Studies of silver photodiffusion dynamics in Ag/Ge<sub><i>x</i></sub>S<sub>1−<i>x</i></sub> (<i>x</i> = 0.2 and 0.4) films using neutron reflectometry
Bibliographic record
Abstract
To better understand the dynamics of silver photodiffusion into amorphous chalcogenide (Ch) films, it is informative to probe the time-dependent distribution of silver in the films while they are simultaneously exposed to visible light. Time-resolved neutron reflectometry is particularly well-suited to this purpose because it can follow time-dependent changes in the multilayer structure (Ag/Ag–Ch/Ch) while excluding the possibility of probe beam induced changes. This paper reports the results of time-resolved neutron reflectivity measurements of two Ag/GexS1−x (x = 0.2 and 0.4) films as they are exposed to a visible light source. Analysis showed that silver diffusion occurs via two distinct processes: a fast diffusion that takes place during the first 2 and 10 min of sample illumination for the x = 0.2 and 0.4 films, respectively; and a subsequent slower change that is observed over the next 18 min (x = 0.2 film) and 107 min (x = 0.4 film). These results suggest the formation of a relatively stable Ag-rich phase in the reaction layer followed by slower diffusion at the interface between Ag-rich and Ag-poor layers. Fourier transform analysis shows that the position of the interface is essentially fixed — a conclusion that contradicts the “diffusion front” model that has been previously postulated.
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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".