Carbon additives to improve polymer performance in energy applications using machine learning
The development of polymer composites enhanced with carbon-based additives has been investigated to reinforce their applicability in energy-related systems. Conductive fillers, such as graphene, Carbon Nano Tubes (CNTs), and Short-cut Carbon Fibers (SCFs), were incorporated into a Polydimethylsiloxa...
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Elsevier
2025-12-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525008976 |
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| author | Juan Chen Khidhair Jasim Mohammed Elimam Ali Riadh Marzouki |
| author_facet | Juan Chen Khidhair Jasim Mohammed Elimam Ali Riadh Marzouki |
| author_sort | Juan Chen |
| collection | DOAJ |
| description | The development of polymer composites enhanced with carbon-based additives has been investigated to reinforce their applicability in energy-related systems. Conductive fillers, such as graphene, Carbon Nano Tubes (CNTs), and Short-cut Carbon Fibers (SCFs), were incorporated into a Polydimethylsiloxane (PDMS) matrix to enhance electrical conductivity and thermal performance. Experimental evaluations included four-probe electrical conductivity testing and self-heating measurements under varied input voltages (8–12 V). Results showed that composites containing 6 mm Carbon Fibers (CF), (Long-CNT) achieved the highest electrical conductivity of 1.8 S/m, significantly outperforming the CNT-only control (Base-CNT, 0.1 S/m). Correspondingly, the Long-CNT samples exhibited the fastest thermal response, with heating time constants (τg) as low as 70.01 s and peak surface temperatures exceeding 150 °C under 12 V input. To guide composite optimization, a hybrid Machine Learning (ML) framework combining Random Forest Regression (RFR) and Support Vector Regression (SVR) was developed. This stacked model was trained on 60 samples and achieved high predictive accuracy across all key outputs, including Coefficient of Determination (R²) = 0.985 for conductivity and R² > 0.95 for heating rate (Hr+c), τg, τd, and temperature. Feature importance analysis revealed that carbon fiber length and input voltage were the dominant factors influencing thermal-electrical performance. The model was also used to simulate untested CF–voltage configurations, identifying optimal engineering windows (e.g., 4.5 mm CF at 11 V) that balanced high heating efficiency with manageable power consumption. The integration of data-driven modeling with experimental validation enabled the accurate prediction and strategic tuning of composite properties. This work provides a scalable framework for designing high-performance, self-healing polymer nanocomposites for thermal management, sensing, and energy conversion applications. |
| format | Article |
| id | doaj-art-b375b6d235594e539797c1c43d907c2c |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-b375b6d235594e539797c1c43d907c2c2025-08-20T02:58:27ZengElsevierCase Studies in Construction Materials2214-50952025-12-0123e0509910.1016/j.cscm.2025.e05099Carbon additives to improve polymer performance in energy applications using machine learningJuan Chen0Khidhair Jasim Mohammed1Elimam Ali2Riadh Marzouki3Guangling college, Yangzhou university, Yangzhou, ChinaAir Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University, Babylon 51001, Iraq; Corresponding author.Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi ArabiaDepartment of Chemistry, College of Science, King Khalid University, P.O. Box 9004, 61413 Abha, Saudi ArabiaThe development of polymer composites enhanced with carbon-based additives has been investigated to reinforce their applicability in energy-related systems. Conductive fillers, such as graphene, Carbon Nano Tubes (CNTs), and Short-cut Carbon Fibers (SCFs), were incorporated into a Polydimethylsiloxane (PDMS) matrix to enhance electrical conductivity and thermal performance. Experimental evaluations included four-probe electrical conductivity testing and self-heating measurements under varied input voltages (8–12 V). Results showed that composites containing 6 mm Carbon Fibers (CF), (Long-CNT) achieved the highest electrical conductivity of 1.8 S/m, significantly outperforming the CNT-only control (Base-CNT, 0.1 S/m). Correspondingly, the Long-CNT samples exhibited the fastest thermal response, with heating time constants (τg) as low as 70.01 s and peak surface temperatures exceeding 150 °C under 12 V input. To guide composite optimization, a hybrid Machine Learning (ML) framework combining Random Forest Regression (RFR) and Support Vector Regression (SVR) was developed. This stacked model was trained on 60 samples and achieved high predictive accuracy across all key outputs, including Coefficient of Determination (R²) = 0.985 for conductivity and R² > 0.95 for heating rate (Hr+c), τg, τd, and temperature. Feature importance analysis revealed that carbon fiber length and input voltage were the dominant factors influencing thermal-electrical performance. The model was also used to simulate untested CF–voltage configurations, identifying optimal engineering windows (e.g., 4.5 mm CF at 11 V) that balanced high heating efficiency with manageable power consumption. The integration of data-driven modeling with experimental validation enabled the accurate prediction and strategic tuning of composite properties. This work provides a scalable framework for designing high-performance, self-healing polymer nanocomposites for thermal management, sensing, and energy conversion applications.http://www.sciencedirect.com/science/article/pii/S2214509525008976Polymer nanocompositesCarbon-based additivesElectrical conductivitySelf-heating performanceMachine learning (ML) ModelingThermal-electrical optimization |
| spellingShingle | Juan Chen Khidhair Jasim Mohammed Elimam Ali Riadh Marzouki Carbon additives to improve polymer performance in energy applications using machine learning Case Studies in Construction Materials Polymer nanocomposites Carbon-based additives Electrical conductivity Self-heating performance Machine learning (ML) Modeling Thermal-electrical optimization |
| title | Carbon additives to improve polymer performance in energy applications using machine learning |
| title_full | Carbon additives to improve polymer performance in energy applications using machine learning |
| title_fullStr | Carbon additives to improve polymer performance in energy applications using machine learning |
| title_full_unstemmed | Carbon additives to improve polymer performance in energy applications using machine learning |
| title_short | Carbon additives to improve polymer performance in energy applications using machine learning |
| title_sort | carbon additives to improve polymer performance in energy applications using machine learning |
| topic | Polymer nanocomposites Carbon-based additives Electrical conductivity Self-heating performance Machine learning (ML) Modeling Thermal-electrical optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525008976 |
| work_keys_str_mv | AT juanchen carbonadditivestoimprovepolymerperformanceinenergyapplicationsusingmachinelearning AT khidhairjasimmohammed carbonadditivestoimprovepolymerperformanceinenergyapplicationsusingmachinelearning AT elimamali carbonadditivestoimprovepolymerperformanceinenergyapplicationsusingmachinelearning AT riadhmarzouki carbonadditivestoimprovepolymerperformanceinenergyapplicationsusingmachinelearning |