Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique
The impedance characteristics of multi-walled carbon nanotube (MWCNT)/polystyrene nanocomposites synthesized via microwave-assisted in-situ polymerization were systematically investigated to determine the effects of microwave power, exposure time, and frequency on impedance properties. The Taguchi m...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024018425 |
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| author | Shohreh Jalali Majid Baniadam Morteza Maghrebi |
| author_facet | Shohreh Jalali Majid Baniadam Morteza Maghrebi |
| author_sort | Shohreh Jalali |
| collection | DOAJ |
| description | The impedance characteristics of multi-walled carbon nanotube (MWCNT)/polystyrene nanocomposites synthesized via microwave-assisted in-situ polymerization were systematically investigated to determine the effects of microwave power, exposure time, and frequency on impedance properties. The Taguchi method and analysis of variance (ANOVA) identified microwave power as the most significant factor, followed by exposure duration and frequency. A predictive model was developed, demonstrating high accuracy with a coefficient of determination (R²) of 0.96 between model predictions and experimental results. Additionally, response surface methodology (RSM) and contour plots were applied to explore optimal parameter combinations, offering valuable insights for achieving tailored impedance values. Machine learning model including Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boost (CatBoost), and Light Gradient-Boosting Machine (LightGBM) were employed to enhance predictive capabilities. Among these, Random Forest and CatBoost demonstrated superior accuracy, achieving R² values of 0.9880 and 0.9811 on testing data, respectively, while Decision Tree and LightGBM exhibited lower performance. This study highlights the potential of machine learning methods to precisely adjust and tailor impedance properties of PS/CNT nanocomposites, supporting the engineering of materials for diverse applications across materials science and engineering. |
| format | Article |
| id | doaj-art-c0ef0bcb31144cdda20763c63a29e4a5 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-c0ef0bcb31144cdda20763c63a29e4a52024-12-19T11:00:05ZengElsevierResults in Engineering2590-12302024-12-0124103599Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi techniqueShohreh Jalali0Majid Baniadam1Morteza Maghrebi2Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, IranDepartment of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, IranCorresponding author.; Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, IranThe impedance characteristics of multi-walled carbon nanotube (MWCNT)/polystyrene nanocomposites synthesized via microwave-assisted in-situ polymerization were systematically investigated to determine the effects of microwave power, exposure time, and frequency on impedance properties. The Taguchi method and analysis of variance (ANOVA) identified microwave power as the most significant factor, followed by exposure duration and frequency. A predictive model was developed, demonstrating high accuracy with a coefficient of determination (R²) of 0.96 between model predictions and experimental results. Additionally, response surface methodology (RSM) and contour plots were applied to explore optimal parameter combinations, offering valuable insights for achieving tailored impedance values. Machine learning model including Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boost (CatBoost), and Light Gradient-Boosting Machine (LightGBM) were employed to enhance predictive capabilities. Among these, Random Forest and CatBoost demonstrated superior accuracy, achieving R² values of 0.9880 and 0.9811 on testing data, respectively, while Decision Tree and LightGBM exhibited lower performance. This study highlights the potential of machine learning methods to precisely adjust and tailor impedance properties of PS/CNT nanocomposites, supporting the engineering of materials for diverse applications across materials science and engineering.http://www.sciencedirect.com/science/article/pii/S2590123024018425Carbon nantoubesPolymer nanocompositesImpedance characteristicsMicrowave-assisted in-situ polymerizationMachine learningTaguchi method |
| spellingShingle | Shohreh Jalali Majid Baniadam Morteza Maghrebi Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique Results in Engineering Carbon nantoubes Polymer nanocomposites Impedance characteristics Microwave-assisted in-situ polymerization Machine learning Taguchi method |
| title | Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique |
| title_full | Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique |
| title_fullStr | Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique |
| title_full_unstemmed | Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique |
| title_short | Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique |
| title_sort | impedance value prediction of carbon nanotube polystyrene nanocomposites using tree based machine learning models and the taguchi technique |
| topic | Carbon nantoubes Polymer nanocomposites Impedance characteristics Microwave-assisted in-situ polymerization Machine learning Taguchi method |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024018425 |
| work_keys_str_mv | AT shohrehjalali impedancevaluepredictionofcarbonnanotubepolystyrenenanocompositesusingtreebasedmachinelearningmodelsandthetaguchitechnique AT majidbaniadam impedancevaluepredictionofcarbonnanotubepolystyrenenanocompositesusingtreebasedmachinelearningmodelsandthetaguchitechnique AT mortezamaghrebi impedancevaluepredictionofcarbonnanotubepolystyrenenanocompositesusingtreebasedmachinelearningmodelsandthetaguchitechnique |