Reverse Monte Carlo study of structural relaxation in vitreous selenium
Bibliographic record
Abstract
Vitreous selenium is used extensively for light detection. Its photoconducting properties are altered by structural relaxation near room temperature. X-ray diffraction, densiometry, and calorimetry are used to determine the change of the glass state during relaxation. Annealing for 24 h at 300 K increases the density by 0.14% and lowers the enthalpy by 105 J/mol. The structure factor of cast samples was measured before and after annealing, and reverse Monte Carlo models of the structure of selenium glass were generated to match the data for the quenched and annealed states. Atoms in amorphous selenium are arranged in randomly oriented chains. In the model clusters annealing effects are found by analyzing inter- and intrachain distances, bond angle distributions, and dihedral angle distributions. The average bond length remains unchanged upon annealing, while the distribution of bond lengths becomes narrower by 1.2%. Distances between atoms in adjacent chains decrease by about 0.03%, i.e., the selenium chains move closer together, and the distribution of interchain distances becomes narrower. Bond angles within the chains are affected slightly. The mean bond angle decreases by 0.016°, which indicates a greater folding of the selenium chains and represents an evolution towards the angles found in crystalline selenium phases. The bond angle distribution for the annealed state is 0.006° narrower.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".