Corrosion Behavior of Structural Materials for Potential Use in Nitrate Salts Based Solar Thermal Power Plants
Bibliographic record
Abstract
Commercial and economic success of concentrated solar power (CSP) plants requires operating at maximum efficiency and capacity which necessitates the use of materials that are reliable at high temperatures. This study investigates the corrosion behavior of structural alloys in molten nitrate salts at three temperatures common to CSP plants. Corrosion behavior was evaluated using gravimetric and inductively-coupled plasma optical emission spectroscopy (ICP-OES) analysis. Surface oxide structure and chemistry was characterized using X-ray diffraction and Raman spectroscopy. Electrochemical behavior of candidate structural alloys Alloy 4130, austenitic stainless steel 316, and super-austenitic Incoloy 800H was evaluated using potentiodynamic polarization characteristics. Gravimetric and ICP-OES analysis indicated that Alloy 4130 exhibited the least corrosion resistance at 500°C compared to SS316 and 800H. However, at 300°C, the three alloys exhibited similar weight gain. Electrochemical evaluation of these candidate materials was observed to correlate well with the corrosion behavior observed from gravimetric and ICP-OES analysis. This study identifies that all three alloys exhibited acceptable corrosion rate in 300°C molten salt, while elevated salt temperatures require the more corrosion resistant alloys, stainless steel 316 and 800H. Characterization of the sample surfaces revealed the presence of spinels at lower temperatures, while Fe 2 O 3 was the dominant iron oxide at higher temperatures for each alloy.
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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".