The Effect of Air Ionization in Testing Perovskite-Based Direct Conversion X-ray Detectors
Bibliographic record
Abstract
sı Supporting Information X -ray detectors have a wide range of applications, including medical diagnostics, industrial product inspection, and scientific investigations.Direct X-ray detectors using a photoconductive material offer highresolution imaging with high sensitivity.1 Conventional photoconductive materials suffer from limitations such as low attenuation coefficient (a-Se) which results in low sensitivities and high production costs (CdTe).In recent years, various alternative materials, including HgI 2 , 2 PbI 2 , 3 and metal halide perovskites, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] have been explored to address these challenges.Perovskites are intensively investigated due to their high attenuation coefficient, excellent charge carrier transport properties, and low production cost thanks to solution processability.[24][25][26][27][28] Going forward, the field is focusing on instability and reliability of perovskite materials and corresponding devices.[29][30][31] With new materials being developed and tested for sensitive X-ray detection, it is important to select the most promising materials with standardized and quantitative measuring techniques.For instance, when measuring the sensitivity of a device (one of the most important figures of merit of an X-ray detector), under atmospheric conditions, the current produced by the ionized air must be properly considered.Air is in fact used in X-ray detectors in ion chambers.32 We noticed that while some of the perovskite X-ray detectors 4,5 offer response currents on the order of microamperes, many others 6-22 report nanoamperes or even lower response currents, where the contribution of air ionization current may become nonnegligible (Figure 1).This raises concerns as air ionization can interfere with sensitivity estimation, necessitating the dissemination of appropriate evaluation methods for X-ray detectors.In this Viewpoint, we show examples of sensitivity overestimation by disregarding air ionization in device structures typically used in the field.In addition, we summarize instances of potential overestimation in the literature and propose possible proper evaluation methods. Evaluation of Air Ionization Current.To investigate how much current is generated from air ionization and how to eliminate it, we prepared a parallel plate device consisting of two Cu electrodes (Figure 2a) and tested the device inside a homemade vacuum chamber (Figure S1a).The electrodes were 5 mm apart and no other material was present between them except for air.Note that the irradiation field cross section
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".