[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127470-en":3,"doc-seo-127470-105":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},127470,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Hybrid Machine Learning Approach to Predict and Evaluate Surface Chemistries of Films Deposited via APPJ - Research Article","A hybrid machine learning framework was built to predict and evaluate plasma polymerization of the TEMPO monomer, with emphasis on nitric oxide (NO) films. The approach integrates artificial neural networks, random forests, and AdaBoost, while genetic algorithm optimization tunes model weights to closely reproduce experimental measurements. TEMPO–helium flow ratio is identified as the most influential factor for surface nitrogen (N) percentage (relative importance 41%), while frequency most strongly affects N–O content (relative importance 30%).","Plasma Processes and Polymers   \n RESEARCH ARTICLE   \nA Hybrid Machine Learning Approach to Predict and Evaluate Surface Chemistries of Films Deposited via APPJ  \nYong Wang1  | Xudong Ma2  | Alexander J. Robson3  | Robert D. Short3  | James W. Bradley1   \n1Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK | 2School of Computer Science, University of Bristol, Bristol,  \nUK | 3Department of Chemistry, University of Sheffield, Sheffield, UK Correspondence: James W. Bradley ([jbradley@liverpool.ac.uk](jbradley@liverpool.ac.uk))  \nReceived: 30 March 2025 | Accepted: 28 April 2025  \nFunding: The authors thank the Engineering and Physical Sciences Research Council (EPSRC) for supporting this work with grants EP/S005153/1 and EP/ S004505/1 . The authors are also thankful to the Chinese Scholarship Council for their support.  \nKeywords: deep learning | films | machine learning | plasma polymerization | TEMPO  \nABSTRACT  \nWe developed a hybrid machine learning model, integrating Artificial Neural Network (ANN), Random Forest (RF) and AdaBoost (AB), to predict and evaluate the plasma polymerization process of TEMPO monomer, specifically for Nitric Oxide films. This model is specifically designed to adeptly navigate the intricate landscape of the plasma polymerization process. Through genetic algorithm optimization, we have fine‐tuned our hybrid model's algorithm weights, achieving results that closely match experimental data. TEMPO‐Helium flow ratio is identified as the most critical parameter for the surface N percentage, with a relative importance of 41% . Frequency has the greatest influence on the N‐O percentage, with a relative importance of 30% . The intertwined influence of different polymerization parameters on the film's surface chemistry has been detailed.  \n1 | Introduction  \nNitric oxide (NO) films find diverse and crucial applications in the biomedical field [1, 2] . These films, capable of controlled NO release, contribute to wound healing by promoting angiogenesis and collagen synthesis while also serving as antimicrobial coatings on medical devices to prevent infections [3] . They act as drug delivery systems for localized therapy, aid in cardiovascular health by vasodilation, and have potential in cancer therapy and neuroprotection [4] . NO‐releasing films extend their utility to respiratory health, dental applications, bioimaging, tissue engineering, and anti‐inflammatory treatments [5–8] . Moreover, they play a role in diabetes management and can be used in various applications to improve overall health outcomes by harnessing the therapeutic properties of nitric oxide while minimizing systemic side effects [9] .  \nIn recent years, atmospheric pressure plasma jets (APPJs) have emerged as versatile tools for thin film deposition with diverse applications. These plasma jets are employed to deposit functional coatings, including antibacterial, hydrophobic, and hydrophilic films, enhancing wear resistance, corrosion protection, and surface properties on a wide range of substrates [10–14] . They find utility in photovoltaic and solar cell production, as well as in flexible electronics, improving energy efficiency and enabling flexible device fabrication [15–17] . APPJs are instrumental in modifying polymer surfaces, producing optical and membrane coatings, and enhancing biomedical devices [18, 19] . They also contribute to gas barrier films for packaging, gas sensors, and energy storage applications, demonstrating their broad impact across industries through precise and controlled film deposition processes [17, 20, 21] . However, the creation and  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s) . Plasma Processes and Polymers published by Wiley‐VCH GmbH.  \nPlasma Processes and Polymers, 2025; 22:e70035 1 of ","cbCairsBdYcoG9g5","https://ap.wps.com/l/cbCairsBdYcoG9g5","pdf",2595959,1,11,"English","en",105,"# Introduction\n## Nitric oxide films and biomedical applications\n## Atmospheric pressure plasma jets (APPJs) for thin film deposition\n## Need for parameter optimization and characterization\n## Machine learning for labeled experimental data\n## Hybrid model design and feature importance","[{\"question\":\"What hybrid machine learning methods were integrated in the model?\",\"answer\":\"The model combines Artificial Neural Network (ANN) with Random Forest (RF) and AdaBoost (AB).\"},{\"question\":\"How was the hybrid model optimized to improve agreement with experiments?\",\"answer\":\"Genetic algorithm optimization was used to fine-tune the hybrid model’s algorithm weights to closely match experimental data.\"},{\"question\":\"Which plasma polymerization parameters were most important for surface chemistry?\",\"answer\":\"TEMPO–Helium flow ratio most strongly influences surface N percentage (41% relative importance), while frequency most strongly impacts N–O percentage (30% relative importance).\"}]","A Hybrid Machine Learning Approach to Predict and Evaluate Surface Chemistries of Films Deposited via APPJ - 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