Three dimensional reciprocal space measurement by x-ray diffraction using linear and area detectors: Applications to texture and defects determination in oriented thin films and nanoprecipitates
Bibliographic record
Abstract
The authors present a method for the fast and efficient measurement of volumes of reciprocal space by x-ray diffraction using linear and area detectors. The goal of this technique is to obtain a complete overview of the reciprocal space to detect and characterize the nature and orientation of all the phases present. They first explain the detailed procedures and scan strategies required for transforming raw scattering data into three-dimensional maps of reciprocal space and present a complete open-source software package for advanced data processing, analysis, and visualization. Several case studies, chosen to highlight the overall capabilities of the technique, are then introduced. First, thermal diffuse scattering from a monocrystalline Si substrate is characterized by the presence of lines linking diffraction peaks in reciprocal space. Second, a detailed investigation of texture in multiphase thin layers permits us to reveal the unambiguous presence of fiber, axiotaxial, and epitaxial components in oriented films. The visualization of a significant fraction of reciprocal space has allowed us to identify an unexpected metastable phase, which could not be deduced from measurements carried out in the Bragg–Brentano geometry. The technique is then used to study planar defects in nickel silicides formed by solid-state reactions and micro twins in a GaP matrix containing coherent MnP precipitates. Overall, the authors show that the systematic acquisition of significant volumes of reciprocal space permits us to observe behaviors that might otherwise remain undetected when analyses are restricted to typical measurement scans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".