Characterization of AgAsS and AgSbS amorphous films prepared by pulsed laser deposition
Bibliographic record
Abstract
Abstract Thin amorphous films of AgAsS and AgSbS systems have been prepared by pulsed laser deposition (PLD) at five different conditions, i.e. at different pulse energies and pulse repetition intervals of the KrF laser. The obtained films were analysed by RBS, ERDA (elastic recoil detection analysis) spectral analysis and also EDXA elemental analysis. The results of RBS and EDXA analysis were compared to the composition of the source bulk glass materials. Film compositions varied compared to the source material according to the deposition condition with film composition close to the stoichiometric one, i.e. AgAsS 2 and AgSbS 2 could be prepared by the PLD technique. RBS spectroscopy is known as an important tool for establishing depth distribution of the elements within the prepared films. The thickness of the films was chosen so that depth profiling of the entire layer is possible. ERDA allowed us to find, apart from all obvious atoms (Ag, As, Sb, S), also H atoms present in the films. The structure of the films has been studied by Raman spectroscopy as well. Vibration bands characteristic of As(Sb)S 2 AgAs(Sb)S 2 structural units present in prepared films have been observed. The films are potentially applicable for optical memories (e.g. digital video discs). Copyright © 2004 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".