Wang, Y. orcid.org/0009-0006-4353-9278, Ma, X. orcid.org/0009-0009-8746-656X, Robson, A.J. orcid.org/0000-0002-1449-9477 et al. (2 more authors) (2025) A hybrid machine learning approach to predict and evaluate surface chemistries of films deposited via APPJ. Plasma Processes and Polymers. ISSN 1612-8850
Abstract
We 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). Plasma Processes and Polymers published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | deep learning; films; machine learning; plasma polymerization; TEMPO |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 May 2025 13:28 |
Last Modified: | 19 May 2025 13:28 |
Status: | Published online |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/ppap.70035 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:226807 |