Corrosion-Fouling of 316 Stainless Steel and Pure Iron by Hot Oil
Bibliographic record
Abstract
We examined the fouling and corrosion that took place when 316 stainless steel and pure iron wires were electrically heated to 540–680 °C in a liquid bath of the atmospheric bottoms fraction of a crude oil. The foulant was determined to be heterogeneous, with a thick macroscale outer layer of pitch, covering a microscale sheath of coke, which was in turn both covering and interspersed with a microscale layer of iron sulfide. This foulant was observed to delaminate from the wire surface, presumably as a result of both the generation of growth stresses and the action of gas bubbles that were evolved during the fouling process. Unexpectedly but conclusively, we observed that the underlying wire surface was heavily corroded. In the case of the stainless steel, we observed a microscale chromium oxide layer that separated the foulant from the underlying metal. This layer presumably reduced the rate of metal dissolution. The degree of corrosion was much higher in the pure iron samples, where such a layer did not exist. Our hypothesis is that there is a synergy between the measured macroscopic fouling and the underlying microscopic corrosion, where the iron from the wire reacts with the sulfur in the oil to build up the thick sulfide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".