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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

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dc.citedby7
dc.contributor.authorKumar K P.en_US
dc.contributor.authorDeepthi Jayan K.en_US
dc.contributor.authorSharma P.en_US
dc.contributor.authorAlruqi M.en_US
dc.contributor.authorid58803258700en_US
dc.contributor.authorid59335934600en_US
dc.contributor.authorid58961316700en_US
dc.contributor.authorid57225072010en_US
dc.date.accessioned2025-03-03T07:48:35Z
dc.date.available2025-03-03T07:48:35Z
dc.date.issued2024
dc.description.abstractRecent 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100606
dc.identifier.doi10.1016/j.flatc.2023.100606
dc.identifier.scopus2-s2.0-85181843324
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85181843324&doi=10.1016%2fj.flatc.2023.100606&partnerID=40&md5=b12bc882f72ed461f436c51e2f19c397
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37201
dc.identifier.volume43
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleFlatChem
dc.titleThermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: Application of explainable ensemble machine learning with hyperparameter optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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