(Invited) Characterizing Crevice Corrosion Processes Electrochemically, Microscopically, and Spectroscopically
Bibliographic record
Abstract
Due to the occluded nature of the process, crevice corrosion is difficult to investigate electrochemically, microscopically and spectroscopically. The electrochemistry/chemistry occurring within propagating crevices can be highly localized and difficult to characterize. With an emphasis on Ti and Ni alloys, we have developed a range of methodologies to determine the electrochemical/chemical details of crevice corrosion processes and how they are influenced by the compositional and microstructural properties of the materials. On both alloy categories crevice propagation can be supported by both external and internal cathodic processes. The kinetics of the external process (O2 reduction) can be followed electrochemically using a galvanic coupling technique, while the internal process (H+ reduction), although detectable by its influence on the O2 reduction kinetics, cannot be directly followed in real time. Which of these two reactions is dominant is very dependent on alloy composition and microstructure. For Ti alloys, the microstructure is very dependent on the added alloying elements (e.g., Ni, Pd, Ru) and the content of unavoidable impurities such as Fe, and perhaps also Mo and Cr. Their distribution in the uncorroded alloy can be determined using atomic probe tomography and their redistribution, as a consequence of a period of crevice propagation, by dynamic secondary ion mass spectrometry. By comparison to Ti alloys, Ni-based alloys (in particular Ni-Cr-Mo alloys) are free of segregated secondary phases, and possess high quality, low energy grain boundaries. These two qualities can be demonstrated using electron backscatter diffraction, transmission electron microscopy and electron energy loss spectroscopy. However, while the quality of the grain boundaries is a key feature in controlling the initiation of crevice corrosion, it is the alloy composition which exerts the dominant influence on the accumulation and distribution of corrosion damage by depositing Mo oxides at propagating sites, a process that can be demonstrated by correlating the damage distribution, determined by confocal laser scanning microscopy, with the accumulation of molybdates, confirmed by microRaman spectroscopy. Using this combination of electrochemical, microscopic and spectroscopic techniques we are presently determining the influence of alloy composition on the relative importance of internally and externally supported crevice propagation. Also, we are developing the technique of X-ray photoelectron spectroscopic imaging to demonstrate the self-healing capability conferred on these alloys by the inclusion of Mo.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".