Amorphous selenium and its alloys from early xeroradiography to high resolution X‐ray image detectors and ultrasensitive imaging tubes
Bibliographic record
Abstract
Abstract We describe the progress in the science and technology of stabilized a‐Se from its early use in xerography and xeroradiography to its present use in commercial modern flat panel X‐ray imagers and ultrasensitive video tubes which utilize impact ionization of drifting holes. Both electrons and holes can drift in stabilized a‐Se, which is a distinct advantage since X‐ray photogeneration of charge carriers occurs throughout the bulk of the photoconductive layer. An a‐Se photoconductor has to be operated at high fields to ensure that the photogeneration efficiency is sufficiently large to provide reasonable X‐ray sensitivity. However, at high fields, the dark current is unacceptably large in simple metal/a‐Se/metal devices, and special multilayer device structures need to be designed. The dark current decays with time and increases with the nominal applied field. The reduction of the dark current to a tolerable level was one of the key factors that lead to the commercialization of a‐Se X‐ray detectors. We discuss the origin of the dark current, and highlight some of the current challenges in the design of next generation detectors. We also discuss the origin of impact ionization in a‐Se, and its fruitful utilization in ultrasensitive imaging devices, including the Harpicon, which are likely to lead to new high detective quantum efficiency detectors.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".