X-ray-induced recombination effects in a-Se-based x-ray photoconductors used in direct conversion x-ray sensors
Bibliographic record
Abstract
Stabilized amorphous selenium (a-Se) is currently used as an x-ray photoconductor in direct conversion flat-panel digital x-ray image detectors. Therefore, there is much interest in x-ray-induced effects in a-Se, especially changes in charge carrier lifetimes that result from x-ray exposure. We have observed that the exposure of an a-Se x-ray detector sample to x rays induces negative capture centers in the bulk and thereby reduces the hole lifetime. By using conventional and interrupted field time-of-flight (IFTOF) transient photoconductivity techniques in a TOF-IFTOF-TOF sequence, we were able to develop a technique that allows the measurement of the capture coefficient Cr between free holes and x-ray-induced negative centers, which we believe to be trapped electrons. We find that the capture process follows the Langevin recombination mechanism, the same recombination mechanism that has been observed in the case of recombination between free holes and free electrons in a-Se. We have shown that the concentration of x-ray-induced negative centers increases almost linearly with the x-ray exposure. As a corollary, in terms of fundamental physics of amorphous semiconductors, we can also conclude that the influence of potential fluctuations in the noncrystalline structure in shielding a charged center in a-Se is relatively small.
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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 | 0.001 |
| 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 | 0.001 | 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".