[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117364-en":3,"doc-seo-117364-105":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},117364,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Design of Modified Polymer Membranes Using Machine Learning","Surface modification offers a way to tailor polymer membrane performance, yet the predictive links between modification strategies, processing conditions, and resulting membrane properties are often unclear. This study applies machine learning to datasets describing modified membrane performance, building models that predict key parameters such as pure water permeability and zeta potential. Low prediction errors support generalization to comparable modifications, while the models reveal how substance properties and process parameters affect membrane outcomes. Results indicate that small materials-science datasets can train reliable predictive models, accelerating high-performance membrane development while reducing time and costs.","This article is licensed under CC-BY 4.0   \n[www.acsami.org](www.acsami.org)  Research Article   \nDesign of Modified Polymer Membranes Using Machine Learning  \nSarah Glass, Martin Schmidt, Petra Merten, Amira Abdul Latif, Kristina Fischer, Agnes Schulze, Pascal Friederich, *,\\# and Volkan Filiz *,\\#  \n Cite This: [https://doi.org/10.1021/acsami.3c18805](https://doi.org/10.1021/acsami.3c18805)  \nRead Online  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nDownloaded via KIT BIBLIOTHEK on April 23, 2024 at 09:15:41 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nABSTRACT: Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure−processing−property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The  \npredictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.  \nKEYWORDS: neural network, regression models, electron beam modification, ultrafiltration membrane, surface modification  \n■ INTRODUCTION  \nIndustrial, agricultural, and pharmaceutical production for human needs have created an enormous water demand and stressed water reserves. Therefore, the development of alternative water sources and improved water recycling methods is an urgent task for humanity, 1,2 to achieve the sustainable development goals of the United Nations and to provide clean water for everyone.3 Membrane processes are regarded as a promising technique for the purification of water streams because they often have reasonable recovery rates and low energy demand.4,5 Since polymeric membranes were introduced in water treatment applications in the 1950s, their utilization can be observed for effectively eliminating bacteria, viruses, macromolecules, organic compounds, and salts from contaminated feed streams.6 Therefore, membrane technology is promising in several applications, not only in waste and process water treatment but also in the purification of solventsand gas separation. 1,7 However, the membrane surface properties often limit their performance. Therefore, surface modification is an attractive strategy to customize the  \nproperties of the polymer effects such as fouling8 or performance9 are common  \nmembranes. Reducing unwanted improving the general membrane reasons to modify membranes.  \nThe primary focus of this study was to examine the modification of polymer membranes through the introduction of positively charged amine groups. Amine-modified mem-  \nbranes showed great potential to adsorb and remove toxic metals 10−12 and textile dyes 13, 14 from water. These water contaminants can cause significant harm and serious illness if consumed long term.15 Therefore, the complete elimination of these pollutants from water is necessary. Functionalization of membranes for those specific application","cbCaijMRm6rc8s5g","https://ap.wps.com/l/cbCaijMRm6rc8s5g","pdf",3506993,1,11,"English","en",105,"# Abstract\n# Introduction\n# Machine Learning Approach\n# Membrane Performance Prediction\n# Impact Analysis on Membrane Properties","[{\"question\":\"Why is predicting membrane performance after surface modification challenging?\",\"answer\":\"Predictive structure–processing–property relationships are often unknown, making it difficult to estimate membrane properties before experimental work.\"},{\"question\":\"What membrane performance parameters are predicted using machine learning?\",\"answer\":\"The models predict parameters such as pure water permeability and zeta potential for membranes modified with new substances.\"},{\"question\":\"How do machine learning models help in understanding factors behind membrane properties?\",\"answer\":\"They identify the impact of both substance properties and process parameters on the resulting membrane properties.\"}]","Design of Modified Polymer Membranes Using Machine Learning | PDF",1785675384,28,{"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},"design-of-modified-polymer-membranes-using-machine-learning","",{"@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/design-of-modified-polymer-membranes-using-machine-learning/117364/",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","2026-08-02",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},"Why is predicting membrane performance after surface modification challenging?","Question",{"text":75,"@type":76},"Predictive structure–processing–property relationships are often unknown, making it difficult to estimate membrane properties before experimental work.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What membrane performance parameters are predicted using machine learning?",{"text":80,"@type":76},"The models predict parameters such as pure water permeability and zeta potential for membranes modified with new substances.",{"name":82,"@type":73,"acceptedAnswer":83},"How do machine learning models help in understanding factors behind membrane properties?",{"text":84,"@type":76},"They identify the impact of both substance properties and process parameters on the resulting membrane properties.","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"]