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<p>Radiation dosimetry urges an exploration of novel materials for large-scale production of low-cost, compact, and highly-efficient dosimeters, because of their rising need. Interestingly, materials with perovskite structures entice much research interest, mainly in the field of photovoltaic cells and photodetection. Perovskites are attractive because their properties can be easily tuned by changing their constituent elements. The hybrid organic–inorganic perovskites (HOIPs) such as <i>MA</i>PbI<sub>3</sub> and <i>FA</i>PbI<sub>3</sub> explored over the recent years show high band gap, high mobility-lifetime product and stable nature. These desirable properties introduced hybrid perovskites into the field of radiation dosimetry. This review attempts to provide a detailed study on the perovskites explored in dosimetry and high-energy radiation detection. We also discuss the state-of-the-art machine learning (ML)-based studies on HOIPs for novel material discovery and property predictions.</p>

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Novel perovskite semiconductors for radiation detection and dosimetry applications

  • 期刊论文

  • 发表日期：

    2026年2月13日

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  • A. Alloffy Mary,
  • R. Vidya

摘要

Radiation dosimetry urges an exploration of novel materials for large-scale production of low-cost, compact, and highly-efficient dosimeters, because of their rising need. Interestingly, materials with perovskite structures entice much research interest, mainly in the field of photovoltaic cells and photodetection. Perovskites are attractive because their properties can be easily tuned by changing their constituent elements. The hybrid organic–inorganic perovskites (HOIPs) such as MAPbI3 and FAPbI3 explored over the recent years show high band gap, high mobility-lifetime product and stable nature. These desirable properties introduced hybrid perovskites into the field of radiation dosimetry. This review attempts to provide a detailed study on the perovskites explored in dosimetry and high-energy radiation detection. We also discuss the state-of-the-art machine learning (ML)-based studies on HOIPs for novel material discovery and property predictions.