Corrosion of 316L Stainless Steel in Eutectic KCl-LiCl under a Thermal Gradient Using a Natural Circulation Loop
Bibliographic record
Abstract
Abstract This study investigates the corrosion behavior of 316L stainless steel in KCl-LiCl molten salt using a natural circulation loop. The loop was operated for 144 hours with a maximum temperature of 600°C (hot leg) and a minimum temperature of 450°C (cold leg). At the end of the test, the salt was allowed to solidify within the loop and the loop was sectioned into small coupons to examine the corrosion behavior at various locations. The salt chemistry was analyzed at corresponding positions to correlate with the observed corrosion damage. Material characterization of extracted coupons revealed distinct corrosion behaviors between the hot and cold legs. In the hot leg, intergranular corrosion was the predominant mechanism, with preferential dissolution of Fe, Cr, and Mn. Additionally, deposition of Cr-rich oxide was observed in the hot leg. In contrast, the cold leg exhibited metallic deposits on the surface. These findings highlight the influence of the temperature gradient and salt chemistry on corrosion processes in molten salts. The underlying corrosion mechanisms are discussed, focusing on the role of thermal gradients and salt composition in modulating corrosion rates and morphology. The results of this work have implications for materials selection and system design in high-temperature molten-salt loops.
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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".