Core-shell ZnO@TiO2 in water-based nanofluid for enhancing finned tube heat exchanger effectiveness
Bibliographic record
Abstract
This study explores the thermal characteristics and stability of ZnO@TiO2 core-shell nanofluid dispersions as a working fluid in a finned tube heat exchanger. Stability enhancement was achieved by depositing hydrophilic TiO2 nanoparticles onto ZnO nanoparticles, which have higher thermal conductivity, with three variations in deposition amount. The resulting core-shell nanoparticles were dissolved in water at low concentrations (0.025–0.1% by weight) and used in the heat exchanger. The findings revealed that the ZnO@TiO2-2 nanofluid achieved a 10.3-fold improvement in stability compared to uncoated ZnO nanofluid at a 0.1% mass fraction. Despite the improved stability, the thermal conductivity of the nanofluid decreased due to the lower conductivity of the TiO2 layer. Performance evaluation using the LMTD method demonstrated that core-shell nanofluids enhanced the temperature difference in the heat exchanger, thereby increasing the heat transfer rate. ZnO nanofluid achieved the highest heat exchanger effectiveness of 34.2% at the maximum mass fraction. However, ZnO@TiO2 nanofluid, while slightly less effective at 31.7%, reduced the fouling factor due to its more stable dispersion. These findings highlight the potential of ZnO@TiO2 nanofluids to improve heat exchanger performance while addressing stability and fouling issues.
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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) | 0.000 | 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".