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Optimizing MXene graphene based fluids for solar energy conversion and storage using a novel intelligent framework.

Mohamed Bechir Ben Hamida1, Ali Basem2, Ala Eldin A Awouda3

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Optimizing graphene/MXene nanofluids for solar energy is now more efficient. A hybrid framework precisely enhances thermal conductivity and dynamic viscosity, reducing costs for solar applications.

Keywords:
Energy efficiencyGrapheneMXeneMulti-objective optimizationResponse surface methodologySolar energy

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Renewable Energy

Background:

  • Graphene/MXene-based fluids offer potential for solar energy systems.
  • Optimizing their thermophysical properties is complex.
  • Enhancing thermal conductivity (TC) and dynamic viscosity (DV) is crucial.

Purpose of the Study:

  • To develop a hybrid framework for optimizing Graphene/MXene nanofluid properties.
  • To enhance thermal conductivity (TC) and dynamic viscosity (DV) for solar energy applications.
  • To provide a cost-effective and precise methodology.

Main Methods:

  • Response Surface Methodology (RSM) for predictive modeling.
  • Heuristic and metaheuristic optimization algorithms (EHC, NSGA-II, MOALO).
  • Decision-making techniques (Desirability Function, VIKOR).

Main Results:

  • RSM models showed high accuracy (R² > 0.998).
  • Optimal conditions identified: ~60°C, 1.5-2 wt% MF, 0.47-0.5 MXene ratio.
  • Decision-making analysis revealed TC/DV trade-offs based on weight distribution.

Conclusions:

  • The hybrid framework effectively optimizes nanofluid properties.
  • Optimal MXene ratios are dependent on mass fraction and temperature.
  • This approach reduces computational and laboratory costs for solar energy applications.