Effects of Acidity and Method of Preparation on Nucleate Pool Boiling of Nanofluids
Why this work is in the frame
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Bibliographic record
Abstract
Past research has shown contradicting trends in the rate of heat transfer during pool boiling of nanofluids, which could be attributed either to their stability or to their method of preparation or to both. An experimental study has been conducted to investigate the effects of electrostatic stabilization and preparation method of nanofluids on their pool boiling rate of heat transfer. Nanofluids made from water and alumina nanoparticles at 0.1 vol% concentration were used. The effect of electrostatic stabilization was investigated by changing the pH value from 6.5, neutral, to 5, acidic. The effect of preparation method has been investigated by using nanofluids prepared from dry particles and from ready-made suspensions. Compared with water, all nanofluids investigated resulted in deterioration in the rate of heat transfer during pool boiling. Neutral nanofluids made from ready-made suspensions and from dry particles resulted into almost the same deterioration in the rate of heat transfer of 49% and 45%, respectively, with respect to that of pure water. The most significant effect of electrostatic stabilization was found in the case of acidic nanofluids made from dry particles, which resulted in deterioration in the rate of heat transfer of 31%. However, acidic nanofluids made from ready-made suspensions resulted in a deterioration of 46%, which is almost the same as that of suspension-made and dry particles-made nanofluids. These results indicate that electrostatic stabilization using acid addition is most effective with nanofluids made from dry particles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it