Ultrasensitive and stable X-ray detection using zero-dimensional lead-free perovskites
Bibliographic record
Abstract
Sensitive and reliable X-ray detectors are essential for medical radiography, industrial inspection and security screening. Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence. Three-dimensional (3D) organic–inorganic hybrid lead halide perovskites are promising for direct X-ray detection – they show improved sensitivity compared to conventional X-ray detectors. However, their high and unstable dark current, caused by ion migration and high dark carrier concentration in the 3D hybrid perovskites, limits their performance and long-term operation stability. Here we report ultrasensitive, stable X-ray detectors made using zero-dimensional (0D) methylammonium bismuth iodide perovskite (MA3Bi2I9) single crystals. The 0D crystal structure leads to a high activation energy (Ea) for ion migration (0.46 eV) and is also accompanied by a low dark carrier concentration (~ 106 cm−3). The X-ray detectors exhibit sensitivity of 10,620 µC Gyair−1 cm−2, a limit of detection (LoD) of 0.62 nGyair s−1, and stable operation even under high applied biases; no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of ~23,800 mGyair, equivalent to > 200,000 times the dose required for a single commercial X-ray chest radiograph. Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.
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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 | 0.000 |
| 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 | 0.001 | 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".