The X-ray Sensitivity of an Amorphous Lead Oxide Photoconductor
Bibliographic record
Abstract
The photoconductor layer is an important component of direct conversion flat panel X-ray imagers (FPXI); thus, it should be carefully selected to meet the requirements for the X-ray imaging detector, and its properties should be clearly understood to develop the most optimal detector design. Currently, amorphous selenium (a-Se) is the only photoconductor utilized in commercial direct conversion FPXIs for low-energy mammographic imaging, but it is not practically feasible for higher-energy diagnostic imaging. Amorphous lead oxide (a-PbO) photoconductor is considered as a replacement to a-Se in radiography, fluoroscopy, and tomosynthesis applications. In this work, we investigated the X-ray sensitivity of a-PbO, one of the most important parameters for X-ray photoconductors, and examined the underlying mechanisms responsible for charge generation and recombination. The X-ray sensitivity in terms of electron–hole pair creation energy, W±, was measured in a range of electric fields, X-ray energies, and exposure levels. W± decreases with the electric field and X-ray energy, saturating at 18–31 eV/ehp, depending on the energy of X-rays, but increases with the exposure rate. The peculiar dependencies of W± on these parameters lead to a conclusion that, at electric fields relevant to detector operation (~10 V/μm), the columnar recombination and the bulk recombination mechanisms interplay in the a-PbO photoconductor.
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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 | 0.001 |
| 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 | 0.000 | 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".