Halide perovskite single crystals (SCs) have attracted much attention for their application in high-performance x-ray detectors owing to their desirable properties, including low defect density, high mobility–lifetime product (μτ), and long carrier diffusion length. However, suppressing the inherent defects in perovskites and overcoming the ion migration primarily caused by these defects remains a challenge. This study proposes a facile process for dipping Cs0.05FA0.9MA0.05PbI3 SCs synthesized by a solution-based inverse temperature crystallization method into a 2-phenylethylammonium iodide (PEAI) solution to reduce the number of defects, inhibit ion migration, and increase x-ray sensitivity. Compared to conventional spin coating, this simple dipping process forms a two-dimensional PEA2PbI4 layer on all SC surfaces without further treatment, effectively passivating all surfaces of the inherently defective SCs and minimizing ion migration. As a result, the PEAI-treated perovskite SC-based x-ray detector achieves a record x-ray sensitivity of 1.3 × 105 μC Gyair−1 cm−2 with a bias voltage of 30 V at realistic clinical dose rates of 1–5 mGy s−1 (peak potential of 110 kVp), which is 6 times more sensitive than an untreated SC-based detector and 3 orders of magnitude more sensitive than a commercial α-Se-based detector. Furthermore, the PEAI-treated-perovskite SC-based x-ray detector exhibits a low detection limit (73 nGy s−1), improved x-ray response, and clear x-ray images by a scanning method, highlighting the effectiveness of the PEAI dipping approach for fabricating next-generation x-ray detectors.
We use an automated research platform combined with machine learning to assess and understand the resilience against air and light during production of organic photovoltaic (OPV) devices from over 40 donor and acceptor combinations. The standardized protocol and high reproducibility of the platform results in a dataset of high variety and veracity to deploy machine learning models to encounter links between stability and chemical, energetic, and morphological structure. We find that the strongest predictor for air/light resilience during production is the effective gap Eg,eff which points to singlet oxygen rather than the superoxide anion being the dominant agent in degradation under processing conditions. A similarly good prediction of air/light resilience can also be achieved by considering only features from chemical structure, that is, information which is available prior to any experimentation.
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