Surface modification of carbon nanotubes with copper oxide nanoparticles for heat transfer enhancement of nanofluids
Bibliographic record
Abstract
Over the last few years, nanoparticles have been used as thermal enhancement agents in many heat transfer based fluids to improve the thermal conductivity of the fluids. Recently, many experiments have been carried out to prepare different types of nanofluids (NFs) showing a tremendous increase in thermal conductivity of the base fluids with the addition of a small amount of nanoparticles. However, little experimental work has been proposed to calculate the flow behaviour and heat transfer of nanofluids and the exact mechanism for the increase in effective thermal conductivity in heat exchangers. This study mainly focuses on the development of nanomaterial composites by incorporating copper oxide nanoparticles (CuO) onto the surfaces of carbon nanotubes (CNTs). The CNT-CuO nanocomposite was used to prepare water-based heat transfer NFs. The morphological surfaces and loading contents of the CNT-CuO nanocomposite were characterized using field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA) while the physical and thermal properties of the water-based nanofluids were characterized using differential scanning calorimetry (DSC), the Mathis TCi system and a viscosity meter for measuring the heat capacity, thermal conductivity and viscosity of the synthesized NFs, respectively. The heat transfer and the pressure drop studies of the NFs were conducted by a horizontal steel tube counter-flow heat exchanger under turbulent flow conditions. The experimental results showed that the developed NFs with different concentrations of modified CNTs (0.01, 0.05 and 0.1 wt%) have yielded a significant increase in specific heat capacity (102% higher than pure water) and thermal conductivity (26% higher than pure water) even at low concentration. The results also revealed that the heat rate of the NF was higher than that of the base liquid (water) and increased with increasing the concentration of nanoparticles. Furthermore, no significant effect of the nanoparticles on the pressure drop of the system was observed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".