Wang, Yong, Ma, Xudong, Robson, Alexander J, Short, Robert D and Bradley, James W 
ORCID: 0000-0002-8833-0180
(2025)
A Hybrid Machine Learning Approach to Predict and Evaluate Surface Chemistries of Films Deposited via APPJ
Plasma Processes and Polymers, 22 (7).
ISSN 1612-8850, 1612-8869
Abstract
ABSTRACTWe 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | deep learning, films, machine learning, plasma polymerization, TEMPO |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 16 May 2025 16:17 |
| Last Modified: | 24 Apr 2026 19:22 |
| DOI: | 10.1002/ppap.70035 |
| Open Access URL: | https://doi.org/10.1002/ppap.70035 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3192802 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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