Adhesion of tin droplets impinging on a stainless steel plate: effect of substrate temperature and roughness
Bibliographic record
Abstract
We photographed impact of small tin droplets on stainless steel surfaces of varying temperature and roughness. To achieve high impact velocities the test surfaces were mounted on the rim of a rotating fly wheel. Substrate temperature (Ts) was varied from 120 to 220 °C and surface roughness (Ra) kept at either 0.05 or 2 µm. We kept constant the impact velocity (30 m/s) and droplet diameter (0.6 mm). To form a coating 60 droplets were deposited randomly on each stainless steel test coupon. Deposition efficiency was evaluated by dividing the mass adhering to the coupon by the mass of sixty droplets prior to impact. The maximum deposition efficiency was achieved at a substrate temperature of 160 °C. For Ts < 160 °C the deposition efficiency was higher on a rough surface (Ra = 2 µm) than on a smooth surface (Ra = 0.05 µm), since splats did not adhere well to the smooth surface. For Ts≥ 160 °C the deposition efficiency was higher on a smooth surface (Ra = 0.05 µm) than on a rough surface (Ra = 2 µm), since splats splashed less on the smooth surface.© 2003 Elsevier Science Ltd. All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".