High‐Sensitivity Perovskite γ‐Ray Detectors Enhanced by Device Engineering Toward Energy Spectroscopy Imaging

Perovskite-based detectors for γ-rays have emerged relatively recently and have attracted limited attention. Although increasing efforts are being directed towards their development, the absence of an adaptive processor, systematic theoretical framework, and various applications limit its further development. In this paper, drawing inspiration from the commercial detectors, electrode configuration is improved to reshape the bias electric field, guide the effective signal carrier, and mitigate the noise from the side section. Through theoretical analysis, simulations, and experimental comparisons, the clear improvement of energy resolution from 7% to ≈5% is demonstrated, and the highest resolution of 1.9% is observed. Furthermore, to fill up the research framework of radiation, the definition of X-ray sensitivity is extended to quantitatively describe the γ-ray response, and assess the performance of γ-ray detectors, reaching a sensitivity ≈10<sup>5</sup> µC Gy<sub>air</sub> <sup>-1</sup> cm<sup>-2</sup>. Based on these, a spectral-enhanced imaging strategy is proposed to broaden the application of γ-Ray detectors, where the spectral dimension of images is utilized to improve contrast and enhance imaging quality.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2025-07-12
DOI
https://doi.org/10.1002/advs.202503597
Primary Topic
Radiation Detection and Scintillator Technologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High‐Sensitivity Perovskite γ‐Ray Detectors Enhanced by Device Engineering Toward Energy Spectroscopy Imaging

Jiahe Zhang, He Tian, Ken Qin, Yi Yang et al.
Advanced Science
Radiation Detection and Scintillator Technologies
article

High‐Sensitivity Perovskite γ‐Ray Detectors Enhanced by Device Engineering Toward Energy Spectroscopy Imaging

Jiahe Zhang, He Tian, Ken Qin, Yi Yang, Tian‐Ling Ren, Jiali Peng, Rufan Zhou, Dan Xie, Guan‐Hua Dun, Minshu Zhang, H. Wang, Xue Sun
article en

Abstract

Perovskite-based detectors for γ-rays have emerged relatively recently and have attracted limited attention. Although increasing efforts are being directed towards their development, the absence of an adaptive processor, systematic theoretical framework, and various applications limit its further development. In this paper, drawing inspiration from the commercial detectors, electrode configuration is improved to reshape the bias electric field, guide the effective signal carrier, and mitigate the noise from the side section. Through theoretical analysis, simulations, and experimental comparisons, the clear improvement of energy resolution from 7% to ≈5% is demonstrated, and the highest resolution of 1.9% is observed. Furthermore, to fill up the research framework of radiation, the definition of X-ray sensitivity is extended to quantitatively describe the γ-ray response, and assess the performance of γ-ray detectors, reaching a sensitivity ≈10<sup>5</sup> µC Gy<sub>air</sub> <sup>-1</sup> cm<sup>-2</sup>. Based on these, a spectral-enhanced imaging strategy is proposed to broaden the application of γ-Ray detectors, where the spectral dimension of images is utilized to improve contrast and enhance imaging quality.

Advanced ScienceVol. 12(34)
Shandong University (CN), State Key Laboratory of Crystal Materials, Tsinghua University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 77%
Radiation Detection and Scintillator Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.