Surface Chemistry of Solution-Grown CsPbBr<sub>3</sub> Single Crystals and Their Selective Cleaning for Linear-Responsive X-ray Detectors
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Bibliographic record
Abstract
Surface contamination challenges solution-grown perovskite crystals, especially all-inorganic CsPbBr 3 due to its complex phase diagram with two additional competing compositions. Here, we present a selective-cleaning strategy for CsPbBr 3 crystals grown in dimethyl sulfoxide (DMSO). We use a saturated solution of CsPbBr 3 in DMSO-H 2 O as a surface cleaner, combined with precleaning using DMSO-glycerol mixed solvents, to remove unreacted precursors and secondary phases without etching the CsPbBr 3 itself. X-ray detectors in Ag/CsPbBr 3 /Au architecture show exceptional linearity ( R 2 = 0.9995) to a wide X-ray dose range (0.077 to 33.6 μGyairs –1 ), achieving a sensitivity of 2,275 μCGyair –1 cm –2 under a 10 V bias and a detection limit of 11 nGyairs –1 . This strategy also increases charge carrier radiative recombination lifetime by 5× and X-ray sensitivity by 10× compared to hot DMSO cleaning. The present selective-cleaning strategy can expand to address surface contamination in a wide range of perovskites grown in solution.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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