@Article{CL.2025.229.777,
AUTHOR = {X. Y. Chen, Y. H. Cai, Y. S. Chen, S. J. Huang, M. H. Li, Y. H. Li, C. H. Lin, H. Chen},
TITLE = {ZnO/ZnS sensor with broadband visible response for flexible polyethylene terephthalate substrates combined with artificial intelligence analysis},
JOURNAL = {Chalcogenide Letters},
VOLUME = {22},
YEAR = {2025},
NUMBER = {9},
PAGES = {777--785},
URL = {http://www.techscience.com/CL/v22n9/64830},
ISSN = {1584-8663},
ABSTRACT = {This study focuses on the development of zinc oxide (ZnO)/zinc sulfide (ZnS) core-shell 
structures on flexible polyethylene terephthalate (PET) substrates for enhanced light sensing. 
PET offers high elasticity, optical transparency, and chemical resistance, making it ideal for 
wearable optoelectronics. By optimizing the vulcanization process, a uniform ZnS shell is 
formed on the exposed regions of ZnO nanorods (NRs), significantly enhancing ZnO-based 
sensor’s sensitivity to visible light, especially red light (peak wavelength at 630 nm). 
Structural and spectral analyses confirm the successful formation of the ZnO/ZnS 
heterostructure, improved charge separation, and broadened light response. To improve data 
processing and classification accuracy, a one-dimensional convolutional neural network 
(1D-CNN) is applied to analyze the time-series signals from the sensor. The model achieves 
100% training accuracy and nearly perfect performance on the test set, as shown in the 
confusion matrices. This demonstrates strong generalization and stable classification across 
different light conditions. The integration of nanomaterial engineering and AI-assisted 
analysis highlights a promising strategy for future development of intelligent, flexible, and 
high-performance optical sensing systems. },
DOI = {10.15251/CL.2025.229.777}
}