Techniques for In Situ Corrosion Studies of 316L Stainless Steel in Sulfuric Acid Solutions
Bibliographic record
Abstract
An in situ optical microscopy and simultaneous electrochemical analysis method is presented for studying 316L stainless steel in sulfuric acid based solutions. The electrochemical methods involved potentiostatic and potentiodynamic analysis of 316L stainless steel surface in aerated and deaerated 1M-3.39M H2SO4 while varying Ni+ and Cl-. This technique is reviewed along with several other in situ surface analytical probes and patents such as scanning tunneling microscopy (STM), scanning electrochemical microscopy (SECM) and variations on SECM (near fieldalternating current) and the results are discussed in conjunction with various theories and applications. The results when compared to SECM data indicate that the SECM resolution, control and performance are improved. The results also illustrate the wide variety of corrosion behaviors possible for 316L stainless steel under potentiostatic and potentiodynamic test conditions. Analysis of these samples provides both a detailed visual account of the corrosion process in addition to standard electrochemical analysis regarding pitting potentials, corrosion potential and corrosion rate. Polarization data and analysis regarding the corrosion patterns observed is presented including in situ images of etching, surface layer changes and pitting. Images and analysis of chromium carbide and sulfide inclusion behaviors in sulfuric acid were performed showing the tendency of inclusions to dissolve and act as nucleation sites for pits. Keywords: Aerated, dearated, etching, grain boundaries, inclusions, in situ, optical, pitting, potentiostatic, potentiodynamic, polarization, SEM-EDS, 316L, 1M-3.39M H2SO4
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".