The Icing Process of Water Droplet Postponed on the Zinc-Deposited Steel Surface in a Low-Temperature Environment
Bibliographic record
Abstract
Ice formation and accretion lead to serve problems for power transmission lines, aircraft, wind turbines, and offshore structures. Superhydrophobic surfaces are featured as static contact angle (CA) above 150and low CA hysteresis (CAH, < 10) and present promising anti-icing capability. These surfaces can delay water freezing and reduce ice adhesion. However, the icing process of water droplets is complicated when this process happens on roughened hydrophobic/ superhydrophobic surfaces. Three (super)hydrophobic surfaces were fabricated by electrodepositing a zinc layer on stainless-steel substrates and coating with stearic acid. The primary testing substrates are 30 30 0.8 mm stainless-steel tiles . A Vaniman Problast micro-abrasive sandblaster (Problast-8008, Vaniman Manufacturing) is used for sandblasting as pre-treatment for the stainless-steel tiles. The material of sandblasting media is Al2O3 (White Fused Alumina, Vaniman Manufacturing), and the sizes are in 100 m and 250 m, respectively. Zinc Chloride (ZnCl2) (ACS grade, 97%, Caledon), Ammonium Chloride (NH4Cl) (ACS grade, 99.5%, ACP) and Stearic Acid (95%, Sigma-Aldrich) are used for the electrodeposition. The prepared samples were placed in a cold room, and the temperature was set at -10 1 C, 13 1 C and -15 1C. A syringe pump generates a water droplet of 23.6 1.4 l, and the distance between the needle of the syringe pump and the sample surface is 4.88 0.15 cm. A thermocouple was used to monitor the temperature of initial water droplets. A thermal bath controls the metal surface temperature at -14.73 2.01 C. A high-speed camera (Phantom V611, Vision research) recorded the whole dynamic and icing process of the water droplet. Wthe temperature of the sample approximately equals to the temperature of the environment (at -10 1 C and -15 1 C ), the icing process of the water droplet (the initial droplet temperature is 5.1 0.1C) on sample ED100SS-SA (sandblasted by 100 m Al2O3, then Zinc electrodeposited, followed with the stearic acids coating) can be postponed for more than an hour. After the temperature of the cold room was decreased while maintaining the same temperature of the samples, the icing delay time dropped significantly. Besides, the results show that the freezing rate is proportional to the final contact area between the droplet and the surface. The more hydrophobic surface leads to a smaller final contact area, which causes longer icing delay on the surface.
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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".