Experimental study of nucleate pool boiling heat transfer of self‐rewetting solution by surface functionalization with TiO<sub>2</sub> nanostructure
Bibliographic record
Abstract
A titanium plate with titanium dioxide nanotube was prepared with the anodic oxidation process. The heat transfer through this plate was studied with quantitative heptanol solution as self‐rewetting solution. The microstructure of this nano‐structure surface was characterized by SEM and the surface tension of the self‐rewetting solution on it was measured by the static contact angle. The results indicated that the heat transfer coefficient (HTC) and the critical heat flux (CHF) were significantly improved with the increasing concentration of heptanol. In the 0.1 mass% heptanol solution, the best heat transfer enhancement effect was obtained. The HTC is 22.46 kW · m−2 · K−1 and CHF is 536.64 kW · m−2, which were increased by 121 and 62.9 % more than those in the distilled water. According to the experimental phenomenon and mechanism analysis, the surface of the nanotube has a more effective vaporization core, greater roughness, and better wettability. Combined with the special double surface tension gradient characteristic of the self‐wetting solution, the hot and cold liquid forms a microcirculation and the synergistic superposition of the driving force strengthens. The system can produce tiny and fine bubbles under low superheat, detach quickly, transfer heat quickly, and perform secondary wetting in time to achieve synergistic enhanced boiling heat transfer.
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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) | 0.002 | 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".