Effects of charge carrier trapping on polycrystalline PbO x-ray imaging detectors
Bibliographic record
Abstract
The effects of charge carrier trapping on the x-ray sensitivity, resolution, and detective quantum efficiency (DQE) in polycrystalline lead oxide (PbO) x-ray imaging detectors are theoretically analyzed. The theoretical models for calculating carrier trapping-limited x-ray sensitivity and modulation transfer function (MTF), and DQE incorporating polyenergetic x-ray spectrum are described. A cascaded linear system model is developed for calculating the spatial frequency f dependent DQE of PbO detectors by incorporating the effects of bulk charge carrier trapping on the MTF and the x-ray interaction depth dependent charge collection efficiency and noise. The theoretical calculations are compared with the published experimental data and show a very good agreement. From the fittings of the sensitivity and MTF curves, the electron and hole ranges in polycrystalline PbO are found to be 3.5×10−7 and ∼10−8 cm2/V, respectively. The f dependent noise power spectrum NPS(f) and DQE(f) performances are analyzed as a function of applied electric field and carrier lifetimes. The calculation shows that the carrier trapping is responsible for a 21% reduction in the MTF at the applied field of 1.0 V/μm at the Nyquist frequency (fN) and the aliased NPS at fN is reduced to 63% of that at zero spatial frequency. The sensitivity, MTF, NPS(f), and DQE(f) strongly depend on the applied electric field and carrier ranges. It is found that the DQE(f) for fluoroscopic applications can be improved dramatically by simply enhancing the carrier lifetimes through improving material properties of the PbO sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".