Preparation, characterization, viscosity, and thermal conductivity of nitrogen-doped graphene aqueous nanofluids
Source abstract
Abstract
Nanofluids perform a crucial role in the development of newer technologies ideal for industrial purposes. In this study, Nitrogen-doped graphene (NDG) nanofluids, with varying concentrations of nanoparticles (0.01, 0.02, 0.04, and 0.06 wt%) were prepared using the two-step method in a 0.025 wt% Triton X-100 (as a surfactant) aqueous solution as a base. Stability, zeta potential, thermal conductivity, viscosity, specific heat, and electrical conductivity of nanofluids containing NDG particles were studied. The stability of the nanofluids was investigated by UV–vis over a time span of 6 months and concentrations remain relatively constant while the maximum relative concentration reduction was 20 %. The thermal conductivity of nanofluids was increased with the particle concentration and temperature, while the maximum enhancement was about 36.78 % for a nanoparticle loading of 0.06 wt%. These experimental results compared with some theoretical models including Maxwell and Nan’s models and observed a good agreement between Nan’s model and the experimental results. Study of the rheological properties of NDG nanofluids reveals that it followed the Newtonian behaviors, where viscosity decreased linearly with the rise of temperature. It has been observed that the specific heat of NDG nanofluid reduced gradually with the increase of concentration of nanoparticles and temperature. The electrical conductivity of the NDG nanofluids enhanced significantly due to the dispersion of NDG in the base fluid. This novel type of fluids demonstrates an outstanding potential for use as innovative heat transfer fluids in medium-temperature systems such as solar collectors.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanofluid Flow and Heat Transfer · Solar-Powered Water Purification Methods · Carbon Nanotubes in Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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