\"question\":\"Which pollutants were tested, and what degradation performance was reported?\",\"answer\":\"Tartrazine and indigo carmine were used as target organic pollutants. Tartrazine degradation exceeded 80%, and indigo carmine reached about 95% under the studied Fenton-like reaction conditions.\"},{\"question\":\"How was machine learning applied to analyze the catalysts?\",\"answer\":\"Unsupervised learning techniques such as PCA and K-Means were used for pattern recognition and clustering, while supervised classifiers were used to classify catalysts based on their dye degradation performance.\"}]","Machine learning approach for classification of REE/Fe-zeolite catalysts for fenton-like reaction - Article overview | PDF",1785819469,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-approach-for-classification-of-reefe-zeolite-catalysts-for-fenton-like-reaction-article-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approach-for-classification-of-reefe-zeolite-catalysts-for-fenton-like-reaction-article-overview/123965/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What catalysts were developed in this study?","Question",{"text":75,"@type":76},"REE/Fe-zeolite heterogeneous catalysts were prepared using lanthanum, praseodymium, or cerium combined with iron on FAU or MFI zeolite supports via ion exchange or impregnation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which pollutants were tested, and what degradation performance was reported?",{"text":80,"@type":76},"Tartrazine and indigo carmine were used as target organic pollutants. Tartrazine degradation exceeded 80%, and indigo carmine reached about 95% under the studied Fenton-like reaction conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"How was machine learning applied to analyze the catalysts?",{"text":84,"@type":76},"Unsupervised learning techniques such as PCA and K-Means were used for pattern recognition and clustering, while supervised classifiers were used to classify catalysts based on their dye degradation performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]