TY - JOUR
T1 - Machine Learning-Guided Discovery of Copper(I)-Iodide Cluster Scintillators for Efficient X-ray Luminescence Imaging
AU - Wang, Yanze
AU - Zhang, Tinghao
AU - Zhao, Wenjing
AU - Xu, Weidong
AU - Wu, Zhongbin
AU - Suh, Yung Doug
AU - Zhang, Yuezhou
AU - Liu, Xiaowang
AU - Huang, Wei
N1 - Publisher Copyright:
© 2024 Wiley-VCH GmbH.
PY - 2025/1/2
Y1 - 2025/1/2
N2 - Developing efficient scintillators with environmentally friendly compositions, adaptable band gaps, and robust chemical stability is crucial for modern X-ray radiography. While copper(I)-iodide cluster crystals show promise, the vast design space of inorganic cores and organic ligands poses challenges for conventional approaches. In this study, we present machine learning-guided discovery of copper(I)-iodide cluster scintillators for efficient X-ray luminescence imaging. Our findings reveal that combining base learning models with fused features enhances model generalization, achieving an impressive determination coefficient of 0.88. By leveraging this approach, we obtain a high-performance Cu(I)-I cluster scintillator, named copper iodide-(1-Butyl-1,4-diazabicyclo[2.2.2]octan-1-ium)2, which exhibit radioluminescence 56 times stronger than that of PbWO4, and enables a detection limit for X-rays of 19.6 nGyair s−1. Furthermore, we demonstrate the versatility of these scintillators by incorporating them as microfillers in the fabrication of flexible composite scintillators for X-ray imaging, achieving a static resolution of 20 lp mm−1 and demonstrating promising performance for dynamic X-ray imaging.
AB - Developing efficient scintillators with environmentally friendly compositions, adaptable band gaps, and robust chemical stability is crucial for modern X-ray radiography. While copper(I)-iodide cluster crystals show promise, the vast design space of inorganic cores and organic ligands poses challenges for conventional approaches. In this study, we present machine learning-guided discovery of copper(I)-iodide cluster scintillators for efficient X-ray luminescence imaging. Our findings reveal that combining base learning models with fused features enhances model generalization, achieving an impressive determination coefficient of 0.88. By leveraging this approach, we obtain a high-performance Cu(I)-I cluster scintillator, named copper iodide-(1-Butyl-1,4-diazabicyclo[2.2.2]octan-1-ium)2, which exhibit radioluminescence 56 times stronger than that of PbWO4, and enables a detection limit for X-rays of 19.6 nGyair s−1. Furthermore, we demonstrate the versatility of these scintillators by incorporating them as microfillers in the fabrication of flexible composite scintillators for X-ray imaging, achieving a static resolution of 20 lp mm−1 and demonstrating promising performance for dynamic X-ray imaging.
KW - copper(I)-iodide cluster
KW - machine learning
KW - scintillators
KW - X-ray luminescence imaging
UR - http://www.scopus.com/inward/record.url?scp=85209078366&partnerID=8YFLogxK
U2 - 10.1002/anie.202413672
DO - 10.1002/anie.202413672
M3 - 文章
C2 - 39470130
AN - SCOPUS:85209078366
SN - 1433-7851
VL - 64
JO - Angewandte Chemie - International Edition
JF - Angewandte Chemie - International Edition
IS - 1
M1 - e202413672
ER -