[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128531-en":3,"doc-seo-128531-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128531,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future","This review illuminates the pivotal synergy between machine learning and biobased polymers, highlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers from renewable sources are presented for their roles across energy and fuel applications. When integrated with machine learning, these polymers show improved functionality, supporting optimization of renewable energy systems, storage, and conversion. Case studies also connect the intersection to biochemical production advances, including drug delivery and medical devices, reinforcing future sustainability.","Scotland's Rural College  \nAdvances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future  \nBin Abu Sofian, Abu Danish Aiman; Sun, Xun; Gupta, Vijai Kumar; Berenjian, Aydin; Xia, Ao; Ma, Zengling; Show, Pau Loke  \nPublished in:  \nEnergy & Fuels  \nDOI:  \n10.1021/acs.energyfuels.3c03842  \nPrint publication: 01/02/2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication  \nCitation for pulished version (APA):  \nBin Abu Sofian, A. D. A. , Sun, X. , Gupta, V. K. , Berenjian, A. , Xia, A. , Ma, Z. , & Show, P. L. (2024) . Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals fora Sustainable Future. Energy & Fuels, 38(3), 1593-1617 . [https://doi.org/10.1021/acs.energyfuels.3c03842](https://doi.org/10.1021/acs.energyfuels.3c03842)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 20. Sep. 2025  \nThis article is licensed under CC-BY 4.0   \n[pubs.acs.org/EF](pubs.acs.org/EF)  Review   \nAdvances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future  \nAbu Danish Aiman Bin Abu Sofian, Xun Sun, Vijai Kumar Gupta, Aydin Berenjian, Ao Xia, Zengling Ma, * and Pau Loke Show*  \n Cite This: [https://doi.org/10.1021/acs.energyfuels.3c03842](https://doi.org/10.1021/acs.energyfuels.3c03842)  \nRead Online  \nDownloaded via SCOTLANDS RURAL COLL on January 3 1, 2024 at 10:17:56 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \nABSTRACT: This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for their roles in sustainable energy and fuel sectors. These polymers, when integrated with ML techniques, exhibit enhanced functionalities, optimizing renewable energy systems, storage, and conversion. Detailed case studies reveal the potential of biobased polymers in energy applications and the fuel industry, further showcasing how ML bolsters fuel efficiency and innovation. The intersection of biobased polymers and ML also marks advancements in biochemical production, emphasizing innovations in drug delivery and medical device development. This review underscores the imperative of harnessing the convergence of ML and biobased polymers for future global sustainability endeavors in energy, fuels, and biochemicals. The collective evidence presented asserts the immense promise this union holds for steering a sustainable and innovative trajectory.  \n1. INTRODUCTION  \nIn the quest for a sustainable future, numerous challenges have been encountered.1,2 Global reliance on nonrenewable energy sources has exacted a significant toll on the environment.3−5 Traditional materials, primarily derived from finite resources, exacerbate the environmental burden, contributing to pollution and resource depletion.","cbCaiaRWUvJXaIuO","https://ap.wps.com/l/cbCaiaRWUvJXaIuO","pdf",2927200,1,26,"English","en",105,"# Introduction\n## Biobased polymers as renewable alternatives\n## Machine learning methods and their role\n## Rationale for combining ML with biobased polymers","[{\"question\":\"What is the focus of the review?\",\"answer\":\"The review focuses on the synergy between machine learning and biobased polymers and how their combination can advance sustainable energy, fuels, and biochemicals.\"},{\"question\":\"How do biobased polymers contribute to sustainable energy and fuels?\",\"answer\":\"Biobased polymers derived from renewable biomass are discussed for their functional roles in sustainable energy and fuel sectors, including energy applications and fuel industry relevance.\"},{\"question\":\"What impact does integrating machine learning have on biobased polymer applications?\",\"answer\":\"Integrating machine learning is described as improving biopolymer functionality and enabling optimization of renewable energy systems, storage, and conversion, while also supporting innovation in biochemical production.\"}]","Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future | 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