Materials Selection for Use in Hydrochloric Acid
Bibliographic record
Abstract
Abstract Hydrochloric acid (HCl) is an important mineral acid with many uses, including the pickling of steel, acid treatment of oil wells and chemical cleaning and processing. This acid is extremely corrosive and its aggressiveness can change drastically depending on its concentration, the temperature and contamination by oxidizing impurities. One of the most commonly encountered oxidizing impurities is the ferric ion. In general, stainless steels cannot tolerate aggressive HCl solutions, hence the need to use corrosion-resistant nickel-based alloys. A part of this study focused on the role of alloying elements on the corrosion performance of commercial nickel-based alloys C-276 (UNS N10276), C-22 (UNS N06022), C-2000 (UNS N06200), Alloy 625 (UNS N06625), Hybrid-BC1 (UNS N10362) and B-3 (UNS N10675)) in HCl solutions, with and without the presence of oxidizing impurities (ferric ions). Aggressive HCl solutions can also be used to simulate the critical crevice solution. Therefore, another aspect of this research is to investigate the role of alloying elements in Nickel-based alloys on the inhibition of crevice corrosion. In the present study various standard corrosion test methodologies, conservative electrochemical techniques, and a range of surface analytical tools have been utilized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".