Antifouling Coatings Fabricated by Laser Cladding
Bibliographic record
Abstract
Laser surface treatment is a very useful technology for the fabrication of functional surfaces. In this study, novel antifouling surfaces are fabricated by laser cladding of TC4 and Ni60 mixed materials in various mass ratios on the surfaces of 316L stainless steel substrates. Parametric studies are carried out to investigate the effects of the mixed powder mass ratios and laser cladding parameters on the antifouling performance of the laser clad coatings (LCCs). The antifouling mechanism of the LCCs is investigated by using the water contact angle/surface energy measurement, scanning electron microscope (SEM) surface observation, and phase composition analysis via XRD (X-ray diffractometer) testing. The experimental results show that the LCCs with Ni60/TC4 mass ratio of 3/7 has better antifouling performance in this study. The antifouling performance of the LCC decreases with the increase in laser scanning speed. Surface energy and surface topography have a significant effect on the antifouling performance of LCCs. In order to get the optimal antifouling performance of LCCs, the Ni60/TC4 mass ratio and laser cladding parameters should be optimized.
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 | 0.000 |
| 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".