Grain Boundary Segregation in NI-Base Alloys: an Integrated Approach using FIB, TEM and SIMS
Bibliographic record
Abstract
Abstract Segregation of elements to the grain boundaries in Ni-base alloys can have a large effect on the mechanical and corrosion properties of these materials. The extent of segregation, whether it is equilibrium or non-equilibrium segregation, is dependent upon the thermo-mechanical treatments applied to the alloy. in order to determine if a particular thermo-mechanical process delivers the desired microstructure, a complete microstructural analysis including an examination of grain boundary segregation must be performed. In the past, TEM has been used to identify the various phases and precipitates in these materials through the use of bright- and dark-field imaging, electron diffraction, and EDS. However, one of the elements that can play a large role in determining the properties of these alloys is boron. in this group of alloys, boron is generally present in bulk analyses in only a few tens of ppm, and as a result it is difficult to detect using the aforementioned techniques. More commonly, boron segregation to the grain boundaries is usually studied by means of Auger analysis, whereby the sample is charged with hydrogen in order to promote brittle intergranular fracture in-situ in the Auger microprobe. Boron can then be detected at the surface of the clean grain boundary fracture surfaces.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".