Sensitivity of x-ray photoconductors: Charge trapping and absorption-limited universal sensitivity curves
Bibliographic record
Abstract
Direct conversion flat panel x-ray sensors that are currently under development are based on stabilized a-Se (a-Se alloyed with a small percentage of As and doped with Cl in the ppm amount). There are also other potential x-ray photoconductors such as PbO, PbI2, HgI2, CdTe, etc. Indeed, good x-ray images have been reported from PbI2, HgI2, and CdTe based x-ray sensors. The present article considers the x-ray sensitivity of photoconductors in terms of the following combined effects: (i) Absorption of x-rays, controlled by the linear attenuation coefficient α(E) and energy absorption coefficient αen(E), both x-ray photon energy E dependent. (ii) Electric field F and x-ray photon energy dependent ionization of the medium, that is, in terms of the electron and hole creation energy W±(E,F). (iii) The transport and trapping of charges across the photoconductor as they drift to the collecting electrodes. (iv) The electron and hole pairs are generated with an exponentially decaying distribution across the thickness of the photoconductor. We analytically solve the continuity equation by considering the drift of electrons and holes in the presence of deep traps. We derive an expression for the amount of collected charge per unit incident radiation, defined as the x-ray sensitivity S, in terms of W±, α, αen, and the normalized parameters: normalized attenuation depth and electron and hole schubwegs per unit thickness. We obtain two- and three-dimensional universal sensitivity curves that allow x-ray sensitivity of any potential x-ray photoconductor material to be determined from the normalized parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".