Automated radiation detection using CsPbBr₃ perovskite sensors using machine learning

Document Type

Article

Publication Date

4-1-2026

Abstract

Cesium lead bromide (CsPbBr₃ or CPB) is a promising all-inorganic perovskite for radiation detection; however, CsPbBr₃ single crystals exhibit unstable electrical behavior under low-dose irradiation. To address challenges in low-does detection, this study advances CsPbBr₃-based detection by evaluating two sensing architectures integrated with machine learning (ML): a direct electrical single-crystal detector and a strain-based CPB-polymethyl methacrylate (CPB-PMMA) coated Fiber Bragg Grating (FBG) sensor. This study enables a systematic comparison of the performance of both sensing modalities in low-dose regimes, which has not been previously reported. Gamma radiation experiments using low-dose Cs-137 and higher-dose Co-57 were performed, and signals were analyzed with ensemble-based ML models. The single crystal detector achieved 85.1% accuracy for Cs-137 and 95.8% for Co-57. Cross-source transfer analysis revealed asymmetric generalization behavior, with models trained on Cs-137 generalizing strongly to Co-57 (97.2% accuracy), whereas the reverse transfer performed more modestly (82.7% accuracy). Additionally, a multi-class model distinguished background and isotope classes with 85.5% accuracy. The CPB–PMMA FBG sensor demonstrated superior low-dose sensitivity, achieving 96.60% accuracy for Cs-137. These results demonstrate the feasibility of combining CsPbBr₃-based sensing with ML for automated radiation state monitoring and photonic strain-based sensing as a promising strategy for low-dose gamma radiation detection.

Publication Source (Journal or Book title)

Materials Today Communications

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