Paraffin/CuO nanocomposites as phase change materials: Effect of surface modification of CuO
Bibliographic record
Abstract
Abstract In this research, different loadings of copper oxide (CuO) nanoparticles (0.5, 1 and 3 wt%) were utilized to fortify the thermal conductivity of paraffin. In order to improve dispersion and inhibit nanofiller agglomeration, the surface of CuO nanoparticles was treated with trimethoxy octadecyl silane. Surface treatment of CuO nanoparticles was validated by Fourier transform infrared spectroscopy and thermogravimetric analysis. The scanning electron microscopy results revealed that the samples loaded with the surface treated CuO had proper dispersion, even up to 3 wt% of loading. Rheological results showed that CuO inclusion and particularly its treatment increased the viscosity of paraffin. Differential scanning calorimetry results exhibited that latent heat of pure paraffin slightly diminished by inclusion of untreated CuO nanoparticles, while inclusion of the treated nanoparticles led to less reduction in latent heat of paraffin. The thermal conductivity (KD2 test) results revealed that CuO inclusion, particularly the treated CuO, increased the thermal conductivity of paraffin over 2‐fold. All in all, this research study corroborates the potential of the treated CuO nanoparticles to produce paraffin‐based composite phase change materials with improved performance.
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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) | 0.000 | 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) | 0.001 | 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".