Effect of Adherent Bacteria on Microbially Influenced Corrosion (MIC) of Stainless Steel Welded Joints.
Bibliographic record
Abstract
The failure cases in plants by microbially influenced corrosion (MIC) of stainless steels were often observed around the welded joints. It is significant to understand the underlying reasons that influence the susceptibility of weld regions of stainless steels to MIC. Experiments were carried out to investigate the effect of the shape of the weld bead on the adhesion of bacteria and the occurrence of pitting corrosion. Coupon exposure studies were conducted using Type 304 stainless steel including the weld bead with TIG using type 308 filler. Coupons were immersed in the bacterial culture medium and then the bacteria (Methylobacterium sp. or Bacillus sp.) were inoculated. After a fixed period of incubation, the bacteria adhered on the surface of the specimen were observed by epifluorescence microscope. In the case of reciprocal shaking condition, the adhesion area of bacteria at HAZ and the toe region were more than those at base metal and top of weld region. In the case of non-shaking condition, the bacteria were adhered uniformly at base metal, HAZ, toe and top of weld metal. After 60 days of incubation, the surface of the specimen was observed by SEM. Pitting corrosion was observed at the toe region. The austenite phase was preferentially attacked and δ-ferrite phase was retained like a skeleton. Bacterial adhesion correlated with the occurrence of corrosion. Therefore the weld bead shape is considered to have significant influence on bacterial adhesion and in turn, MIC occurrence in stainless steel. (250 words)
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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".