Effect of CuO Nanoparticles in Enhancing the Thermal Conductivities of Monoethylene Glycol and Paraffin Fluids
Bibliographic record
Abstract
The effect of CuO nanoparticles on the thermal conductivities of paraffin and monoethylene glycol (MEG) was investigated. An enhancement in the effective thermal conductivity was found for both fluids. This enhancement was studied with regard to various factors: nanoparticle concentration, nanoparticle size, and base-fluid type. For both base fluids, an improvement in thermal conductivity was found as nanoparticle concentration increased; this was attributed to an increase in particle-to-particle interactions. It was also found that, as the particle size was reduced, there was also an improvement in the thermal conductivities of the fluids. A reduction in nanoparticle size leads to an increase in the Brownian motion of the particles, which also causes more particle-to-particle interactions. The role that the base fluid plays in the observed enhancement is complex. Lower fluid viscosities are believed to contribute to greater enhancement, but a second effect, the interaction of the fluid with the nanoparticle surface, can be even more important. Nanoparticle−liquid suspensions generate a shell of organized liquid molecules on the particle surface. These organized molecules more efficiently transmit energy, via phonons, to the bulk of the fluid. The efficient energy transmission results in enhanced thermal conductivity. The experimentally measured thermal conductivities of the suspensions were compared to a variety of models. None of the models were found to adequately predict the thermal conductivities of the nanoparticle suspensions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".