Atomic-level Mapping of Cd0.9Zn0.1Te Crystals
Bibliographic record
Abstract
The presence of precipitates in the Cd\textsubscript{0.9}Zn\textsubscript{0.1}Te (CZT) semiconductors can play a key role in shaping the performance of CZT-based detectors. These precipitates range from nanometers to tens of nanometers, as it has been extensively reported in literature using transmission electron microscopy. Herein, we introduce laser-assisted atom probe tomography (APT) to map in atom-by-atom basis the three-dimensional of CZT crystals. The investigated crystals were grown by Travelling Heater Method. APT is an ideal method to investigate the distribution and local composition of individual precipitates. Our investigations focused on both as-grown CZT crystals and post-growth annealed crystals. APT specimens were prepared using a Focus Ion Beam (FIB) with Gallium ions. The preservation of the crystal structure was verified after FIB preparation by electron backscatter diffraction (EBSD). First APT results show no precipitation of Tellurium or Cadmium at the nanoscale. The structural integrity of the investigated CZT crystals has therefore been confirmed showing that homogeneous CZT structure down to the sub-nanometerscale on the scale of the micrometer-sized volumes APT explores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".