Transmission Kikuchi Diffraction Mapping Induces Structural Damage in Atom Probe Specimens
Bibliographic record
Abstract
Measuring local chemistry of specific crystallographic features by atom probe tomography (APT) is facilitated by using transmission Kikuchi diffraction (TKD) to help position them sufficiently close to the apex of the needle-shaped specimen. However, possible structural damage associated to the energetic electrons used to perform TKD is rarely considered and is hence not well-understood. Here, in two case studies, we evidence damage in APT specimens from TKD mapping. First, we analyze a solid solution, metastable β-Ti-12Mo alloy, in which the Mo is expected to be homogenously distributed. Following TKD, APT reveals a planar segregation of Mo among other elements. Second, specimens were prepared near Σ3 twin boundaries in a high manganese twinning-induced plasticity steel, and subsequently charged with deuterium gas. Beyond a similar planar segregation, voids containing a high concentration of deuterium, i.e., bubbles, are detected in the specimen on which TKD was performed. Both examples showcase damage from TKD mapping leading to artefacts in the distribution of solutes. We propose that the structural damage is created by surface species, including H and C, subjected to recoil from incoming energetic electrons during mapping, thereby getting implanted and causing cascades of structural damage in the sample.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".