Defect sensitive etching of hexagonal boron nitride single crystals
Bibliographic record
Abstract
Defect sensitive etching (DSE) was developed to estimate the density of non-basal plane dislocations in hexagonal boron nitride (hBN) single crystals. The crystals employed in this study were precipitated by slowly cooling (2–4 °C/h) a nickel-chromium flux saturated with hBN from 1500 °C under 1 bar of flowing nitrogen. On the (0001) planes, hexagonal-shaped etch pits were formed by etching the crystals in a eutectic mixture of NaOH and KOH between 450 °C and 525 °C for 1–2 min. There were three types of pits: pointed bottom, flat bottom, and mixed shape pits. Cross-sectional transmission electron microscopy revealed that the pointed bottom etch pits examined were associated with threading dislocations. All of these dislocations had an a-type burgers vector (i.e., they were edge dislocations, since the line direction is perpendicular to the [211¯0]-type direction). The pit widths were much wider than the pit depths as measured by atomic force microscopy, indicating the lateral etch rate was much faster than the vertical etch rate. From an Arrhenius plot of the log of the etch rate versus the inverse temperature, the activation energy was approximately 60 kJ/mol. This work demonstrates that DSE is an effective method for locating threading dislocations in hBN and estimating their densities.
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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 | 0.000 | 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 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".