Interactions Between Molten Metal Splats Landing on a Substrate during Coating Formation
Bibliographic record
Abstract
Abstract Interactions between multiple splats landing on a substrate was studied experimentally by photographing deformations of droplets as they land and freeze on the substrate, or previously solidified splats. Uniform-size molten tin droplets (550 µm diameter) were produced using a drop-on-demand generator. To achieve high impact velocities the stainless steel coupons used as substrates were mounted on the rim of a rotating flywheel and heated using cartridge heaters to vary substrate temperature. To hit a falling droplet with the substrate and photograph its impact, a timing circuit was used to synchronize the ejection of a droplet, triggering of the camera and a flash to provide illumination. The substrate temperature and substrate roughness significantly affected splat impact dynamics. Droplets hitting a smooth cold substrate splashed extensively whereas those hitting a hot substrate spread in the form of a smooth disc. The final splat shapes were dependent on the offset distance between the impacting droplet and the previously solidified splat. The size of fingers around the splat edge increased with the offset distance. Large pieces of metal detached from the droplet rim when the droplet hit a rough substrate whereas droplets hitting previously solidified splats splashed in a star-like shape with extremely long fingers.
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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 | 0.001 | 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".