Contact angle measurements of liquid lithium on surface-modified stainless steel, insulating materials, and other metals and coatings
Bibliographic record
Abstract
Liquid lithium plasma facing components (PFCs) may provide an attractive alternative to more conventional solid PFCs due to improved plasma performance and the reduction of erosion and wall damage issues. Conceptual designs for liquid lithium divertors have been proposed, but a complete understanding of the interaction between liquid lithium and structural materials will be required for their successful implementation. One aspect of the interaction is the wetting of different materials by liquid lithium at temperatures relevant to fusion applications. Contact angle measurements were used to study the wetting of liquid lithium on 304 stainless steel with varying surface roughnesses, metallic coatings, advanced alloys, and insulating materials in the temperature range from 200 °C to 350 °C. A mirror finish on 304 stainless steel was found to decrease the contact angle and lower the critical wetting temperature while all rougher 304 stainless steel treatments behaved similarly. For thin film coatings and other alloys, the surface roughness was found to impact the wettability more than the change in chemical composition. Compatibility issues with all three insulating materials tested are discussed and limited contact angle data was collected for these samples. • Contact angles measured for lithium wetting on structural materials and insulators. • Compatibility issues between lithium and three tested insulators observed. • Mirror finish treatment on stainless steel decreases wetting temperature. • Surface roughness impacts wettability more than chemical composition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".