Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization
Recent research has extensively focused on 2D materials such as graphene oxide (GO) and MXene due to their intriguing properties, significantly advancing nanotechnology and materials research. This experimental study explores the use of a vanadium electrolyte-based hybrid nanofluid (HNF) composed of...
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Elsevier B.V.
2025
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| author | Kumar K P. Deepthi Jayan K. Sharma P. Alruqi M. |
| author2 | 58803258700 |
| author_facet | 58803258700 Kumar K P. Deepthi Jayan K. Sharma P. Alruqi M. |
| author_sort | Kumar K P. |
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| content_provider | Universiti Tenaga Nasional |
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| continent | Asia |
| country | Malaysia |
| description | Recent research has extensively focused on 2D materials such as graphene oxide (GO) and MXene due to their intriguing properties, significantly advancing nanotechnology and materials research. This experimental study explores the use of a vanadium electrolyte-based hybrid nanofluid (HNF) composed of GO and MXene (90:10) to enhance vanadium redox flow batteries (VRFBs). The synthesis and characterization of GO and Mxene nanoparticles (NPs) were conducted using various techniques. The HNF, produced at different weight concentrations, underwent analysis for stability, rheology, thermal conductivity (TC), and electrical conductivity (EC) within a temperature range of 10?45 �C. The results indicate that the HNF exhibits favorable stability and Newtonian behavior in the specified temperature range. At 45 �C, the HNF achieves a maximum enhancement of 20.5 % in EC and 6.81 % in TC for 0.1 wt% compared to the vanadium electrolyte. Subsequently, a prognostic model was developed using an explainable ensemble LSBoost-based machine learning approach, employing a test dataset and applying 5-fold cross-validation to prevent overfitting. Hyperparameter optimization was achieved using the Bayesian technique. The LSBoost-based prognostic models created for TC, EC, and viscosity (VST) demonstrated high effectiveness, with R2 values of 0.9981, 0.99, and 0.9954, respectively. The prediction errors were minimal, with RMSE values of 0.00089255, 5.553, and 0.09391 for the TC, EC, and VST models, respectively. Similarly, the MAE values were low, at 0.00068948, 4.0919, and 0.06129. ? 2023 Elsevier B.V. |
| format | Article |
| id | my.uniten.dspace-37201 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
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| spelling | my.uniten.dspace-372012025-03-03T15:48:36Z Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization Kumar K P. Deepthi Jayan K. Sharma P. Alruqi M. 58803258700 59335934600 58961316700 57225072010 Recent research has extensively focused on 2D materials such as graphene oxide (GO) and MXene due to their intriguing properties, significantly advancing nanotechnology and materials research. This experimental study explores the use of a vanadium electrolyte-based hybrid nanofluid (HNF) composed of GO and MXene (90:10) to enhance vanadium redox flow batteries (VRFBs). The synthesis and characterization of GO and Mxene nanoparticles (NPs) were conducted using various techniques. The HNF, produced at different weight concentrations, underwent analysis for stability, rheology, thermal conductivity (TC), and electrical conductivity (EC) within a temperature range of 10?45 �C. The results indicate that the HNF exhibits favorable stability and Newtonian behavior in the specified temperature range. At 45 �C, the HNF achieves a maximum enhancement of 20.5 % in EC and 6.81 % in TC for 0.1 wt% compared to the vanadium electrolyte. Subsequently, a prognostic model was developed using an explainable ensemble LSBoost-based machine learning approach, employing a test dataset and applying 5-fold cross-validation to prevent overfitting. Hyperparameter optimization was achieved using the Bayesian technique. The LSBoost-based prognostic models created for TC, EC, and viscosity (VST) demonstrated high effectiveness, with R2 values of 0.9981, 0.99, and 0.9954, respectively. The prediction errors were minimal, with RMSE values of 0.00089255, 5.553, and 0.09391 for the TC, EC, and VST models, respectively. Similarly, the MAE values were low, at 0.00068948, 4.0919, and 0.06129. ? 2023 Elsevier B.V. Final 2025-03-03T07:48:35Z 2025-03-03T07:48:35Z 2024 Article 10.1016/j.flatc.2023.100606 2-s2.0-85181843324 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181843324&doi=10.1016%2fj.flatc.2023.100606&partnerID=40&md5=b12bc882f72ed461f436c51e2f19c397 https://irepository.uniten.edu.my/handle/123456789/37201 43 100606 Elsevier B.V. Scopus |
| spellingShingle | Kumar K P. Deepthi Jayan K. Sharma P. Alruqi M. Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization |
| title | Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization |
| title_full | Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization |
| title_fullStr | Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization |
| title_full_unstemmed | Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization |
| title_short | Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimization |
| title_sort | thermo-electro-rheological properties of graphene oxide and mxene hybrid nanofluid for vanadium redox flow battery: application of explainable ensemble machine learning with hyperparameter optimization |
| url_provider | http://dspace.uniten.edu.my/ |