Radiation Tolerance of Lead Halide Perovskite Films: An <i>in Situ</i> X-ray Scattering Study
Bibliographic record
Abstract
Lead halide perovskites, known for their excellent optoelectronic properties, have found application in solar cells, LEDs, and photodetectors. One particularly promising application of these materials is in X-ray detectors. This requires a high degree of tolerance to the effects of ionizing radiation, and one of the main drawbacks of lead halide perovskites is their instability, with respect to a wide variety of environmental stimuli. Although we know a lot about how moisture, oxygen, and visible light affect and degrade the perovskite lattice, relatively little is known about the radiation tolerance of these materials. In this work, we discuss the effect of high-energy (15.12 keV) X-ray radiation on perovskites of various compositions. We use in situ GIWAXS (grazing incidence wide-angle X-ray scattering) to follow the structural changes that occur as the result of X-ray exposure in atmospheres of varying relative humidity. Perhaps surprisingly, we find that there is a low compositional and humidity dependence on the rate of radiation damage in perovskite films. An accumulating radiation dose is associated with increasing structural disorder including the formation of ion vacancies and Schottky defects. These results are an important first step in the design of robust, radiation-tolerant, and perovskite-based 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".