Zinc Treatment Effects on Corrosion Behavior of Alloy 600 in High Temperature, Hydrogenated Water
Bibliographic record
Abstract
Trace levels of soluble zinc(II) ions (30 ppb) maintained in mildly alkaline, hydrogenated water at 260 C were found to reduce the corrosion rate of Alloy 600 (UNS N06600) by about 40% relative to a non-zinc baseline test [2]. Characterizations of the corrosion oxide layer via SEM/TEM and grazing incidence X-ray diffraction confirmed the presence of a chromite-rich oxide phase and recrystallized nickel. The oxide crystals had an approximate surface density of 3500 {micro}m{sup -2} and an average size of 11 {+-} 5 nm. Application of X-ray photoelectron spectroscopy with argon ion milling, followed by target factor analyses, permitted speciated composition vs. depth profiles to be obtained. Numerical integration of the profiles revealed that: (1) alloy oxidation occurred non-selectively and (2) zinc(II) ions were incorporated into the chromite-rich spinel: (Zn{sub 0.55}Ni{sub 0.3}Fe{sub 0.15})(Fe{sub 0.25}Cr{sub 0.75}){sub 2}O{sub 4}. Spinel stoichiometry places the trivalent ion composition in the single phase oxide region, consistent with the absence of the usual outer, ferrite-rich solvus layer. By comparison with compositions of the chromite-rich spinel obtained in the non-zinc baseline test, it is hypothesized that zinc(II) ion incorporation was controlled by the equilibrium for 0.55 Zn{sup 2+}(aq) + (Ni{sub 0.7}Fe{sub 0.3})(Fe{sub 0.3}Cr{sub 0.7}){sub 2}O{sub 4}(s) {r_equilibrium} 0.40 Ni{sup 2+}(aq) + 0.15 Fe{sup 2+}(aq) + (Zn{sub 0.55}Ni{sub 0.3}Fe{sub 0.15})(Fe{sub 0.3}Cr{sub 0.7}){sub 2}O{sub 4}(s). It is estimated that only 8% of the Ni(II) ions generated during non-selective oxidation of the alloy were retained as Ni(II) in the corrosion layer; the remainder either recrystallized to Ni(0) (38%) or were released to the aqueous phase (54%).
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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".