Crystallite size and microstrain: XRD line broadening analysis of AgSiN thin films
Bibliographic record
Abstract
Purpose This paper aims to determine the crystallite size and microstrain values of AgSiN thin films using potential approach called approximation method. This method can be used as a replacement for other determination methods such as Williamson-Hall (W-H) plot and Warren-Averbach analysis. Design/methodology/approach The monolayer AgSiN thin films on Ti6Al4V alloy were fabricated using magnetron sputtering technique. To evaluate the crystallite size and microstrain values, the thin films were deposited under different bias voltage (−75, −150 and −200 V). X-ray diffraction (XRD) broadening profile along with approximation method were used to determine the crystallite size and microstrain values. The reliability of the method was proved by comparing it with scanning electron microscopy graph and W-H plot method. The second parameters’ microstrain obtained was used to project the residual stress present in the thin films. Further discussion on the thin films was done by relating the residual stress with the adhesion strength and the thickness of the films. Findings XRD-approximation method results revealed that the crystallite size values obtained from the method were in a good agreement when it is compared with Scherer formula and W-H method. Meanwhile, the calculations for thin films corresponding residual stresses were correlated well with scratch adhesion critical loads with the lowest residual stress was noted for sample with lowest microstrain and has thickest thickness among the three samples. Practical implications The fabricated thin films were intended to be used in antibacterial applications. Originality/value Up to the knowledge from literature review, there are no reports on depositing AgSiN on Ti6Al4V alloy via magnetron sputtering to elucidate the crystallite size and microstrain properties using the approximation method.
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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 | 0.001 | 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) | 0.002 | 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".