Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach
Efficient thermal management is required in advanced engineering applications such as energy systems, electronics cooling, and industrial processes. The exceptional thermal properties of graphene quantum dots GQDs combined with the thermoelectric performance of bismuth selenide Bi2Se3 and the high c...
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Elsevier
2025-08-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25004915 |
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| author | Sohail Ahmad Hessa A. Alsalmah |
| author_facet | Sohail Ahmad Hessa A. Alsalmah |
| author_sort | Sohail Ahmad |
| collection | DOAJ |
| description | Efficient thermal management is required in advanced engineering applications such as energy systems, electronics cooling, and industrial processes. The exceptional thermal properties of graphene quantum dots GQDs combined with the thermoelectric performance of bismuth selenide Bi2Se3 and the high conductivity of silver Ag provide significant advancements in heat transfer efficiency and thermal control systems. We explore, in this study, the novel thermal attributes of a ternary nano-composition consisting of GQDs + Bi2Se3+Ag particles suspended in Therminol VP-1. The incorporation of thermal radiation and activation energy offers insights into the temperature-sensitive processes. The analysis covers the features of three types of nano-compositions such as GQDs/Therminol VP-1, GQDs-Bi2Se3/Therminol VP-1 and, GQDs-Bi2Se3-Ag/Therminol VP-1. An order reduction approach is applied to streamline the mathematical modelling and computational efforts while preserving the system's accuracy. The analysis incorporates a machine learning technique based on recurrent neural network (RNN) to evaluate the nonlinear impacts of the physical parameters. The outcomes evidently disclose the fact that the volume concentration Φ2 of bismuth selenide and Φ3 of silver tend to elevate the temperature in usual, hybridized and tri-hybridized cases of nano-compositions. The heat transfer rate increased up to 24.5 % when the volume concentration Φ2 of bismuth selenide and Φ3 of silver increased up to 0.7 and 0.3 respectively. The activation energy AE substantially promoted the concentration in either case of nano-composition e.g., ternary, binary and pure nano-composition case. |
| format | Article |
| id | doaj-art-e48e30ebfe48421f872342850e4d9aba |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-e48e30ebfe48421f872342850e4d9aba2025-08-20T01:51:13ZengElsevierCase Studies in Thermal Engineering2214-157X2025-08-017210623110.1016/j.csite.2025.106231Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approachSohail Ahmad0Hessa A. Alsalmah1Department of Basic Sciences and Humanities, Muhammad Nawaz Sharif University of Engineering and Technology, Multan, 60000, PakistanDepartment of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia; Corresponding author.Efficient thermal management is required in advanced engineering applications such as energy systems, electronics cooling, and industrial processes. The exceptional thermal properties of graphene quantum dots GQDs combined with the thermoelectric performance of bismuth selenide Bi2Se3 and the high conductivity of silver Ag provide significant advancements in heat transfer efficiency and thermal control systems. We explore, in this study, the novel thermal attributes of a ternary nano-composition consisting of GQDs + Bi2Se3+Ag particles suspended in Therminol VP-1. The incorporation of thermal radiation and activation energy offers insights into the temperature-sensitive processes. The analysis covers the features of three types of nano-compositions such as GQDs/Therminol VP-1, GQDs-Bi2Se3/Therminol VP-1 and, GQDs-Bi2Se3-Ag/Therminol VP-1. An order reduction approach is applied to streamline the mathematical modelling and computational efforts while preserving the system's accuracy. The analysis incorporates a machine learning technique based on recurrent neural network (RNN) to evaluate the nonlinear impacts of the physical parameters. The outcomes evidently disclose the fact that the volume concentration Φ2 of bismuth selenide and Φ3 of silver tend to elevate the temperature in usual, hybridized and tri-hybridized cases of nano-compositions. The heat transfer rate increased up to 24.5 % when the volume concentration Φ2 of bismuth selenide and Φ3 of silver increased up to 0.7 and 0.3 respectively. The activation energy AE substantially promoted the concentration in either case of nano-composition e.g., ternary, binary and pure nano-composition case.http://www.sciencedirect.com/science/article/pii/S2214157X25004915Recurrent neural networkGraphene quantum dotsBismuth selenideTernary nano-compositionOrder reduction technique |
| spellingShingle | Sohail Ahmad Hessa A. Alsalmah Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach Case Studies in Thermal Engineering Recurrent neural network Graphene quantum dots Bismuth selenide Ternary nano-composition Order reduction technique |
| title | Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach |
| title_full | Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach |
| title_fullStr | Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach |
| title_full_unstemmed | Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach |
| title_short | Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach |
| title_sort | exploring the thermal attributes of nano composition gqds bi2se3 ag suspended in therminol vp 1 an artificial intelligence based approach |
| topic | Recurrent neural network Graphene quantum dots Bismuth selenide Ternary nano-composition Order reduction technique |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25004915 |
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