[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118794-en":3,"doc-seo-118794-105":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},118794,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning to Predict the Adsorption Capacity of Microplastics - Article Abstract","Extensive plastic production and degradation enable micro- and nanoplastics to contaminate ecosystems, where they can adsorb chemical pollutants and accelerate their spread while impacting living organisms. To address limited adsorption data, three machine learning models—random forest, support vector machine, and artificial neural network—were developed to predict microplastic/water partition coefficients (log Kd) using two input-variable approximation strategies. The best models achieved query-phase correlation coefficients above 0.92, supporting rapid estimation of organic contaminant adsorption on microplastics.","nanomaterials   \nArticle  \nMachine Learning to Predict the Adsorption Capacity of Microplastics  \nGonzalo Astray 1, Anton Soria-Lopez 1, Enrique Barreiro 2, Juan Carlos Mejuto 1 and Antonio Cid-Samamed 1, *  \nCitation: Astray, G.; Soria-Lopez, A.; Barreiro, E.; Mejuto, J.C.; Cid-Samamed, A. Machine Learning to Predict the Adsorption Capacity of Microplastics. Nanomaterials 2023, 13, 1061. [https://](https://)[ ](https://)[doi.org/10.3390/nano13061061](doi.org/10.3390/nano13061061)  \nAcademic Editor: Chandan Singh  \nReceived: 14 February 2023  \nRevised: 10 March 2023  \nAccepted: 11 March 2023  \nPublished: 15 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Universidade de Vigo, Departamento de Qu½mica F½sica, Facultade de Ciencias, 32004 Ourense, Spain  \n2 Universidade de Vigo, Departamento de Inform¡tica, Escola Superior de Enxeñar½a Inform¡tica,  \n32004 Ourense, Spain  \n* Correspondence: acids@uvigo.es  \nAbstract: Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro-and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log K d) using two different approximations (based on the number of input variables) . The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.  \nKeywords: microplastics; adsorption capacity; machine learning; random forest; support vector machine; artificial neural network; prediction  \n1. Introduction  \nSince the appearance of plastics, their production has grown exponentially in recent decades, and due to their versatility, they are used in different fields, such as packaging, building, or electronic industries, among others [1] . The use of microbeads or nanobeads based on plastic polymers (e.g., Bisphenol-A diglycidyl ether, polyetheramine, and Polyvinyl alcohol) to functionalize and decorate CNTs has been reported to improve their shielding against electromagnetic interference, and the electrical and mechanical properties of elastic, functional composites can also be improved by interlacing the beaded and coated fibers into a smart tissue [2,3] . The transformation of plastics into microplastics (MPs) and then into nanoplastics (NPs) through fragmentation makes the presence of micro-and nanoplastics (MNPs) in both water sources and our planet's agroecosystems a worldwide concern [4–7] . In this sense, and as reported by Matthews et al. (2021), microplastics are plastic fragments of less than 5 mm, and the most commonly accepted size for nanoplastics is that falling within the range of 1–1000 nm [8] .  \nThe cycle from the production of plastics to their entry into the environment includes different stages, as reported by Woods et al. (2021) [9]: production for textile manufacturing and use, tires use, or packaging production, among others [10] . Additionally, there are different pollution sources by MPs; due to this, they can be differentiated between primary and secondary sources [11] . Reg","cbCaipM6IM4iq9NV","https://ap.wps.com/l/cbCaipM6IM4iq9NV","pdf",1209478,1,17,"English","en",105,"# Introduction\n## Plastics, micro- and nanoplastics formation and environmental concern\n## Adsorption of chemical pollutants and partition coefficient (Kd)\n## Need for prediction methods and QSPR + ML approach\n# Machine Learning Models (RF, SVM, ANN)\n## Prediction of log Kd using input-variable approximations","[{\"question\":\"What problem does the study address about microplastics in the environment?\",\"answer\":\"Micro- and nanoplastics can adsorb chemical pollutants in aquatic environments, helping these pollutants disperse more quickly and potentially affect living organisms. The study targets the lack of reliable adsorption data for such processes.\"},{\"question\":\"Which machine learning models are used to predict microplastic adsorption performance?\",\"answer\":\"The study develops three models: random forest, support vector machine, and artificial neural network. These are trained to predict microplastic/water partition coefficients (log Kd).\"},{\"question\":\"How accurate are the best models for adsorption capacity estimation?\",\"answer\":\"In the query phase, the best-selected models generally reach correlation coefficients above 0.92. This indicates the models can support rapid estimation of organic contaminant adsorption on microplastics.\"}]","Machine Learning to Predict the Adsorption Capacity of Microplastics - Article Abstract | PDF",1785720297,43,{"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-to-predict-the-adsorption-capacity-of-microplastics-article-abstract","",{"@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-to-predict-the-adsorption-capacity-of-microplastics-article-abstract/118794/",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","2026-08-03",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 problem does the study address about microplastics in the environment?","Question",{"text":75,"@type":76},"Micro- and nanoplastics can adsorb chemical pollutants in aquatic environments, helping these pollutants disperse more quickly and potentially affect living organisms. The study targets the lack of reliable adsorption data for such processes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used to predict microplastic adsorption performance?",{"text":80,"@type":76},"The study develops three models: random forest, support vector machine, and artificial neural network. These are trained to predict microplastic/water partition coefficients (log Kd).",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the best models for adsorption capacity estimation?",{"text":84,"@type":76},"In the query phase, the best-selected models generally reach correlation coefficients above 0.92. 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