Mechanical Stress-Induced Defects in Thick a-PbO Layers
Bibliographic record
Abstract
Amorphous lead oxide (a-PbO) X-ray photoconductors show potential for applications in direct conversion medical imaging detectors within the diagnostic energy range. a-PbO enables large-area deposition at low temperatures and exhibits no signal lag. Low dark current can be maintained through specialized blocking layers, similar to those used in multilayer amorphous selenium (a-Se) structures in commercial detectors. However, the current state of a-PbO technology faces challenges in thick layer deposition, leading to crystalline inclusions and cracks. Our proposed stress-induced crystallization model reveals that intrinsic stress in a-PbO layers amplifies with thickness, leading to crystallographic defects. These defects, which are associated with the stable phase of β-PbO, contribute to increased dark current and initiate layer cracking. We calculate the thermal expansion coefficient of a-PbO, indicating a thermomechanical mismatch between the photoconductor and the substrate as the primary source of stress. Furthermore, we demonstrate that layer deposition parameters significantly impact heat accumulation within the growing layer, thereby facilitating temperature-induced crystallization. Our study suggests that relieving stress in grown a-PbO layers by eliminating thermal expansion coefficient mismatches between different layers in a-PbO blocking structures, coupled with optimizing deposition parameters to prevent heat accumulation during layer growth, may inhibit or even prevent stress-induced crystallization and the emergence of structural defects in thick a-PbO layers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".