Nanoscale Precursor Sites and their Importance in the Prediction of Stress Corrosion Cracking Failure
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Bibliographic record
Abstract
Stress corrosion cracking (SCC) usually initiates at locally compromised surface regions, and ultimately at nanoscale precursor sites. The ability to identify such sites would be instrumental in predicting SCC failure and developing proactive mitigation strategies. Modern microscopy capabilities allow for the requisite micro-to-atomic scale analysis to characterize SCC and identify precursor sites at various length scales. In the latter part of his career, Roger Staehle recognized and emphasized the benefit of modern capabilities in microscopy and computational science for modeling and performing physical characterization of atomic and nanoscale processes related to SCC. Consequently, he developed the quantitative micro-nano (QMN) approach with the goal of attaining a global model of SCC on an atomistic basis. This article reviews recent studies that have applied state-of-the-art microscopy techniques to characterize SCC and associated precursors in the context of the QMN approach. Initial examples used to demonstrate characterization of nanoscale precursors include SCC of Alloy 800 in Pb-containing, caustic, and acid sulfate solutions relevant to secondary side crevice environments in nuclear power plants. In line with the QMN approach, the focus is on characterizing and understanding SCC mechanisms, leading to prediction and identification of associated precursors. Precursors to secondary side SCC of Alloy 800 are shown to include monolayer-level S or Pb at oxide-metal interfaces, the onset of dealloying, or metastable pitting corrosion. Following this, intergranular oxidation embrittlement of Alloy 600 in hydrogenated water/steam environments is explored to demonstrate the benefits of a multitechnique approach to identify SCC precursors and highlight recent advancements in in situ microscopy. Although nuclear-relevant SCC systems are used as examples, the QMN approach and benefit of identifying nanoscale precursors that correlate with SCC failure are applicable to a broad spectrum of SCC systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it